40 minute read
Potential Benefits of Social Support in Video Games On College Student Mental Health During COVID-19
Celestial Pigart
Author’s note
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Advisor: Matthew C. Whited Acknowledgments: I am extremely grateful to Emily Midgette, Alex Capiaghi, Ashley Grif fith, Hunter Davis, and Ashlan McNinch f or all the guidance with survey design. Thank you!
Abstract
Many studies examine video games as mediators of aggression or addictive behavior, but f ew seek to understand possible associations with positive aspects of mental health. The American Psychological Association states, "Nearly one in f ive U.S. adults experience some f orm of mental illness, and one in twenty-four experience a serious mental illness" (APA 2018). College students are joining that statistic at an increasingly stressful point in their lives as culture shock, moving away f rom home, and high course loads are abundant f or several years of their early adulthood. Potential f actors of student anxiety and depression include not only academic stress, but lack of effective social support and coping strategies. This study seeks to understand both the potential positive outcomes of video games as a hobby with a f ocus on measuring online social support and coping behaviors. One-hundred and f ifty students were surveyed across introductory psychology sections at ECU where routine gaming behaviors were reported and compared against measures that assess f or depression, anxiety, social anxiety, alcohol use, social support, happiness, and perceived group inclusion. Further, the higher a participant endorsed solo gaming, the higher they scored on the Social Phobia Inventory (SPIN) measure which was not f ound in cooperative gaming. The results of this survey suggest that any level of cooperative gaming increases perceived group inclusion f or college students, and that cooperative gaming may serve as a protective factor to social anxiety. Keywords: mental health, video games, social support
Mental Health in College Students
A study by Einsenberg, Golberstein, and Hunt (2009) suggests that depression is a strong predictor of drop-out and low GPA (Einsenberg, Golberstein, Hunt 2009). With high anxiety and depression rates, maladaptive coping strategies, and a lack of ef fective social support, students may turn to drugs, f ood, or distractions such as hobbies like video games. Mental health awareness has increased over the past f ew decades; mental health is inf luential in not only how we think, f eel, and handle stress but also in the psychological, social, and emotional well-being of every individual (US Department of Health & Human Services 2019). This increase in awareness is a step in the right direction as the American Psychological Association states, "Nearly one in f ive U.S. adults experience some f orm of mental illness, and one in twenty-four experience a serious mental illness" (APA 2018). College students are joining that
“Suicide is the second-leading cause statistic at an increasingly stressful of death among college students, point in their lives as culture shock, resulting in over 1,100 lives lost moving away f rom home, and each year” (Floyd, Mimms, & Yelding academic or career stress is abundant 2007). f or several years of their early adulthood. With the addition of a biopsychosocial imbalance during such a hectic schedule, the mental health and overall well-being of a college student can be jeopardized with devastating outcomes. Suicide is the second-leading cause of death among college students, resulting in over 1,100 lives lost each year (Floyd, Mimms, & Yelding 2007). Poor mental health is a condition that can impact a student's ability to seek help, lowering academic perf ormance, involvement, and the ability to self-care (Moses, Bradley, & O’Callaghan 2016). In a survey of college student mental health conducted by APA, which spanned 400 university counseling centers, the investigation f ound that 41.6% of students had anxiety and 36.4% experienced depression (Association f or University and College Counseling Center Directors 2012). Potential f actors of student anxiety and depression include not only academic stress, but lack of ef fective social support and coping strategies. The National Center on Addiction and Substance Abuse (CASA) f ound more maladaptive coping strategies in college students who belonged to a f raternity or sorority than those not in Greek organizations (CASA 2003) emphasizing the importance of effective social support, in which being surrounded by peers may not always equate to a supportive network an individual may need. Furthermore, Blanchard-Fields and colleagues (1991) report that college students, “have been f ound more likely to use maladaptive coping strategies like escape avoidance compared to other age groups" (Blanchard-Fields, Sulsky, & Robinson-Whelen 1991). Students with poor quality of lif e at home are at high risk of depression and other mental health illnesses, which can cycle into poor academic performance.
