Customization of Avatars in a HPV Digital Gaming Intervention for College-Age Males: An Experimental Study

Abstract

Background. Digital games are increasingly popular among college age men and present themselves as an ideal platform to deliver HPV interventions. Customizing avatars in role playing games encourage intrinsic motivation in the learning process because of self-representation. Proteus Effect research suggests that the representations people make are an adaption of their actual/ ideal self and dictate how one conforms to the expectations and identity cues of their avatar. Objective/Purpose: This study evaluated the effectiveness of a digital gaming intervention aimed at increasing HPV risk perceptions, self- efficacy and behavioral intention to receive the HPV vaccine among college age men (18 - 26).

Methods. This randomized control trial employed a 2 X 2 fully-crossed between subjects and tested the effects of avatar characters (assigned/customized) and perception of self (ideal/actual) on HPV risk perception, HPV vaccine self-efficacy and behavioral intent to receive the HPV vaccine. Participants were randomly assigned to one of four conditions.

Results. A Factorial ANOVA with repeated measures and a between subjects’ factor (split plot) was used to test for the differences between the means of the outcome variables. Despite having no main or interaction effect our analysis did report a significant main effect of using a pre-post design with the experiment as a stimulus.

Discussion/Conclusion. For experimental data, customizing an avatar to look like one’s actual self increases risk perception for the HPV virus and self – efficacy for the HPV vaccine whereas customizing an avatar to look like one’s ideal self increases one’s intent to receive the HPV vaccine.

Keywords

digital game-based learning public health role-playing simulation/gaming technology

Introduction

Human Papillomavirus (HPV) is a group of 150 related viruses that can result in genital warts as well as cancers of the mouth/throat, cervix, vagina, vulvar, anus and penis ((Centers for Disease Control and Prevention, 2016)). While the body is able to fight most HPV infections resulting in no health problems or consequences, there are 9 that cause the majority of HPV related cancers and diseases. These include types 6, 11,6,18, 31, 33, 45, 52 and 58 (Merck, 2017). But despite public health efforts and the availability of a vaccine, HPV prevalence and incidence rates remain relatively high. HPV is the most common sexually transmitted disease (STD) in the United States (U.S.) with the highest infection rates observed among sexually active adults under the age of 25 with highest risk in adolescents (aged 15 – 19) and young adults (aged 20 to 24) (Gerend & Magliore, 2008; Patel et al., 2012). With over 20 million people currently infected with HPV, and 6.2 million new cases being diagnosed each year in the U.S., HPV- related cancers are now becoming a greater threat to younger generations of Americans (Katz, Krieger, & Roberto, 2011).

HPV-associated diseases in men are often asymptomatic (Gargano, Meites, Watson, Unger, & Markowitz, 2017). The lack of symptoms in men often causes them to seek care at a later stage of the disease process, thus increasing their risk for oropharyngeal, anal and penile cancers (Fontenot, Collins Fantasia, Charyk, & Sutherland, 2014). Despite this, HPV vaccine uptake is low. Men have less basic knowledge about health, are less likely to seek out health information, and are less likely to engage in preventative health-promoting behaviors compared to females (Davies et al., 2000). Low vaccine uptake among men has been related to low perceived threat due to the absence of HPV vaccination information from healthcare providers, parents, peers, public health officials, prevention campaigns and the media (Daley et al., 2011; Ratanasiripong, 2015). This is of great concern, since males are the highest carriers and transmitters of HPV virus yet are so instrumental to herd immunity (Hosseini, Munoz, Lakon, & Brown, 2014). Herd immunity occurs when a critical part of the community is immunized from a contagious disease because of their increased risk, allowing for most members of the community to be protected against that disease (National Institute of Allergy and Infectious Diseases, 2010). Because of the herd immunity effect, there are compelling arguments to make in favor of male vaccination against HPV, such as taking a gender neutral approach to vaccination which has not been traditionally adopted since recommendations were released by the Centers for Disease Control and Prevention (Zimet & Rosenthal, 2010).

Despite the obvious health benefits of male vaccination against HPV, there are no screening procedures nor vaccine prevention campaigns specifically for males (Crosignani et al., 2013). With several countries including the US exclusively focusing their efforts towards only educating the female population, HPV risk perception among males is low (No et al., 2011). According to National Health and Nutrition Examination Survey (NHANES) data, HPV infection prevalence was almost 50% for men 18 – 59 years old, equating to nearly 34.8 million men (Han, Beltran, Song, Klaric, & Choi, 2017). College males have a higher risk of contracting HPV, due to their riskier sexual behaviors such as having multiple sexual partners and having sex while intoxicated (Hittner, Owens, & Swickert, 2016). Furthermore, college males are reluctant to seek care from a healthcare provider due to social expectations to avoid extreme pain and pressures to conceal any vulnerabilities (Davies et al., 2000). Although the HPV vaccine is expected to reduce healthcare costs and decrease the fear associated with treatment of genital warts, there are key barriers that have affected vaccine uptake (M. Jones & Cook, 2008). These barriers include the stigmatizing nature of HPV, physical discomfort associated with vaccine uptake and the fear of vaccine side effects (Anic & Giuliano, 2011; Fontenot et al., 2014; G. Jones, Perez, Huta, Rosberger, & Lebel, 2016).

Serious Digital Games and Character Customization

Digital games may be an ideal platform to deliver HPV interventions to college males. About seven in ten men younger than 30 (72%) say they play video games often or sometimes, compared with 49% of women in the same age group (Pew Research Center, 2016). Games for health have emerged as a personalized, entertaining and informative means to health promotion. These games not only impact attitudes and beliefs, but have been shown to increase knowledge, skills and changes in behavior (Thompson, 2012). Therefore, video games can be marketable to a vast range of population subgroups including college students as they are a powerful channel to deliver health messages.

Customizing digital games provides increased opportunities for interventions to focus on the individual and ensures that messages (a combination of content, source, graphics, channel, etc.) the audience receives reflect relevant individual characteristics that matches the self (Kalyanaraman & Sundar, 2006). Based on self-concept theory “the self”” has been operationally defined as “a person’s representation or description of his or her personal and social characteristics” (Leary, Estrada, & Allen, 2009, p. 148). These characteristics can reflect people’s ideal, actual and feared selves and can even extend into the ecological, interpersonal, extended, private and conceptual selves (Benson, 2002; Leary et al., 2009). As the digital landscape evolves however, the idea of self has been extended to online communication technologies such as the internet, smartphones, virtual worlds, and video/ digital games (Belk, 2014). The “extended self” occurs when external objects, possessions and identities become viewed as part of the “self” because of the power or control that can be forced upon them (Belk, 1988). Role playing games are popular because they allow an individual immerse into the game narrative through personalizing and controlling the selected avatar character (Turkay & Kinzer, 2014). Through this customization, the player becomes more immersed in the game, which increases the saliency of messages due to its personal relevance (Vanhatupa, 2011). Using the avatar customization to attract a player’s attention, role playing games initiate information processing which is pivotal to facilitating sustained behavior change (Thompson, 2012). Because research has pointed to differences in preferences among gamers, customization may enhance the effectiveness of digital games developed for sexual health (DeSmet, Shegog, Van Ryckeghem, Crombez, & De Bourdeaudhuij, 2015).

