Hello again

Contents lists available at ScienceDirect

Journal of Affective Disorders

journal homepage: www.elsevier.com/locate/jad

Research paper

Video game addiction in emerging adulthood: Cross-sectional evidence of pathology in video game addicts as compared to matched healthy controls☆

Laura Stockdale⁎, Sarah M. Coyne School of Family Life, Brigham Young University, 2086 JFSB, Provo, UT 84602, United States

A R T I C L E I N F O

Keywords: Video game addiction Pathological gaming Emerging adults Internet gaming addiction

A B S T R A C T

Background: The Internet Gaming Disorder Scale (IGDS) is a widely used measure of video game addiction, a pathology affecting a small percentage of all people who play video games. Emerging adult males are sig- nificantly more likely to be video game addicts. Few researchers have examined how people who qualify as video game addicts based on the IGDS compared to matched controls based on age, gender, race, and marital status. Method: The current study compared IGDS video game addicts to matched non-addicts in terms of their mental, physical, social-emotional health using self-report, survey methods. Results: Addicts had poorer mental health and cognitive functioning including poorer impulse control and ADHD symptoms compared to controls. Additionally, addicts displayed increased emotional difficulties including in- creased depression and anxiety, felt more socially isolated, and were more likely to display internet pornography pathological use symptoms. Female video game addicts were at unique risk for negative outcomes. Limitations: The sample for this study was undergraduate college students and self-report measures were used. Conclusions: Participants who met the IGDS criteria for video game addiction displayed poorer emotional, physical, mental, and social health, adding to the growing evidence that video game addictions are a valid phenomenon.

1. Introduction

Video games have become a normative part of Western culture. For most video game players, video games are a harmless way to relive stress, socialize with peers, and spend time. Parents of adolescents and young adults frequently joke that their kids are "addicted" to video games, but this is hyperbole for most youth. However, there is evidence that for some individuals, video game play can interfere with social functioning and well-being. There is no universal definition of addic- tion, but Orford (2001) defined addiction as "a combination of operant reward, usually in the form of some powerful emotional change, plus wide cue elicitation of conditioned responses that assists consumption in one way or the other, operating within diverse social contexts, be- tween them constitute a powerful set of processes responsible for the amplification of a small and unremarkable liking into a strong and potentially troublesome attachment (p.22)." Hellman et al. (2013) further elaborate that a reward in this context can be anything that is pleasurable, and does not limit only to substances, but can include re- wards like gambling and video games. Therefore, addiction need not be limited only to substances, but can include any external stimuli that

creates a "strong and potentially troublesome attachment." Video game use becomes pathological when this strong attachment damages mul- tiple levels of functioning such as family life, social functioning, school or work performance, or psychological functioning (Gentile et al., 2011).

1.1. Video game addiction

A nationally representative sample of 8–18-year-old youth in the United States found that approximately 8% of video game players dis- played pathological patterns of play (Gentile, 2009). In a nationally representative sample of 15–40-year-old participants in Norway ap- proximately 4.6% of video game players displayed pathological pat- terns of play and .6% met the criteria for true video game addiction (Mentzoni et al., 2011), suggesting that video game addiction is a rare, but valid phenomenon affecting a small percentage of video game players. In fact, formal features of video games may increase the like- lihood of developing addictive behaviors, similar to the formal features of slot machines increasing the likelihood of gambling addiction. Pre- vious researchers have argued that video games are exceptional

http://dx.doi.org/10.1016/j.jad.2017.08.045 Received 17 January 2017; Received in revised form 27 June 2017; Accepted 14 August 2017

☆ This research was funded by a LUROP fellowship awarded by the Loyola University Chicago Office of the Provost. ⁎ Corresponding author. E-mail address: lstockdale@byu.edu (L. Stockdale).

Journal of Affective Disorders 225 (2018) 265–272

Available online 18 August 2017 0165-0327/ © 2017 Elsevier B.V. All rights reserved.