Video gaming as a hobby
According to Merriam-Webster, a video game is f ormally def ined as “an electronic game in which players control images on a video screen” (Merriam-Webster), and the word f irst appeared in 1973; however, video games as a whole have diversified into a multitude of genres and play styles since its humble beginnings in Pong (Chodos & Tretkoff 2008). Genres vary signif icantly and video games can be categorized in dif f erent ways. For example, games produced by large production companies are of ten called “Triple-A titles” like Call of Duty, where smaller games like Stardew Valley, are considered “Indie” as they are made either by a small company or one person. Although hundreds of classification systems exist, the three major broad categories are online cooperative or co-op (e.g., Portal), online competitive (e.g., Overwatch), and single player of fline (e.g., Skyrim). It is also important to note two major distinctive modes in online gameplay, which sometimes exist within the same game: "Player vs Player" (PvP), which is ref erred to f or competitive play, and "Player vs Environment" (PvE), which is ref erred to f or f ighting computer-generated enemies (Bartle 2003). An important commonality between both PvP and PvE is the ability f or a game to provide a cooperatively multiplayer experience, where individuals work together towards a common goal; players may cooperatively f ight against both computer-generated units and/or other enemy players. Overall, no of ficial or systematic standardization of game categories exists, and this is a signif icant setback f or research in the f ield as new categories emerge for video games every year. Despite changes in nomenclature, core f eatures of video games include the degree to which a player has social interaction (playing either by themselves, locally with others, or online with others) and the method by which they choose to interact (cooperatively or competitively: one-on-one or as part of a team, against other players, or against artificial intelligence [AI] enemy units). Playing video games has become more mainstream over the past f ew years, and it is necessary to understand who, specifically, is a subject to studies involving mental health and video games. The term “gamer” is an evolving term as video gaming has become a mainstream hobby and is applied to any individual who plays digital games as a leisure or prof essional activity (Shaw 2012). There are of ten unspoken levels of intensity of ten associated with each game genre and thus, a f ew variations of the term “gamer” which vary f rom “casual” to “hardcore”. In World of Warcraft, the intensity or “level” of a player was associated with how many hours they routinely spent in the game. The level of game can sometimes be determined by dif ficulty to completion, and if the game itself is overall happy or dark in theme (Juul 2010). A great example of a dark theme classif ied as “hardcore” is the Dark Souls series, in which solo players f ight grotesque medieval-inspired monstrosities, whereas a relevant example of a happy theme that may be classif ied as “casual” is the Animal Crossing series in which a solo player builds and decorates a house while bef riending several anthropomorphized animal neighbors that resemble stuffed animals. Video games have entered most common households, with 57.45% of the respondents self-identified as gamers in a survey across f ive northeastern universities (Stone 2019). This suggests that there is a growing number of
college students who play video games and that playing video games is viewed positively by the general public.
Video games and aggression
Contrary to the increasingly positive perspective of the general public on video games, many studies in academia f ocus exclusively on the graphical depictions of violence and aggression while f ailing to understand the complexity of types of gameplay or the potential f or creating positive social support networks through gaming. A systematic review on violent video games and aggression (Griffiths 1999) examined the research methodologies of such studies which included observation of f ree play, self -report methods, and experimental research to conclude that “Studies only include possible short-term measures of aggressive consequences” and the majority of the studies are perf ormed with young children opposed to teens or emerging adults. Not much headway has been made since the start of the century when this study was perf ormed, although Doom has been re-released f or the umpteenth time (a game of research debate long past). In terms of recent research, a randomized control trial by Valadez & Ferguson in 2012 sought to handle conf ounding variables of previous paramount studies, by randomly assigning one hundred participants to six dif f erent conditions based on varying levels of duration of playtime and genre. The genre was divided into violent gameplay, non-violent gameplay, and non-violent gameplay in an otherwise violent game and duration of gameplay was sorted by f ifteen or f orty-five-minute duration sessions (Valadez & Ferguson 2012). The effect sizes were insignif icant, suggesting that violence in video games has no statistical inf luence when the study is a randomized control trial. More than just violent video games exist and there are many subgenres with signif icant positive mental health potential even in their def ault commercial f orm.