Simulations and Role Playing

Simulation games could be of great benefit to sexual health education because real-world activities are mimicked and practiced in a safe gaming environment (DeSmet et al., 2015). Simulation and role playing games allow players to participate in a virtual representation of a real world situation and maneuver through game activities without any real-world consequences (Thompson, 2012). Through using the combination of emotional storytelling and character immersion, role playing increases a player’s personal stake in a video game’s outcome. Although some believe that using simulations and role playing can diminish the perceived seriousness and impact of the activity on health, others suggest that gaming strategies increase intrinsic motivation, behavioral self-efficacy and adoption rates (McCallum, 2012).

Avatar Self-Representation, Projection & Relatedness

Avatars are becoming a constant presence within digital and virtual world realities because they are malleable objects that allow users to customize self-presentation in diverse ways, while maintaining the user’s privacy (Yee & Bailenson, 2007). Avatars allow for expressive freedom over an otherwise static online identity and are especially important for gamers who are still forming their own in person and online personal identities (Vasalou, Joinson, Banziger, Goldie, & Pitt, 2008). Because young people are in these early stages of identity formation, they engage in wishful identification and may choose avatars with characteristics they would like to possess themselves (Konijn & Bijvank, 2009). The player behind the avatar regularly experiences the emotions attached to achievement or failure in the gaming situation, which often leads to continued adoption of the avatar’s identity (Li, Liau, & Khoo, 2013).

Self- representation is defined as an individual’s ability to express their own self-image (Park & Chung, 2011). In digital gaming environments, self-representation is more flexible and easy to transform as users can customize their own avatar to form a digital and animated representation of themselves (Yee & Bailenson, 2007). Gamers benefit from the opportunity to select a ready-made character with physical attributes to express individual identity (Jin, 2009; Kim & Sundar, 2012). Research suggests that the representations that people make of themselves online are an amalgamation of their actual and ideal selves (Bessière, Seay, & Kiesler, 2007). Actual self is the way in which a person sees himself or herself now, whereas ideal self is the way in which a person would like to see himself or herself (Abel, Buff, & O’Neill, 2013).

Proteus Effect suggests that users in online environments conform to the expectations and stereotypes related to the individual identity cues of their avatars (Yee & Bailenson, 2007). Because digital games simulate real-world environments how a gamer identifies himself/herself may influence the actions they choose to participate in and outcomes they achieve during the digital game. Additionally, because avatar users and gamers often feel connected to their self-representations (ideal vs. actual), real world behaviors can also be impacted (Kim & Sundar, 2012). The repetitive internalization of the identity of the avatar is a powerful and seductive strategy of identification that influences the player to treat the avatar as if it was himself/herself (Cohen, 2001; Li et al., 2013).

Consistent with Proteus Effect, it is hypothesized that college-aged men playing as their ideal self will feel more self-efficacious towards the targeted behavior and therefore have a greater intent to get the HPV vaccine due to their increased confidence playing the game as who they would want to be. Alternatively, those playing the game with an avatar representing their “actual self” will have increased experiential learning and engagement and thus an increased risk perception of the HPV virus and greater intent to get the HPV vaccine. This is because they are visualizing their character as they really are in real life. This mergence or alignment of their self-image will make the game experiences more relatable and therefore the in game consequences more salient and applicable to their real life. Therefore, our research hypotheses for this study are:

Customizing an avatar character based on an individual’s perception of their actual self will be associated with an increase in one’s perceived risk of the HPV virus.

Customizing an avatar character based on an individual’s perception of their ideal self will be associated with an increase in one’s self–efficacy to receive the HPV vaccine.

Customizing an avatar character based on an individual’s perception of their actual and ideal selves will be associated with an increase in behavioral intention to receive the HPV vaccine.

Theoretical Framework

The Health Belief Model (HBM) has been used extensively to study vaccination beliefs and behaviors (Glanz, Rimer, & Viswanath, 2008). According to Krieger and Sarge (2013), health messages are only salient when perception of threat (risk) is aroused in relation to perceived severity and perceived susceptibility. Research has also indicated that perceived risk/threat and self-efficacy has been identified as theoretical constructs that can impact HPV vaccination intention uptake in males (Fontenont et al., 2014). According to Grace-Leitch and Shneyderman (2016), vaccine acceptability among men is high when perceived susceptibility to HPV and self – efficacy is high. This mirrored a similar study that concluded that HPV vaccine acceptability being associated with perceived severity, barriers and HPV vaccine self-efficacy (Mehta, Sharma, & Lee, 2014).

Although behavior change is often measured in research studies and is included in the Health Belief Model as an outcome, behavioral intention was used as a proxy for this study as it is the most important antecedent of health behavior (Fogel & Ebadi, 2011). Behavioral intention is defined as a person’s the subjective probability that one will choose to engage in a given behavior. Behavioral intention is the hallmark construct of the Theory of Reasoned Action and Planned Behavior (Ajzen, 1991; Gutierrez et al., 2013). In health communication research and practice, behavioral intention is one variable that is often targeted due to its high predictive validity in relation to willingness and motivation to adopt a specific behavior (National Library of Medicine, n.d.).

Methodology

Participants

This study was conducted at a large research-intensive university in the southeastern United States. To be included in the study, males had to be enrolled as full-time students, between the ages of 18-26 and non-recipients of the HPV vaccine. After IRB approval was received for the study, the University’s registrar office provided a database of male undergraduate and graduate college students meeting the study’s age and enrollment requirements. Of the 16,365 males enrolled at the University, a simple random sample of 6,000 men was generated using computer software. This sample represented 36% of the male population at the University. Researchers intentionally oversampled men to account for the trend of low participation rates of college age men in research studies related to HPV (Priest & Knowlden, 2015).

Recruitment emails were sent out to the sample of 6,000 men. SuperSaaS (2018) scheduling software was used to facilitate participant enrollment, and participants were asked to sign up for a 1-hour (60 minute) appointment to participate in the study. Although the software enabled automatic generic reminders to confirm session times for participants, researchers also emailed all enrolled study participants an appointment confirmation, with their Participant ID number and a link to an electronic informed consent and survey.

Procedure

Once participants completed the electronic informed consent and pre-assessment survey administered using Qualtrics, they were randomly assigned into one of the four condition groups (see Figure 1). During the experimental study, two gaming stations were used. Each station was equipped with a Lenovo laptop, gaming mouse, thought listing exercise, and pen.