T

teachers because they increase in difficulty as players’ master game content and technique, present multiple ways of solving or mastering a problem, require repeated practice over multiple days, provide rewards for achievement, increase popularity by achieving success, and provide an adrenaline rush which excite learners (Gentile and Gentile, 2005). These exceptional “teachers” have narrative and identity features such as being able to create an avatar playing in the game that looks like the player or how the player wishes they looked and constant reward and punishment features such as experience points, loss of life, gaining health, repairing items, difficult "bosses" at the end of a level, instant rewards, and the ability to instant replay a level, which all lead to in- creased difficulty disengaging from video games (King et al., 2010). These formal features which make video games excellent teachers and difficult to disengage from also increase the likelihood of developing addictive behaviors and tendencies.

1.2. Risk factors for video game addiction

Young adult males have been shown to be at the greatest risk for video game addiction possibly due to the flexible work/study hours associated with the higher education typical during this age range, living outside of the home for the first time, and increased autonomy (King et al., 2012b; Young, 1998). Time spent playing video games, poor social competence (Gentile et al., 2011), poor impulse control, increased sensation seeking, increased narcissistic personality traits (Griffiths et al., 2012), high state and trait anxiety (Mehroof and Griffiths, 2010) and previous truancy and few leisure activities (Rehbein et al., 2010) are all risk factors to developing video game addictions and in fact are risk factors to most addictive behaviors. In adolescents, being from a single parent home is a risk factor for de- veloping a video game addiction (Rehbein and Baier, 2013), likely due to lack of monitoring and increased time spent playing video games. A series of studies by Dong et al., (2010, 2013) found executive func- tioning problems in response to a color word stroop task in video game addicts, further reflecting the importance of poor impulse control and behavioral inhibition in video game addiction.

1.3. Outcomes of video game addiction

Video game addiction has been associated with a variety of negative psychological and social outcomes including decreased life satisfaction, loneliness, social competence (Lemmens et al., 2009), poorer academic achievement, increased impulsivity (Gentile, 2009), increased aggres- sion (Griffiths et al., 2012), and increased depression and anxiety (Mentzoni et al., 2011). It is important to note that time spent playing video games alone was not associated with these negative social, emotional, and psychological outcomes and that these negative out- comes are specifically related to video game addiction (Brunborg et al., 2014). Some research suggests that some of the negative consequences of pathological gaming can be negated if gamers are able to disconnect from the gaming world. For example, Gentile et al. (2011) found that depression, anxiety, and social phobias all improve when adolescents stop being a pathological gamer. Similarly, Cognitive Behavioral Therapy (CBT), a therapeutic approach that teaches people to recognize emotions and thought processes associated with addictions and learn coping skills to correct these cognitions, has been relatively effective at treating and preventing relapse of video game addictions (Griffiths and Meredith, 2009).

1.4. Purpose of the current study

Though much research has examined the risks and outcomes of video game addiction, all of the previously mentioned studies failed to compare video game addicts to age and gender matched healthy con- trols and instead compare addicts to the general population. Comparing video game addicts to the general population fails to take into account

subtle differences in mental, social, physical, and emotional health outcomes that vary by gender, ethnicity, age, and marital status. For example, racial-ethnic minority populations display significantly higher rates of obesity (Carroll et al., 2008; Paeratakul et al., 2002) and married people display lower rates of depression (Inaba et al., 2005). Thus, comparing a racial-ethnic minority or married video game addict to the general population may compound outcomes and falsely attri- bute differences in health outcomes to video game addiction. Similarly, the previous studies did not use measures of social and psychological functioning recommended by leading health organizations. This study seeks to further lend support to the potential validity of the IGDS as a measure of video game addiction by assessing the relationship between participants whose IGDS scores would qualify them as video game ad- dicts and how this classification is associated with poorer emotional, social, mental, and physical health. Therefore, the goal of the present study is to compare video game addicts to healthy controls that are matched on age, race, gender, and marital status on measures of phy- sical, social, mental, and emotional health recommended by the Na- tional Institute of Mental Health, the U.S. Department of Health and Human Services, and the World Health Organization. This study will also assess comorbidity between IGD video game addiction, substance use, and other online addictions. Previous researchers have shown high comorbidity between substance addiction and addictions to other sub- stances (Dani and Harris, 2005), gambling addiction and tobacco use (McGrath and Barrett, 2009), and gambling addiction and substance use and abuse (Lorains et al., 2011), and the comorbidity between addiction and psychiatric disorders (Kessler et al., 2008; Stein et al., 2001). However, few researchers have examined video game addiction and potential comorbidity with substance use, gambling, and internet pornography use.