Video games and mental health research
Video games have been explored as vehicles f or behavioral treatment therapies f or both mental health and physical activity. Colder and colleagues (2018a) created a national study on veterans recruited f rom VA clinics who were considered habitual gamers (7 hours or more game time per week) which, through interviews, f ound adaptive coping, well-being, and socializing online in-game environments beneficial f or veteran mental health (Colder et al 2018a). This study is not without its caveats, as its operational def inition of video games is rather broad by including mobile video games, or electronic games which are played on a smartphone or tablet, and its sample size is small with only twenty veteran participants. Despite these downsides, the interview qualitative style approach is a novel methodology in the existing body of research. In the same year, Colder and other colleagues compiled a “call-to-research” review article outlining several key research studies about commercial video games as vehicles f or mental health therapy because "Emerging research suggests that commercial, off-the-shelf video games have potential applications in preventive and therapeutic medicine. Despite these promising f indings, systematic efforts to characterize and better understand this potential have not been undertaken" (Colder et al 2018b). Video games already pre-
exist in the homes of most Americans, offering new avenues of accessibility to resources f or homebound or populations where traditional therapy may not be an option. Other studies maintain a neutral perspective on the argument, such as “Sleep quality and video game playing: Ef fect of intensity of video game playing and mental health” by Altintas, Karaca, Hullaert, & Tassi (2019) which consisted of 217 emerging adult participants that sought to investigate how video games have an inf luence on sleep quality which could impact mental health through cluster analysis looking at both sleep quality, medical outcomes, video game play per week, and intensity of video game play where results were sorted into either “high quality sleep prof ile” f or 132 participants and “low quality sleep prof ile” f or 85 participants. Individuals with a “low quality sleep prof ile” were more likely to have a long sleep latency, bigger sleep disturbances, worse subjective sleep quality, and use sleep-aid medication more f requently than those in the “high quality sleep prof ile” as well as reported higher intensity and duration of game play with more poor mental health than the “high quality” prof ile, despite no dif ference in groups f or daytime f unctioning or the duration of sleep. These two resulting groups showed dif ferent associations f or duration and intensity of video games played for each week, and subsequent sleep impairment associated with mental health impairment. Although this study sought to explore the hypothesis that game intensity impacts sleep which impacts mental health, bidirectionality exists as authors also mention that good mental health is a predictor of a higher sleep prof ile despite game intensity. This study teases apart the nuances which plague the current research because it suggests that duration of gaming is not a predictor of poor sleep and mental health, rather the cognitive investment and intensity of the game is the determining f actor. Many studies are quick to point a proverbial f inger at video games as a whole, and research that delves deeper into what it is about a particular video game genre, type, intensity, etc. that mediates adaptive and maladaptive outcomes f or mental health is scarce.
Commercial video games and involvement in goal setting
Research pertaining to goals within video games is predominantly within the serious (e.g., Duolingo) and exergame (e.g., Wii Fit) fields and is nonexistent f or commercially available video games where it is arguably more prominent. Within a commercial video game, a strong motivational f actor to explore or advance in the game is through an achievement system or a series of progression modules that of ten include achievements and goal setting. This includes but is not limited to leveling up a character, questing to explore a storyline or location, skill tiers or talent trees (e.g., leveling skills of the player's choice in Skyrim), competitive ranking (e.g., online competitive play in Call of Duty), and or customization unlocks (e.g., such as cool hats in Team Fortress 2). These methods of goal setting of ten have one thing in common: once obtained, a player has something that they can show of f to other players; whether that is something they could wear or a badge on their prof ile to display their rank. In the same breath, many of these achievements often encourage players to group together in order to progress or obtain that novel badge or item of honor, opening up additional goals within the game to pursue.
Commercial video games and time investment
Goal setting and the ef fect necessary to achieve those milestones may have additional social benef its, as individuals may trade time and energy invested into an online game f or a perceived inclusiveness and acceptance within a high-level community of the game. A study sought to investigate this phenomenon which has previously been labeled as "addictive" play and f ound that these players with high-time investments in an online game experienced the highest psychosocial benefits from that online community whereas casual players f elt lonelier (Snodgrass et al 2018). This stance on high-time investment is only a recent development, and it is more commonly understood that moderation of game-time is the most physically and mentally benef icial. Longman, Connor, and Obst report that MMORPGs played for social purposes experienced benefits such as low scores f or anxiety, depression, and stress compared to those who sunk more time into an MMORPG or played f or achievement purposes (Longman, Connor, and Obst 2009). This distinction is important to consider when reviewing studies that base judgment on the quantity of time invested into a game because the overall motivation f or the player can affect f or their mental health. Further, cooperatively multiplayer genres create a means of potential social interaction and adding a higher time investment, just as in Snodgrass and colleagues’ (2018) study, increases the potential psychological benefit. A gap in the research exists in exploring the mental health impact of not only the amount of time one plays a video game, but the inf luence of game genre, the degree of cooperation, and the motivation f or playing.