Figure 1.

Experimental design conditions.

Participants were randomly assigned into 4 experimental conditions (see Table 2). Each condition group played the game as a different avatar character. The lead researcher provided participants with specific instructions on how to customize or select from a group of pre-set avatar profiles in Sitepal to use during the gaming experiment (A_HPV1 to A_HPV6) (see Figure 2). Condition 1 participants (n=27) were asked to customize their avatar character to represent how they would ideally like to look. Condition 2 participants (n=24) was asked to pick one of the existing avatar characters that best represented how they ideally would like to look. Condition 3 participants (n=29) was asked to customize their avatar character to represent how they actually looked. Condition 4 (n=28) was asked to pick one of the existing avatar characters that best represented how they actually looked (Oddcast Company, 2018).

Figure 2.

SitePal assigned avatar profiles.

Following avatar selection, participants were behaviorally primed using the thought listing method often used in persuasion studies to measure elaboration (Cacioppo, Von Hippel, & Ernst, 1997). Using this method, students were given a paper form that asked them to “List 3 reasons why male college students should take precautions to prevent HPV” and “List 3 ways that male college students can prevent the spread of the HPV virus.” Participants were behaviorally primed to think about HPV prevention, because the VAX game used as the stimulus for this study did not include HPV-specific messages during the actual gaming experience. Rather, the thought listing exercise was conducted to encourage users to associate the spread of the infectious disease during the game as the spread of the HPV virus If the VAX-HPV association was not organically made during this thought listing activity, instructions were also included after the exercise to inform participants that they would be playing a HPV video game as their designated avatar character.

While the participant was completing the thought listing exercise, the research staff embedded the code of the participant’s avatar into the game so that they could see the avatar and assume the role of the avatar while playing the game. An existing digital game called Vax! (http://vax.herokuapp.com/) was used in this study. Developed in 2014 by Ellsworth Campbell, a doctoral student affiliated with Penn State University’s Salathe Group, the Vax! game presents an interactive learning environment by which users are immersed into contagion scenario within which a disease is spread across a network of individuals. According to the instructions, “players are tasked to prepare for an outbreak by vaccinating a network that resembles human social networks. After distributing vaccines, an infectious outbreak begins to spread, and the player is tasked to quell the epidemic by quarantining individuals at risk of becoming infected. Vax! is a puzzle-game infused with chance that was inspired by real-world challenges, infectious disease modeling, and addictively rewarding gameplay” (Salathe Group, 2014, p. 1). Before playing the Vax! Game, participants were instructed to complete a “tour” activating the avatar to appear on the “gaming option” screen (see Figure 3). The avatar remained present during the game (Figure 4) and only disappeared once results were presented to gamers at the conclusion of the Vax! gaming sessions. Post-assessment surveys were administered to participants immediately following gameplay using Qualtrics online survey software. Participants were compensated with a $10 Visa debit card for their participation in the study.

Figure 3.

Game play screen.

Figure 4.

Gaming option screen.

Measures

The independent variables (IVs) in this study were categorical: (a) avatar character (assigned/customized) and (b) perception of self (ideal/actual (Abel et al., 2013; Bailey, Wise, & Bolls, 2009; Sah, Ratan, Tsai, Peng, & Sarinopoulos, 2017). The dependent variables (DVs) included the following continuous variables: (a) risk perception (b) self-efficacy and (c) HPV vaccine intention. Behavioral intention was used as a proxy to behavior change. Scale items included on the survey were adapted from a scale used in previous HPV studies (Katz, Kam, Krieger, & Roberto, 2012). Three items measured risk perception, three items measured self-efficacy, and four items measured behavioral intention. The questions in the survey measured dependent variables using a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree). The total score for each of the constructs of interest (risk perception, self-efficacy and behavioral intention) was computed by summing up the scores of each statement used to measure that construct. This was done at both pre- and post-assessment. There were three statements used to measure risk perception, thus a minimum score possible was 3 and maximum score possible was 21. There were three statements used to measure self-efficacy, thus a minimum score possible was 3 and maximum score possible was 21. There were four statements used to measure behavioral intention, thus a minimum score possible was 4 and maximum score possible was 28. The scales were within the acceptable range with respect to internal consistency for self-efficacy (α = 0.812) and intention (α= 0.836). Although risk perception was a variable of interest for the previous study the scale was used in, Cronbach’s alpha was not reported. In our study however Cronbach’s alpha of 0.910 was reported for risk perception which indicated an excellent level of internal consistency. Other questions collected data on age, race, ethnicity, student status/classification, health insurance coverage, religious/spiritual affiliation, and current sexual health practices and beliefs (Division of STD Prevention, 2015; Ward, Dahlhamer, Galinsky, & Joestl, 2014).

Data Analysis

All statistical analyses were conducted using SPSS v24.0 software. Pearson correlation coefficients were calculated to examine the relationships between the pre and post-test score for risk perception, self-efficacy and behavioral intention. A descriptive analysis was conducted to collect measures of central tendency and variation for each variable. Pearson’s r correlation coefficients were calculated to determine the relationship between pre- and post-test scores for each dependent variable (risk perception, self-efficacy and behavioral intent) (see Table 3). A Between-Subjects (split plot) Factorial Analysis of Variance (ANOVA) with repeated measures was used to test differences between the means on the outcome variables across our experimental conditions. Condition group was treated as a between-subject factor with pre- and post-test scores treated as two levels of the within-subject variable. Main and interaction (type of avatar X perception of self) effects were computed.

Results

Demographics

A total of 168 participants initially enrolled in the experiment, with 108 (64.29%) completing the protocol. Study participants were about evenly distributed with respect to age: 18-20 years old (n=39; 36.1%), 21-23 years old (n=45; 41.7%) and 24-26 years old (n = 24; 22.2%). Participants self-identified as being White or Caucasian (n=46; 42.6%), Asian (n=35; 32.4%), Native Hawaiian or Pacific Islander (n=16; 14.8%), Hispanic (n=6; 5.6%) and Bi-racial or Multi-racial (n=5; 4.6%). None of the respondents of this study identified as Black/African American or American Indian/Alaskan Native. Our sample group did not match the demographics of the institution, with the exception to the higher level of involvement of Caucasian males in the study. Of the total number of students enrolled at the institution, demographics for males were White or Caucasian (n=12,491; 23.9%), Asian (n=1,690; 3.2%), Native Hawaiian or Pacific Islander (n=72; 0.13%), Hispanic (n=3,695; 7.0%) and Bi-racial or Multi-racial (n=480; 0.9%) (College Tuition Compare, 2017). When asked about their spiritual or religious affiliation, 24.1% were Protestant or Christian (n=26) and 19.4% self-identified as spiritual (n=21). Most of the participants reported having health insurance through a private plan under their parents/guardians (n=69; 63.9%) and of that amount 19.4% (n=21) agreed that cost or health insurance coverage would not stop them from getting the HPV vaccine (see Table 1).