Online video games are currently the hottest genre of video games, and online versus of fline social support groups is an even hotter topic of debate. The majority of the research regarding commercial video games as a vehicle f or social support presumes a lesser effect of social video games and only mentions a social displacement hypothesis where online social circles are viewed as a negative alternative to of fline, or in-person, peer support (Nie 2001). From the perspective that internet activity is a highway to isolation, Nie notes that individuals who dedicate internet time must take that time f rom some other activity that is assumed to be social (and benef icial) in nature (Nie 2001). Conversely, time diary studies suggest that time dedicated to online activity is not removed f rom f riends and f amily; rather time with other media (e.g., TV), hygiene, and time spent at work (Kestnbaum, Robinson, Neustadtl, & Alvarez, 2002). A concern of Nie’s research is that it excludes the idea of the internet serving as a medium of socialization and ignores any compensatory nature of online social interaction, especially among those who play cooperatively multiplayer, where a group of gamers work together towards a common goal. Furthermore, longitudinal analysis between gaming and non-gaming social support among a representative sample of online players f ound that online video games are not connected to perceived social support either longitudinally or cross-sectionally (Domahidi et al 2018). Players may either not f eel lonely with online f riend groups as opposed to offline groups, or individuals may not f eel that online f riend groups are less substantial than of fline groups. It is important to note
that how an individual may f orm an online support group within a video game varies dramatically on the basis of the game genre, e.g., World of Warcraft can have "guilds" of hundreds of players working together whereas a Mario Kart game can have eight players all competing against each other (Williams 2006). Likewise, many games can have multiple "modes" of play creating a gray area in research that is dif f icult to study; f or example, Halo has a campaign that is of fline but can be played with multiple people on one TV or "local co-op", as well as a mode that lets teams of f ive compete to capture f lags in online gameplay or "online competitive" (Morris 2004). Regardless, these distinctions are valuable as the variations in game modes are inf luential of how players interact with other players and derive social support which may impact their mental health.
The current study
This study seeks to understand the outcomes of commercial video games on mental health with a f ocus on measuring online social support and coping behaviors. Many studies examine video games as mediators of aggression or addictive behavior, but f ew seek to understand possible associations with positive aspects of mental health.
Aim 1 - Vehicle for social support The f irst aim of this study is to determine if individuals who spend more of their overall game time in cooperative gaming (participants rated their percentage of cooperative gaming to total hours typically spent gaming in either a 0-15%, 16-40%, 41-60%, 6185%, or 86-100% category), who play in a group with others towards a common goal, present with higher scores in social measures. Hypothesis I states that those who endorsed cooperative gaming present lower scores on social anxiety (SPIN). Hypothesis II states that those who endorsed cooperative present with better scores on positive social support (OSSS). Hypothesis III, states that those who endorsed cooperative gaming. present with higher scores on an inclusivity and acceptance scale (PGIS).
Aim 2 – mental health, game modes and genres The second aim of this study is to determine if individuals that endorsed cooperative gaming (in the 16-40%, 41-60%, 61-85%, or 86-100% category) demonstrate better mental health. Mental health scales were chosen based on widespread use within the f ield and with good internal consistency (Cronbach’s a. > 0.75). Detailed descriptions of each measure’s validation are available in the next section. Hypothesis IV states that those who endorsed cooperative gaming demonstrate lower scores on measures of depression, social anxiety, and anxiety (PHQ-9, GAD7, BADS-SF). Hypothesis V states that those who endorsed cooperative gaming demonstrate lower scores on alcohol substance use (CAPS-r).
Measures and questionnaires
Gaming Questionnaire Individual items were also developed to assess what genres individuals preferred to play, whether they participated in PvP or PvE more, as well as if they pref erred one-onone or team f ight online interactions, or offline environments. Additionally, amount of time played on a weekly basis f or each preferred genre was also assessed by game type, cooperative play or single player, and individuals reported what time category they reported; either 0-15%, 16-40%, 41-60%, 61-85%, or 86-100%. It is important to note that these are categorical variables with numerical names, were not treated as continuous variables, and were not mutually exclusive within the sample. This scale was applied to players who endorsed single player use as well as cooperative multiplayer use.
Demographics Participants were asked to provide basic demographic data, including age, gender, f amily income, and race/ethnicity. Patient Health Questonnaire-9 (PHQ-9) The Patient Health Questionnaire (PHQ-9) is a nine-item self-report assessment examining the f requency of depression-related symptoms in the span of the past two weeks. The items exist on a f our-point scale where zero represents “not at all” and a score of three represents “nearly every day.” The higher total score of all the items suggests that an individual experiences more depression-related symptoms. The PHQ-9 demonstrates test-retest reliability (r = 0.84) and internal consistency (Cronbach’s a. = 0.89) to support its use as an ef f ective measurement tool (Kroenke, Spitzer, & Williams, 2001). Online Social Support Scale (OSSS) The Online Social Support Scale (OSSS) is a 40-item questionnaire which assesses the pillars of in-person social support—esteem/emotional, social companionship, inf ormation, and instrumental support—within an online setting. Ten items are dedicated to each pillar, with an additional ten social media items (e.g., “people respond to something I’ve posted online”) and eight gaming items (“Other players give me items, or f orge or craft items f or me during gameplay”) all graded on a f ive point-Likert scale f rom 0 meaning “never” to 4 meaning “a lot”. This scale demonstrates internal consistency across each of its f our subscales: esteem/emotional support (Cronbach’s a. = .95), social companionship (Cronbach’s a. = .94), inf ormational support (Cronbach’s a. = .95), and instrumental support (Cronbach’s a. = .95), demonstrating its ef ficacy as an ef fective measurement tool (Nick et al 2018).
General Anxiety Disorder 7 (GAD7) The General Anxiety Disorder 7-item scale (GAD7) is a brief questionnaire often used in healthcare settings as a screening f or common anxiety disorders and the degree of severity, if any, over the past two weeks. Items are rated on a f our-point scale f rom 0
meaning “not at all” to 4 meaning “nearly every day”, with mild symptoms f rom an overall score of 5-9, moderate symptoms at 10-14, and severe symptoms f or 15 or more. This scale demonstrates internal consistency (Cronbach’s a. = .89) supporting its use as a reliable measurement tool (Zhong et al 2009).
Behavioral Activation for Depression (BADS-SF) The Behavioral Activation f or Depression Scale Short Form (BADS-SF) is an abbreviated scale which measures avoidance and activation f or those experiencing depressive symptoms during the previous seven days. The scale considers changes in behavior regarding activation, avoidance/rumination, work/school impairment, and social impairment. The scale consists of 9-items rated on a 7-point Likert scale of 0 meaning “not at all” to 6 meaning “completely”. This scale demonstrates internal consistency (Cronbach’s a. = .819) supporting its use as a reliable measurement tool (Manos et al 2011).
College Alcohol Problem Scale—revised (CAPS-r) The College Alcohol Problem Scale-revised (CAPS-r) is an abbreviated scale of original 20-item CAPS scale developed to assess alcohol-related problems in undergraduate students. The revised scale consists of 8-items with two subscales dichotomized to either personal problems (e.g., “felt bad about myself”) or social problems (e.g., “drove under the inf luence”) graded on a 5-point scale f rom 0 meaning “never” to 5 meaning “ten or more times”. This abbreviated scale demonstrates acceptable internal consistency (Cronbach’s a. = .76) supporting its use as a reliable measurement tool (Talbott et al 2008).
Social Phobia Inventory (SPIN) The Social Phobia Inventory (SPIN) is a scale measuring social anxiety disorder and its severity. The SPIN scale uses a 17-item statement questionnaire (e.g., “I am af raid of people in authority” and “I avoid talking to people I don’t know”) scored on a 5-point Likert scale f rom 0 meaning “not at all” to 4 meaning “extremely” with an overall score of 19 dichotomizing those with symptoms of social phobia f rom those without. This scale demonstrates internal consistency (Cronbach’s a. = .92) and good test-retest reliability (r = .86) supporting its use as a reliable measurement tool (Antony et al 2006). Perceived Group Inclusion Scale (PGIS) The Perceived Group Inclusion Scale (PGIS) is a scale which measures perceptions of inclusion f rom perceptions of belonging and authenticity rather than the optimal distinctiveness or self-determination theory. PGIS is a 16-item questionnaire with subscales assessing perception of group membership, group affection, room f or authenticity, and value in authenticity. Each item was ranked on a 5-point Likert Scale f rom 1 meaning “strongly agree” to 5 meaning “strongly disagree”. The PGIS scale is a reliable measure of inclusion as a result of total reliability score (Cronbach’s a. >.96) and predictive validity, as well as its invariance between men and women, students, or employee populations, and f or cultural minority and majority populations.
Subjective Happiness Scale (SHS) The Subjective Happiness Scale (SHS) is a measurement designed to assess global subjective happiness of a research participant. This short f orm includes a self assessment of happiness, a rating in relation to peers, and two generalized statements, one positive and one negative, in which the participant is asked to rate how much it describes them. This scale uses a 7-point scale f rom 1 meaning “not at all” to 7 meaning “a great deal”. The SHS scale demonstrates reasonable internal consistency (Cronbach’s a. = .86) supporting its use as a measurement tool (Lyubomirsky & Lepper 1999).