Table 1.

Demographic Characteristics of Study Participants.

Age	Frequency	Percent	Race/ Ethnicity	Frequency	Percent
17 and under	0	0	American Indian or Alaskan Native	0	0
18 - 20	39	36.1	Asian	35	32.4
21 - 23	45	41.7	Black or African American	0	0
24 – 26	24	22.2	Hispanic	6	5.6
27 and over	0	0	Native Hawaiian or Pacific Islander	16	14.8
Total	108	100	White or Caucasian	46	42.6
			Bi-racial or Multi-racial	5	4.6
			Total	108	100
Religious/Spiritual Affiliation	Frequency	Percent	Health Insurance Coverage	Frequency	Percent
Protestant/ Christian	26	24.1	University Sponsored	18	16.7
Roman Catholic	13	12.0	Private with Parents/ Guardians	69	63.9
Anglican	1	0.9	Insurance Through Employer	6	5.5
Mormon / Latter –Day Saints	0	0	Public/ Government Sponsored	10	9.2
Orthodox	3	2.8	No Insurance	2	1.9
Judaism	2	1.9	Do Not Know/ Unsure	3	2.8
Islam	3	2.8	Total	108	100
Buddhism	9	8.3
Hinduism	8	7.4
Atheist (Do not believe in God)	9	8.3
Agnostic (Not sure if there is a God)	9	8.3
I am spiritual	21	19.4
I am not religious	4	3.7
Total	108	100

Table 2.

Condition Group Delineation for Experimental Study.

Condition	Frequency	Percent
1	27	25.0
2	24	22.2
3	29	26.9
4	28	25.9

Table 3.

Correlations Between Pre- and Post- Test Scores for Risk Perception, Self - Efficacy and Behavioral Intention.

Independent Variables	Risk Perception Score Pre	Risk Perception Score Post	Self –Efficacy Score Pre	Self- Efficacy Score Post	Behavioral Intention Score Pre	Behavioral Intention Score Post
Risk Perception Score Pre	1	.488**	-.095	-.090	-.043	.042
Risk Perception Score Post	.488**	1	.026	.105	.018	.345*
Self –Efficacy Score Pre	-.095	.026	1	.637**	.236	.064
Self- Efficacy Score Post	-.090	.105	.637**	1	.269	.195
Behavioral Intention Score Pre	-0.43	.018	.236	.269	1	.803**
Behavioral Intention Score Post	.042	.345*	.064	.195	.803**	1

Correlation is significant at the 0.01 level (2-tailed).

Correlation is significant at the 0.05 level (2 –tailed).

Participants in this study identified as freshmen (n=14; 13%), sophomores (n=18; 29.6%), juniors (n=24; 22.2%), seniors (n=22; 20.4%), graduate students (n=29; 26.9%), and professional students (n=1, 0.9%). The majority of participants pursued majors in the College of Engineering (n=37; 34.3%), College of Liberal Arts and Sciences (n=35; 32.4), and College of Business Administration (n=13; 12%).

Current Sexual Health Practices

Overall, 88% of our study sample self-identified as straight (n=95), whereas 4.6% self-identified as gay (n=5) and bisexual (n=5). When asked about sexual activity in the last 6 months, 55.6% indicated that they had been sexually active (n=60). More than half of the study participants (n=88; 81.4%) had less than 5 total lifetime sexual partners. Nearly 48% (n=52) reported between 1 and 4 sexual partners. When asked about condom practices in the last 6 months, 25.9% (n=28) always used condoms, 19.4% (n=21) sometimes used condoms and 16.7% (n=18) never used condoms. The majority of the sample reported never being diagnosed with an STD (n=104; 96.3%).

Digital Gaming Practices

Most of the participants in our study played video games prior to the study (n=95; 88%). Of those who played video games, the top three genre played most frequently were Role Playing Games (n=20; 18.5%), Sports (n=16; 14.8%) and First Person Shooter (n=15; 13.9%). Of our study participants, 20.4% played video games less than 1 hour per week (n=22), 23.1% played 1-3 hours per week (n=25) and 21.3% played 3-6 hours per week (n=23). Additionally, almost half of the participants preferred to play video games using a dedicated game console such as an Xbox, PlayStation Wii™ (n=49; 45.4%). Laptops or Personal Computers (PCs) were identified as the second most preferred platform (n=34; 31.5%) with smartphones ranking third (n=12; 11.1%).

Thirty-one percent (n=34) of our participants indicated that they had received health messages while playing a video game; 68.5% (n=74) indicated that they had not. Despite previous exposure, 87% (n=94) of gamers indicated that they would be receptive to receiving health messages while playing a video game. Nearly 40.7% of respondents indicated that they were ‘likely’ to be receptive to playing an existing video game embedded with HPV messages (n=44) as well as a game developed to increase knowledge and awareness of HPV (n=44). However, 53.7% (n=58) of respondents indicated that they were ‘extremely likely” to be receptive to HPV information presented in a HPV game in lieu of a lecture/assignment within a college sexual health course.

Risk Perception

There was not a statistically significant interaction between avatar type and perception of self (F (3,104) = 0.979, p=.406) in the model and no significant main effects for condition group (F (3,104) = .657, p=.581). Therefore, we cannot support our hypothesis. Despite having no main or interaction effect for risk perception, our analysis did report a significant main effect for the experimental game with avatar (regardless of type) as a stimulus (F (1,104) = 48.029, p<0.001). Participants in Condition 3 (customized avatar, actual self-perception) showed the largest mean difference (3.587) in risk perceptions for the HPV virus (see Table 4 and 5).

Table 4.

Estimated Marginal Means Condition * PrePost for HPV Risk Perception.

Condition	PrePost	Mean	Standard Error (SD)	Mean Difference
1	12	14.77817.370	.927.720	2.592
2	12	14.87517.542	.983.764	2.667
3	12	13.17216.759	.894.695	3.587
4	12	15.21417.000	.910.707	1.786

Table 5.

Tests of Within-Subjects Effects (Sphericity) for HPV Risk Perception.

Source		Type III Sum of Squares	df	Mean Square	F	Sig.
PrePost	Sphericity AssumedGreenhouse – GeisserHuynh-FeldtLower Bound	379.521379.521379.521379.521	11.0001.0001.000	379.521379.521379.521379.521	48.02948.02948.02948.029	.000.000.000.000
PrePost* Condition	Sphericity AssumedGreenhouse – GeisserHuynh-FeldtLower Bound	23.20023.20023.20023.200	33.0003.0003.000	7.7337.7337.7337.733	.979.979.979.979	.406.406.406.406
Error (PrePost)	Sphericity AssumedGreenhouse – GeisserHuynh-FeldtLower Bound	821.800821.800821.800821.800	104104.000104.000104.000	7.9027.9027.9027.902

Self-Efficacy

There was not a statistically significant interaction between avatar type and perception of self (F (3,104) = 0.567, p =.638) in the model and no significant main effects for condition group (F (3,104) = .254, p =.859). Therefore, we cannot support our hypothesis. Our analysis did report a significant main effect for the experimental game with avatar (regardless of type) as a stimulus (F (1,104) = 28.44, p<0.001). Condition 3 (customized avatar, actual self-perception) had the largest mean difference (1.793) demonstrating that for experimental data, having a customized avatar most representative of one’s actual self is most indicative of an increased level of self-efficacy or confidence to receive the HPV vaccine (see Table 6 and 7).

Table 6.

Estimated Marginal Means Condition * PrePost for HPV Self-Efficacy.

Condition	PrePost	Mean	Standard Error (SD)	Mean Difference
1	12	16.48117.519	.697.558	1.038
2	12	16.20817.250	.739.592	1.042
3	12	15.51717.310	.673.538	1.793
4	12	15.60717.179	.685.548	1.572

Table 7.

Tests of Within-Subjects Effects (Sphericity) for HPV Self-Efficacy.

Source		Type III Sum of Squares	df	Mean Square	F	Sig.
PrePost	Sphericity AssumedGreenhouse – GeisserHuynh-FeldtLower Bound	99.49299.49299.49299.492	11.0001.0001.000	99.49299.49299.49299.492	28.4428.4428.4428.44	.000.000.000.000
PrePost* Condition	Sphericity AssumedGreenhouse – GeisserHuynh-FeldtLower Bound	5.9495.9495.9495.949	33.0003.0003.000	1.9831.9831.9831.983	.567.567.567.567	.638.638.638.638
Error (PrePost)	Sphericity AssumedGreenhouse – GeisserHuynh-FeldtLower Bound	363.769363.769363.769363.769	104104.000104.000104.000	3.4983.4983.4983.498

Behavioral Intention

There was not a statistically significant interaction between avatar type and perception of self (F (3,104) = 1.549, p=.206) in the model and no significant main effects for condition group (F (3,104) = .249, p=.862). Therefore, we cannot support our hypothesis. Despite having no main effect (condition group) or interaction effect for behavioral intention, our analysis did report a significant main effect for the experimental game with avatar (regardless of type) as a stimulus (F (1,104) = 33.907, p<0.001). Condition 1 (customized avatar, ideal self-perception) had the largest mean difference (2.82); whereas, Condition 3 (customized avatar, actual self-perception) had the lowest mean difference (0.827) demonstrating that for experimental data customizing an avatar to represent your ideal self was the most indicative of an increased level of behavioral intention to receive the HPV vaccine (see Table 8 and 9).

Table 8.

Estimated Marginal Means Condition * PrePost for HPV Behavioral Intention.

Condition	PrePost	Mean	Standard Error (SD)	Mean Difference
1	12	16.77819.598	.9461.023	2.82
2	12	16.95819.417	1.0031.085	2.459
3	12	17.89718.724	.912.987	0.827
4	12	17.89320.357	.9291.005	2.464

Table 9.

Tests of Within-Subjects Effects (Sphericity) for HPV Behavioral Intention.

Source		Type III Sum of Squares	df	Mean Square	F	Sig.
PrePost	Sphericity AssumedGreenhouse – GeisserHuynh-FeldtLower Bound	246.337246.337246.337246.337	11.0001.0001.000	246.337246.337246.337246.337	33.90733.90733.90733.907	.000.000.000.000
PrePost* Condition	Sphericity AssumedGreenhouse – GeisserHuynh-FeldtLower Bound	33.76633.76633.76633.766	33.0003.0003.000	11.25511.25511.25511.255	1.5491.5491.5491.549	.206.206.206.206
Error (PrePost)	Sphericity AssumedGreenhouse – GeisserHuynh-FeldtLower Bound	755.567755.567755.567755.567	104104.000104.000104.000	7.2657.2657.2657.265

Discussion

Digital gaming shows promise as a communication tool for delivering health interventions and improving sexual health behavior among the digital native population. This study is the first to our knowledge that explores use of digital gaming to improve HPV and HPV-related vaccine outcomes among college men. Although our results were not statistically significant, our data analysis did indicate an increase in gain score for risk perception, self –efficacy and behavioral intention when participants were able to customize their avatar to look like their ideal or actual self. This reflected similar research that indicates that playing as their actual self would lead to higher perceptions of physical risks, such as HPV risk (Kim & Sundar, 2012). Our results were comparable to previous research that identified that, “the more they visualized their ideal body through their desired avatar, the more motivated they were to engage in preventative behaviors” (Kim & Sundar, 2012, p. 1363). Results for self-efficacy however, proved to be significantly different to research previously conducted. According to Kim and Sundar (2012), the greater the risk perceived by players playing as their actual self, the less motivated they would be engage in proactive behaviors.

Limitations and Implications for Future Research

Although results are promising, there are limitations to our study. First, the use of avatars with the Vax! Game may not have been an appropriate or automatic fit. In conversation with gamers, participants indicated that they saw Vax! as more of a strategy game. Additionally, although an adequate range of customization options was available to users, the SitePal software limited extensive creativity in changing physical features or attributes. Compared to the vivid graphics of today’s role playing games, the avatars appeared “cartoony” in nature which could have affected the perception of the avatar truly looking like one’s ideal or actual self. Lastly, the “assigned” and “customized” avatars may not have been significantly different in appearance to produce any treatment or interaction effects among experimental groups.

As part of the experiment, research staff had to provide instructions to participants so that they could progress and complete the study as planned. Detailed instructions were necessary due to limitations in being able to alter the existing Vax! Game to allow for the avatar customization, behavioral priming exercise, and game to be housed in one space and naturally flow as one stimulus. Because of this, participants may not have been able to immerse themselves in the Vax! Game and achieve a constant level of flow while playing, which could have affected the results.

It can be concluded from our study that the use of an avatar is important; however, the type of avatar may not be as important when discussing games related to the HPV virus and HPV vaccine. To explore this idea further, studies should look at the use of more advanced avatar customization software similar to the vivid imagery and graphics often found in off-the-shelf video games. Future studies should also consider utilizing a game where the customization of the avatar and priming exercise is integrated in the game as one system rather than via separate software and experimental components used in our study.

This study did not include a true control group to truly measure impact of the independent variables. Therefore, future research should include a true control to further test effects and include background variables (sexual protection/condom use, orientation, identity) in the analysis due possibly mediating the interaction. Apart from background variables, future researchers should also look at the possibility of using full body avatars customized to ideal and actual perceptions of self since HPV is a sexually transmitted disease affecting the sexual organs. Since sexuality, manhood and overall appearance extend beyond the face, full body avatars may be more salient for men.