Results
Game play style was “solo gaming” and “cooperative (co-op) gaming”, and amount of time spent in each category was reported in f ive levels f or analysis: 0-15%, 16-40%, 41-60%, 61-85%, or 86-100%. A subset of individuals identif ied as Hispanic (12%; N=18). Only 37.33% of participants were in a relationship (N=56) and the sample was predominately f emale (54.67%; N=82). The sample largely identif ied as gamers (76.67%; N=115). Other descriptive statistics were reported f or demographics (Table 5a-d. See Appendices f or tables.). Aim 1 A between-subjects analysis of variance was used if someone who endorsed more than 16% of cooperative gaming would present lower scores on social anxiety. The results were non-significant in cooperative gaming (Table 1). The results were also nonsignif icant in solo gaming (Table 1). It is worth noting that the SPIN measure was approaching an alpha of 0.05 (Table 1), suggesting a potential Type II error. A between-subjects analysis of variance was used to determine if those who endorsed more than 16% of cooperative gaming had reported greater scores on social support. The results were non-significant for the OSSS measure for solo gaming. The results were also non-signif icant f or cooperative gaming; however, signif icance was f ound f or the PGIS and its two subscales on belonging and authenticity in cooperative gaming only. Perceived group inclusion scores were substantially higher among individuals who endorsed the 16-40%, 41-60%, 61-85%, and 86-100% category in cooperative gaming compared to individuals who reported only “0-15% of time spent in cooperative gaming”; this f inding is also replicated within the belonging subscale. The subscale f or authenticity suggests that scores f or authenticity were signif icantly higher among individuals who f ell into the “86-100” and “16-40%” categories compared to those who f ell into the “0-15%” category. All other associations were non-significant for cooperative gaming. All other associations were non-significant f or solo gaming. Correlations were performed, which revealed that the higher a participant endorsed solo gaming, the higher they scored on the SPIN social anxiety measure (Table 4). Furthermore, the higher any individual scored f or social anxiety, the lower they scored on perceived group inclusion within a video game f riend group (Table 4).
Aim 2 The second aim of this study is to determine if individuals that endorsed cooperative gaming (playing in a group with others and working towards a common goal) demonstrate better mental health. A between-subjects analysis of variance was used to determine if those who endorsed cooperative gaming present lower scores on measures of depression, social anxiety, and anxiety (PHQ-9, GAD7, BADS-SF). The results were non-signif icant f or cooperative gaming across PHQ-9, GAD7, and BADS-SF (Table 2). The results were non-significant for solo gaming across PHQ-9, GAD7, and BADS-SF (Table 2). A between-subjects analysis of variance was used to determine if those who endorsed a higher degree of solo gaming will present lower scores on alcohol substance use (CAPSr). This result was non-signif icant. A between-subjects analysis of variance was used to determine if those who endorsed a higher degree of cooperative gaming present lower scores on alcohol substance use (CAPS-r). This measure demonstrates significance with an alpha level of 0.05 f or only cooperative gaming, such that individuals in the “6085%” category of cooperative gaming scored lower on the college alcohol problems scale compared to individuals in the “16-40%” category f or cooperative gaming (Table 2). The data also suggests that as individuals endorse cooperative gaming more, the higher they score on the behavioral activation of depression scale (Table 4).
Discussion
The cooperative gamer and solo gamer are of ten one in the same person, as someone can enjoy the delightf ul worlds of Mario Kart by themselves or with others. However, a gamer is a creature of practice and may lean towards friends to make games enjoyable rather than the content itself . In the context of mental health, having an online social support group through video games—especially amidst of the limited contact of COVID19—may serve as a guardrail to curb the f eeling of isolation and subsequent mental health disparities in college students. The relationship of video games and mental health is complex with many variables, two of which are social anxiety and social support. College students may use video games amidst COVID-19 as a vehicle f or social interaction.
This research sought to examine how solo gaming and cooperative gaming f aired on social anxiety measures as well as social support measures aimed specifically at an online setting. The research was inconclusive with an alpha level of 0.05; more data is necessary to tease apart social anxiety in solo gaming, as well as cooperative gaming. From the inconclusive results, there is the suggestion that with a large enough sample size, Type II error rate may be reduced to reveal that more time spent solo gaming may predict higher scores on social anxiety measures. If this were the case, it may open the pathway to investigate if cooperative gaming serves as a protective f actor against social anxiety. Again, more data is needed before any substantial conclusions. The scale that asked participants to consider their online video game f riend group and rate how they f elt about this group (PGIS) f ound signif icance at an alpha level of 0.05 f or the entire