Because two of the constructs measured in this study came from the Health Belief Model (i.e., perceived threat, self-efficacy) with another being derived from the Theory of Planned Behavior (i.e., behavioral intention), future HPV studies for college age men should investigate the impact of digital gaming that leverages all HBM constructs, including perceived benefits and perceived barriers of taking action. Although this study measured behavioral intention as a proxy for behavior change, future research should also directly measure completion of the 3 shot HPV vaccination series by the target population. A gap still exists in the literature on HPV vaccination studies among college age men measuring vaccine series completion.

Lastly, there was limited racial/ethnic diversity in the participants of the digital gaming study. None of the study participants identified as Black or American Indian/Alaskan Native. Placing an emphasis on vaccinating males from racial/minority groups can protect females from those circles and communities as young adults tend to have sexual partners from within their own community (Jeudin, Liveright, Del Carmen, & Perkins, 2014). Since research shows that racial/ethnic minorities account for a disproportionate number of HPV-related cancers) cultural differences could affect vaccine uptake (Okafor, Hu, & Cook, 2015. Future research should expand this study to be replicated among specific isolated racial and ethnic groups (Anderson, 2015; Daley et al., 2011). Such studies could demonstrate positive improvements in in preventing HPV-related cancers and conditions among males from various minority groups. This approach could prove to be a strategic way to achieve herd immunity among risky sexual networks and thereby reduce the disproportionately high rates of cancer in female minority groups such as Blacks and Hispanics.

Conclusion

Research on the human papillomavirus (HPV) and vaccine is still lacking among college age males. However, extensive opportunities exist to utilize information communication technologies like digital gaming change HPV knowledge and impact HPV vaccine. Despite the fact that a large percentage of college age men play video games, video gaming interventions have been used heavily in other aspects of health promotion such as physical activity, diabetes prevention and management, anxiety and depression. Few interventions and research studies have explored the application to sexual health and specifically as it relates to HPV prevention. The digital game or video game presents itself as the optimal platform to communicate and educate college age men about a topic that could be very stigmatizing and uncomfortable to discuss. Furthermore, using a digital game can have direct impact on HPV risk perception increasing the likelihood of vaccine uptake. The team that contributed to our study included researchers in public health, health communication, media psychology, computer engineering, research methodology and evaluation. Therefore, an interdisciplinary team of stakeholders involved in the HPV vaccine conversation should all partake in the development of a digital game that would be most receptive by college age men.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the NIH/NCATS Clinical and Translational Science Award to the University of Florida UL1 TR000064.

Author Biographies

Gabrielle Darville is an Instructor and Evaluation Coordinator in the College of Public Health and holds an Affiliate Faculty appointment in the Institute of Women’s Studies at the University of Georgia. She holds a MPH from the University of Southern Mississippi, and PhD in Health Behavior from the University of Florida. Her research focuses on utilizing health information technologies to impact HPV vaccine informed decision making among adolescents/young adults; as well as improving health outcomes among underserved population groups.

Charkarra Anderson-Lewis is a Professor of Public Health and serves as an Associate Dean for the College of Nursing and Health Professions and the Director of the School of Health Professions. She received her MPH from the University of Southern Mississippi and PhD in Health Education and Promotion from the University of Alabama Birmingham. Her research focuses on utilizing digital health technologies, community engaged research, community health workers and qualitative research methods to address chronic health disparities.

Michael Stellefson is an Associate Professor in the Department of Health Education and Promotion at East Carolina University. He received his PhD in Health Education from Texas A&M University. His research program focuses on developing and evaluating patient-centered technologies to improve chronic disease self-management.

Jann MacInnes is a Lecturer at the University of Florida in Research and Evaluation Methodology. She holds a PhD in Research and Evaluation Methodology from the University of Florida and a Masters in Statistics from the University. Her research interests include applying statistics, research and evaluation methodology to behavioral science, health and educational psychology.

Yu-Hao Lee is an Assistant Professor in the Department of Telecommunication at University of Florida. He received his PhD from Michigan State University in Media and Information Studies. His research examines how users process and respond to messages communicated through interactive communication technology such as digital games, virtual reality, and social media.

R. Morgan Pigg Jr. is Professor Emeritus in the Department of Health Education at the University of Florida. He holds H.S.D. and M.P.H. degrees from Indiana University and has held faculty positions at the University of Georgia, Indiana University and the University of Florida. His professional and research interests include child and adolescent health, philosophy and ethics, and effects of the “entertainment culture” on the civic, social, and character development of young people.

Juan E. Gilbert is the Andrew Banks Family Preeminence Endowed Professor and Chair of the Computer & Information Science & Engineering Department at the University of Florida where he leads the Human Experience Research Lab. He is also a Fellow of the American Association of the Advancement of Science, a Fellow of the National Academy of Inventors, an ACM Distinguished Scientist and a Senior Member of the IEEE. Dr. Gilbert received his MS and PhD in computer science from the University of Cincinnati and his BS in systems analysis from Miami University.

Sanethia Thomas is a PhD Candidate, a National Science Foundation (NSF) Graduate Research Fellow, and GEM fellow at the University of Florida in the Human Experience Research Lab under Dr. Juan Gilbert. She holds a bachelor’s degree in Information Technology from the University of Texas El Paso and a Masters in Youth Development Leadership from Clemson University. Her research is interdisciplinary as it includes the fields of Human-Computer Interaction (HCI), Athlete Development, Artificial Intelligence and Affective Computing. Specifically, she explores the concept using Natural Language Interfaces and Intelligent Embodied Conversational Agents.

References

Abel

J. I.

Buff

C. L.

O’Neill

J. C.

(2013). Actual self-concept versus ideal self-concept: An examination of image congruence and consumers in the health club industry. Sport, Business and Management: An International Journal, 3(1), 78-96. doi:10.1108/20426781311316915

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. doi:10.1016/0749-5978(b91)90020-T

Anderson

(2015, December). Views on gaming differ by race, ethnicity. Retrieved from http://www.pewresearch.org/fact-tank/2015/12/17/views-on-gaming-differ-by-race-ethnicity/

Anic

G. M.

Giuliano

A. R.

(2011). Genital HPV infection and related lesions in men. Preventive Medicine, 53, S36-S41. doi:10.1016/j.ypmed.2011.08.002

Bailey

Wise

Bolls

(2009). How avatar customizability affects children’s arousal and subjective presence during junk food–sponsored online video games. Cyberpsychology & Behavior, 12(3), 277-283. doi:10.1089/cpb.2008.0292

Belk

R. W.

(1988). Possessions and the extended self. Journal of Consumer Research, 15(2), 139-168. doi:10.1086/209154

Belk

(2014). Digital consumption and the extended self. Journal of Marketing Management, 30(11-12), 1101-1118. doi:10.1080/0267257X.2014.939217

Benson

(2002). The cultural psychology of self: Place, morality and art in human worlds. New York, NY: Routledge.