measure as well as both of its subscales in cooperative gaming only. The post-hoc testing discussed earlier, hints at the idea that 16% or more time reported of cooperative gaming may be beneficial to students’ concepts of acceptance and inclusiveness, which may serve as protective f actors f or their mental health amidst college. Although this study is unique in its perspective of assessing the impacts of video games on mental health by gaming style, in essence it supports the same notion f rom the national study on veterans which f ound adaptive coping, well-being, and socializing online in game environments beneficial f or [veteran] mental health (Colder et al 2018a). Working with others towards a single unif ying goal can bring a rhythm and harmony that might provide the grounds f or a bonding experience and emotional stability. Next, we examined the relationship between solo gaming and mental health scales. All measures came back inconclusive with an alpha level of 0.05; more data is necessary to tease apart mental health. This was repeated with cooperative gaming with the same inconclusive results. One noteworthy correlation for BADS-SF scale (Table 4) suggests a potential Type II error where signif icance may be f ound at the population level with replication and more data. The last mental health variable examined involved substance use of specifically alcohol. This substance was chosen given the population of college students, and the introduction of drinking around college campuses as individuals become legally able to consume alcoholic beverages. This measure demonstrates significance with an alpha level of 0.05 f or only cooperative gaming, such that individuals in the “60-85%” category of cooperative gaming scored lower on the college alcohol problems scale compared to individuals in the “16-40%” category for cooperative gaming. This may suggest that those who more highly endorse cooperative gaming, are at lower risk f or developing alcohol use complications; whether this is a result of the social network that exists within cooperative gaming or if it is the result of the game itself , is to be determined. Several limitations were present in this study. A restricted sample size due to COVID-19 time constraints meant survey participation was highly limited, and more signif icance may have been located within variables that demonstrated a medium to strong effect size yet an inconclusive p-value, suggesting hypothesis imprecision and potential Type II errors. This may be in part to analysis methods, where categorization of variables f or analysis of variance resulted in the loss of signif icance through estimation bias. In the f uture, this can be remedied with linear regression models using a continuous measure of time spent in solo gaming versus cooperative gaming. Interpretation is f urther limited in that the demographics of the sample call into question how representative it is of the sample; notably the f emale majority in sample of an otherwise male-dominated hobby population. Another limitation that presented itself was the f ormatting of the survey itself , such that the online support measure (OSSS) in its def ault f orm does not measure explicitly social support f ound in gaming, but rather anywhere online, including social media. Results f or OSSS emotional support subscale may be skewed if participants considered “online” beyond the context of video games. Due to sample size limitations, validity checks were generated but unaccounted f or within analysis in trade of saving
some statistical power. A potential limitation is a non-representative sample of the population, such that individuals in an introductory psychology course may or may not be as invested in video games in general compared to a survey administered directly to a video game community—on that same note, the entertainment system used by each participant went unaccounted f or, so f lash games on social media could count f or video games in one participants’ mind whereas only something on a computer or a console may f it the def inition f or another individual. In on-going replication of this study, the limitations which were the result of study design have been remedied. Despite these limitations, this study shows potential quantif iable dif ferences in social support, social anxiety, and mental health of those who endorse cooperative gaming. This is important f rom a standpoint where physical or in-person contact is restricted— such as during a pandemic—college students can develop and maintain social support through a virtual environment that engages them to work with others towards a common goal. While this is by no means anything more than a preliminary analysis to an exploratory study, there is evidence of associations within the population that support the importance of this topic. Further research is underway to establish conclusive results.
Appendices
Table 1
Aim 1 ANOVAs on solo gaming and ANOVAs on co-op gaming against scales SPIN, OSSS, and PGIS Gameplay Solo Co-op
Measure dfg dfe F p dfg dfe F p SPIN 4 94 1.98 0.11 4 97 0.18 0.95 OSSS 4 83 1.55 0.2 4 86 0.58 0.68 OSSS - emotional 4 91 1.53 0.2 4 94 1.74 0.15 OSSS - gaming 4 96 1.06 0.38 4 99 0.9 0.47 OSSS - social 4 93 1.05 0.39 4 96 1.98 0.1 OSSS - instrumental 4 94 0.81 0.52 4 97 0.24 0.91 OSSS - informational 4 91 0.78 0.54 4 94 0.69 0.6 PGIS 4 47 0.3 0.88 4 49 3.3 0.02 a PGIS belonging 4 48 0.74 0.57 4 50 3.74 0.01 a PGIS authentic 4 48 0.34 0.85 4 50 2.69 0.04 a a Significant with an alpha level of 0.05.
Table 2
Aim 2 ANOVA on solo gamer and co-op gamer types against scales PHQ-9, GAD-SF, BADS, and CAPS
Gameplay Solo Co-op
Measure dfg dfe F p dfg dfe F p CAPS-r 4 96 1.35 0.26 4 99 2.67 0.04 a BADS 4 93 1.53 0.2 4 96 1.55 0.19 PHQ9 4 96 0.72 0.58 4 99 1.39 0.24 GAD7 4 94 1 0.41 4 97 0.36 0.84
Note. Co-op = Cooperative online multiplayer, video game type of interest; solo = single player online, video game type of interest; PHQ9 = Patient Health Questionnaire, measure of clinical depression symptoms; GAD7 = General Anxiety Disorder, measure of general anxiety disorder; BADSF = Behavioral Activation for Depression short form, measure of depression; CAPS = College Alcohol Problems Scale Revised, measure of alcohol use. a Significant with an alpha level of 0.05.