Bessière

Seay

A. F.

Kiesler

(2007). The ideal elf: Identity exploration in World of Warcraft. Cyberpsychology & Behavior, 10(4), 530-535. doi:10.1089/cpb.2007.9994

10.

Cacioppo

J. T.

Von Hippel

Ernst

J. M.

(1997). Mapping cognitive structures and processes through verbal content: The thought-listing technique. Journal of Consulting and Clinical Psychology, 65(6), 928. doi:10.1037/0022-006X.65.6.928

11.

Centers for Disease Control and Prevention. (2016, December). Human papillomavirus. Retrieved from https://www.cdc.gov/hpv/parents/whatishpv.html

12.

Cohen

(2001). Defining identification: A theoretical look at the identification of audiences with media characters. Mass Communication and Society, 4(3), 245-264. doi:10.1207/S15327825MCS040301

13.

College Tuition Compare. (2017). Student population at the University of Florida. Retrieved from https://www.collegetuitioncompare.com/edu/134130/university-of-florida/enrollment/

14.

Crosignani

De Stefani

Fara

G. M.

Isidori

A. M.

Lenzi

Liverani

C. A.

. . . Signorelli

(2013). Towards the eradication of HPV infection through universal specific vaccination. BMC Public Health, 13(1), Article 642. doi:10.1186/1471-2458-13-642

15.

Daley

E. M.

Marhefka

Buhi

Hernandez

N. D.

Chandler

Vamos

. . . Giuliano

A. R.

(2011). Ethnic and racial differences in HPV knowledge and vaccine intentions among men receiving HPV test results. Vaccine, 29(23), 4013-4018. doi:10.1016/j.vaccine.2011.03.060

16.

Davies

McCrae

B. P.

Frank

Dochnahl

Pickering

Harrison

. . . Wilson

(2000). Identifying male college students’ perceived health needs, barriers to seeking help, and recommendations to help men adopt healthier lifestyles. Journal of American College Health, 48(6), 259-267. doi:10.1080/07448480009596267

17.

DeSmet

Shegog

Van Ryckeghem

Crombez

De Bourdeaudhuij

(2015). A systematic review and meta-analysis of interventions for sexual health promotion involving serious digital games. Games for Health Journal, 4(2), 78-90. doi:10.1089/g4h.2014.0110

18.

Division of STD Prevention. (2015, November). Sexually transmitted disease surveillance report 2014. Retrieved from http://www.cdc.gov/std/stats14/surv-2014-print.pdf

19.

Fogel

Ebadi

(2011). Religious categories and the human papillomavirus (HPV) vaccine: Attitudes, intentions, and behaviors regarding vaccination. Journal of Medical Marketing, 11(4), 303-311. doi:10.1177/1745790411428232

20.

Fontenot

H. B.

Collins Fantasia

Charyk

Sutherland

M. A.

(2014). Human papillomavirus (HPV) risk factors, vaccination patterns, and vaccine perceptions among a sample of male college students. Journal of American College Health, 62(3), 186-192. doi:10.1080/07448481.2013.872649

21.

Gargano

Meites

Watson

Unger

Markowitz

(2017). Human papillomavirus. In VPD surveillance manual. Retrieved from https://www.cdc.gov/vaccines/pubs/surv-manual/chpt05-hpv.pdf

22.

Gerend

M. A.

Magliore

Z. F.

(2008). Awareness, Knowledge, and Beliefs about Human Papillomavirus in a Racially Diverse Sample of Young Adults. Journal of Adolescent Health, 42, 237-242. doi: 10.1016 j.jadohealth.2007.08.022

23.

Glanz

Rimer

B. K.

Viswanath

(Eds.). (2008). Health Behavior and Health Education: Theory, Research and Practice (4th Edition). San Francisco, CA: Jossey-Bass.

24.

Grace-Leitch

Shneyderman

(2016). Using the Health Belief Model to examine the link between HPV knowledge and self-efficacy for preventive behaviors of male students at a two-year college in New York City. Behavioral Medicine, 42(3), 205-210. doi:10.1080/08964289.2015.1121131

25.

Gutierrez

Leung

Jones

K. T.

Smith

Silverman

Frank

Leader

A. E.

(2013). Acceptability of the human papillomavirus vaccine among urban adolescent males. American Journal of Mens Health, 7(1), 27-36. doi:10.1177/1557988312456697

26.

Han

J. J.

Beltran

T. H.

Song

J. W.

Klaric

Choi

Y. S.

(2017). Prevalence of genital human papillomavirus infection and human papillomavirus vaccination rates among US adult men: National Health and Nutrition Examination Survey (NHANES) 2013-2014. JAMA Oncology, 3(6), 810-816. doi:10.1001/jamaoncol.2016.6192

27.

Hittner

J. B.

Owens

E. C.

Swickert

R. J.

(2016). Influence of social settings on risky sexual behavior. SAGE Open, 6(1), 1-11. doi:10.1177/2158244016629187

28.

Hosseini

P. S. K.

Munoz

Lakon

C. M.

Brown

(2014). Factors correlating perceptions of HPV and vaccine uptake among high school students. California Journal of Health Promotion, 12(3), 79-87. Retrieved from http://cjhp.fullerton.edu/Volume12Issue3_2014/documents/79-87FormattedKhalessiBrown_2014Issue3.pdf

29.

Jeudin

Liveright

Del Carmen

M. G.

Perkins

R. B.

(2014). Race, ethnicity, and income factors impacting human papillomavirus vaccination rates. Clinical Therapeutics, 36(1), 24-37. doi:10.1016/j.clinthera.2013.11.001

30.

Jin

S. A. A.

(2009). Avatars mirroring the actual self versus projecting the ideal self: The effects of self-priming on interactivity and immersion in an exergame, Wii Fit. Cyberpsychology & Behavior, 12(6), 761-765. doi:10.1089/cpb.2009.0130

31.

Jones

Perez

Huta

Rosberger

Lebel

(2016). The role of human papillomavirus (HPV)-related stigma on HPV vaccine decision-making among college males. Journal of American College Health, 64(7), 545-554. doi:10.1080/07448481.2016.1192542

32.

Jones

Cook

(2008). Intent to receive an HPV vaccine among university men and women and implications for vaccine administration. Journal of American College Health, 57(1), 23-32. doi:10.3200/JACH.57.1.23-32

33.

Kalyanaraman

Sundar

S. S.

(2006). The psychological appeal of personalized content in web portals: Does customization affect attitudes and behavior? Journal of Communication, 56(1), 110-132. doi:10.1111/j.1460-2466.2006.00006.x

34.