Table 3
Correlations of social scales PGIS, SPIN, and OSSS with OSSS subscales Game Type OSS Subscales PGIS Subscales
Co op Solo OSSS (1) Emotional Social (2) l (3) Instrumenta Info (4) Gamer (5) PGIS authentic belonging SPIN
Co-op 1.00 Solo -.22* 1.00 OSSS 0.11 -0.10 1.00 OSSS (1) 0.14 -0.04 .80** 1.00 OSSS (2) .24* -0.07 .91** .75** 1.00 OSSS (3) -0.02 -0.06 .79** .57** .61** 1.00 OSSS (4) 0.12 -0.07 .89** .69** .78** .64** 1.00 OSSS (5) 0.10 -0.09 .70** .28** .54** .41** .49** 1.00 PGIS 0.27 -0.05 0.12 .31* .31* -0.13 0.27 0.24 1.00 authentic 0.26 -0.07 0.08 0.18 0.26 -0.10 0.23 0.15 .92** 1.00 belonging 0.26 -0.04 0.15 .35** .30* -0.11 0.26 .28* .95** .77** 1.00 SPIN -0.01 .22* 0.01 -0.08 0.03 0.07 -0.04 0.10 -.32* -0.26 -.27* 1.00 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
Note. Co-op = Cooperative online multiplayer, video game type of interest; solo = single player online, video game type of interest; PGIS = Perceived Group Inclusion Scale, how participants feel about their video game social group; OSSS = Online Social Support Scale, how participants feel generally about online social spaces; Emotional = Subscale of the OSSS looking at emotional support within online groups; Social = Subscale of the OSSS looking at social support within online groups; Instrumental = Subscale of the OSSS looking at perception of received help within online groups; Informational = Subscale of the OSSS looking at perception of received advice within online groups; Gamer = Subscale of the OSSS looking at perception of gaming support within online groups; SPIN = Social Phobia Inventory, social anxiety measure; Authentic = Subscale of SPIN, perception of inclusivity and ability to be authentic self within online video game group; Belonging = Subscale of SPIN, perception of acceptance and belonging within online video game group.
Table 4
Correlations of mental health measures Game Type Mental Health Scales Co-op Solo PGIS OSSS SPIN PHQ9 GAD7 BADSF CAPS Co-op 1.00 Solo -.22* 1.00 PGIS 0.27 -0.05 1.00 OSSS 0.11 -0.10 0.12 1.00 SPIN -0.01 .22* -.32* 0.01 1.00 PHQ9 0.13 0.01 0.00 0.06 .64** 1.00 GAD7 0.01 0.05 -0.04 -0.04 .71** .826** 1.00 BADSF .24* 0.07 .54** 0.08 0.00 0.11 0.11 1.00 CAPS -0.15 0.12 -0.05 -0.05 .50** .45** .52** 0.07 1.00 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
Note. Co-op = Cooperative online multiplayer, video game type of interest; solo = single player online, video game type of interest; PGIS = Perceived Group Inclusion Scale, how participants feel about their video game social group; OSSS = Online Social SupportScale, how participants feel generally about online social spaces; SPIN = Social Phobia Inventory, social anxiety measure; PHQ9 = Patient Health Questionnaire, measure of clinical depression symptoms; GAD7 = General Anxiety Disorder, measure of general anxiety disorder; BADSF = Behavioral Activation for Depression short form, measure of depression; CAPS = College Alcohol Problems Scale revised, measure of alcohol use.
Demographics
Table 5a
Participants by how difficult it was for bills to be paid in childhood Not Difficult Somewhat Very Extremely
Table 5b
Sample by race
Race Black White Asian Native American Native Hawaiian Multi-Ethnic Other Participants 21 110 3 2 0 8 4
Percent 14.19 74.32 2.03 1.35 0.00 5.41 2.70
Table 5c
Participants sorted by how many hours per week they dedicate to video games Video game Time Hours per week 1-3 4-10 11-20 21-30 31-50 50+ Participants 115 35 14 11 3 1 Percent 41.74 33.04 12.17 9.56 2.61 0.87
Table 5d Participants with relevant continuous demographic measures: Age, Semesters, GPA Mean SD Median Min Max Range Skew Kurtosis SE Age 18.49 .95 18 18 25 7 3.25 15.2 .08 Semesters 1.26 0.75 1 1 5 5 2.26 5.52 0.06 GPA 3.08 .92 3.3 1 4 4 -.22 4.68 0.08
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