Katz

M. L.

Krieger

J. L.

Roberto

A. J.

(2011). Human papillomavirus (HPV): college male’s knowledge, perceived risk, sources of information, vaccine barriers and communication. Journal of Men’s Health, 8(3), 175-184. doi:10.1016/j.jomh.2011.04.002

35.

Katz

M. L.

Kam

J. A.

Krieger

J. L.

Roberto

A. J.

(2012). Predicting human papillomavirus vaccine intentions of college-aged males: An examination of parents’ and son’s perceptions. Journal of American College Health, 60(6), 449-459. doi:10.1080/07448481.2012.673523

36.

Kim

Sundar

S. S.

(2012). Visualizing ideal self vs. actual self through avatars: Impact on preventive health outcomes. Computers in Human Behavior, 28(4), 1356-1364. doi:10.1016/j.chb.2012.02.021

37.

Konijn

E. A.

Bijvank

M. N.

(2009). Door to another me: Identity construction through digital game play. In Ritterfeld

Cody

Vorderer

(Eds.), Serious games (pp. 201-225). New York, NY: Routledge.

38.

Krieger

J. L.

Sarge

M. A.

(2013). A serial mediation model of message framing on intentions to receive the human papillomavirus (HPV) vaccine: Revisiting the role of threat and efficacy perceptions. Health Communication, 28(1), 5-19. doi:10.1080/10410236.2012.734914

39.

Leary

M. R.

Estrada

M. J.

Allen

A. B.

(2009). The analogue-I and the analogue-me: The avatars of the self. Self and Identity, 8(2-3), 147-161. doi:10.1080/15298860802501532

40.

D. D.

Liau

A. K.

Khoo

(2013). Player–Avatar Identification in video gaming: Concept and measurement. Computers in Human Behavior, 29(1), 257-263. doi:10.1016/j.chb.2012.09.002

41.

McCallum

(2012). Gamification and serious games for personalized health. Student Health Technology Information, 177, 85-96. doi:10.3233/978-1-61499-069-7-85

42.

Mehta

Sharma

Lee

R. C.

(2014). Designing and evaluating a health belief model-based intervention to increase intent of HPV vaccination among college males. International Quarterly of Community Health Education, 34(1), 101-117. doi:10.2190/IQ.34.1.h

43.

Merck. (2017). What is HPV? Retrieved from https://www.gardasil9.com/hpv-facts/what-is-hpv/

44.

National Institute of Allergy and Infectious Diseases. (2010). Community immunity (“Herd” immunity). Retrieved from https://www.niaid.nih.gov/topics/pages/communityimmunity.aspx

45.

National Library of Medicine. (n.d.). Consumer health informatics research resource—Behavioral intention. Retrieved from https://chirr.nlm.nih.gov/behavioral-intention.php

46.

J. H.

Kim

M. K.

Jeon

Y. T.

Kim

Y. B.

Song

Y. S.

(2011). Human papillomavirus vaccine: Widening the scope for cancer prevention. Molecular Carcinogenesis, 50(4), 244-253. doi:10.1002/mc.20657

47.

Oddcast Company. (2018). Empower your online communication—Create a speaking avatar for your website [Software]. Available from www.sitepal.com/

48.

Okafor

Cook

R. L.

(2015). Racial/ethnic disparities in HPV vaccine uptake among a sample of college women. Journal of Racial and Ethnic Health Disparities, 2(3), 311-316. doi:10.1007/s40615-014-0074-7

49.

Park

S. B.

Chung

(2011). Mediating roles of self-presentation desire in online game community commitment and trust behavior of Massive Multiplayer Online Role-Playing Games. Computers in Human Behavior, 27(6), 2372-2379. doi:10.1016/j.chb.2011.07.016

50.

Patel

D. A.

Zochowski

Peterman

Dempsey

A. F.

Ernst

Dalton

V. K.

(2012). Human papillomavirus vaccine intent and uptake among female college Students. Journal of American College Health, 60 (2), 151-161. doi:10.1080/07448481.2011.580028

51.

Pew Research Center. (2016). Gaming and gamers. Retrieved from http://www.pewinternet.org/2015/12/15/attitudes-about-video-games/

52.

Prensky

(2001). Digital natives, digital immigrants part 1. On the Horizon, 9(5), 1-6. doi:10.1108/10748120110424816

53.

Priest

Knowlden

A. P.

(2015). Systematic review of primary prevention human papillomavirus interventions targeting college students. International Journal of Sexual Health, 27, 125-144. doi:10.1080/19317611.2014.945631

54.

Ratanasiripong

N. T.

(2015). Factors related to human papillomavirus (HPV) vaccination in college men. Public Health Nursing, 32(6), 645-653. doi:10.1111/phn.12198

55.

Sah

Y. J.

Ratan

Tsai

H. Y. S.

Peng

Sarinopoulos

(2017). Are you what your avatar eats? Health-behavior effects of avatar-manifested self-concept. Media Psychology, 20(4), 632-657. doi:10.1080/15213269.2016.1234397

56.

Salathe Group. (2014). Vax! A game about epidemic prevention [Computer software]. Retrieved from https://github.com/salathegroup/VaxGame

57.

SuperSaaS

B. V.

(2018). SuperSaaS appointment schedule [Computer software]. Available from https://www.supersaas.com/

58.

Thompson

(2012). Designing serious video games for health behavior change: Current status and future directions. Journal of Diabetes Science and Technology, 6(4), 807-811. doi:10.1177/193229681200600411

59.

Turkay

Kinzer

C. K.

(2014). The effects of avatar-based customization on player identification. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 6(1), 1-25. doi:10.4018/ijgcms.2014010101

60.

Vanhatupa

J. M.

(2011, June). Guidelines for personalizing the player experience in computer role-playing games. In Proceedings of the 6th International Conference on Foundations of Digital Games (pp. 46-52). ACM. doi:10.1145/2159365.2159372

61.

Vasalou

Joinson

Banziger

Goldie

Pitt

(2008). Avatars in social media: Balancing accuracy, playfulness and embodied messages. International Journal of Human-Computer Studies, 66, 801-811. doi:10.1016/j.ijhcs.2008.08.002

62.

Ward

B. W.

Dahlhamer

J. M.

Galinsky

A. M.

Joestl

S. S.

(2014, July). Sexual orientation and health among U.S. adults: National Health Interview Survey, 2013. National Health Statistics Report, 77, 1-12. Retrieved from http://www.cdc.gov/nchs/data/nhsr/nhsr077.pdf

63.

Yee

Bailenson

(2007). The Proteus effect: The effect of transformed self-representation on behavior. Human Communication Research, 33(3), 271-290. doi:10.1111/j.1468-2958.2007.00299.x

64.

Zimet

G. D.

Rosenthal

S. L.

(2010). HPV vaccine and males: Issues and challenges. Gynecologic Oncology, 117(2), S26-S31. doi:10.1016/j.ygyno.2010.01.028