We hypothesize that IGD video game addicts will display poorer social, emotional, physical, and mental health than matched non-ad- dicts. We also hypothesize that IGD video game addicts will display increased comorbidity between video game addiction and other ad- dictive behaviors as compared to matched non-addicts.

2. Method

2.1. Participants

1205 young adults (mean age = 20.32, SD age = 4.17; 48.85% male, 50.15% female, all participants reported their gender) who re- ported playing video games were recruited from two large universities in the United States, one in a large urban setting in the Midwest and one in the Mountain West. Of the 1205 young adults screened, 87 met the criteria for video game addiction (approximately 7%). The 87 video game addicts (mean age = 20.80, SD age = 2.18; 68% male, 15% female; 78.3% Non-Hispanic White, 6.6% African American, 2.8% Latino, 5.7% Asian, and 8.5% Other; 85% single, 15% married) were matched on geographical location, age, sex, ethnicity, and marital status to non-addicts. This results in a final sample of 174 addicts and non-addicts.

2.2. Procedures

Participants were recruited through university online systems for introductory psychology courses and were given class credit required for course completion for completing an online study. Participants completed an online survey through Qualtrics which took approxi- mately one hour to complete. They were specifically told that the purpose of the study was to examine media and behavior and that they must have played video games to participate. All participants gave implied consent and all procedures and materials were approved by both universities internal review boards.

L. Stockdale, S.M. Coyne Journal of Affective Disorders 225 (2018) 265–272

266

2.3. Measures

2.3.1. Video game addiction Video game addiction was measured using the Internet Gaming

Disorder Scale (IGD; Lemmens et al., 2015). The IGD is a nine question self-report measure of addiction that covers criteria described in the DSM-V, including preoccupation, tolerance, withdrawal, persistence, escape, deception, displacement, and conflict regarding video game use. Participants answer yes or no to the nine questions in regards to their gaming behavior in the last 12 months. Participants who respond yes to five or more items are classified as addicts. Participants who played video games, but responded yes on two or fewer questions were used as controls. Previous research has shown the IGD to be a reliable and valid measure of video game addiction (Lemmens et al., 2015, α = .93). Example items include "In the last 12 months have you hidden the time you spend on games from others?" and "In the last 12 months have you played games so that you would not have to think about annoying things?"

2.3.2. Attention-deficit/Hyperactivity Attention-deficit/Hyperactivity (ADHD) symptoms were measured

using the World Health Organization Adult ADHD Self-Report Scale (ASRS; Kessler et al., 2004). The ASRS is an eighteen-item measure of ADHD symptoms that can be administered verbally or through survey. Participants select how frequently statements are true for them on a five-point scale from 1 = never to 5 = very often. Higher scores are indicative of more symptoms associated with ADHD. Previous re- searchers have shown the ASRS to be a reliable and valid measure of ADHD symptoms for the general population (Kessler et al., 2007; α = .72). Example items include "How often do you have trouble wrapping up the fine details of a project, once the challenging parts have been done?" and "How often do you find yourself talking too much when you are in a social situation?"

2.3.3. Cognitive functioning Cognitive functioning was measured using the Neuro-QOL (quality

of life) Cognitive Functioning short form (Cella et al., 2012). The Neuro-QOL was developed by the National Institute of Neurological Disorders and Stroke and has been shown to be reliable for clinical populations and the general public in order to assess multiple domains of quality of life (α = .85–.96, Cella et al., 2012). The cognitive functioning subscale contains eight questions and participants answer four questions on a five-point scale of 1 = never to 5 = very often (several times a day) and four questions on a scale of 1 = none to 4 = cannot do. Higher scores are indicative of more cognitive difficulties. Example items include "In the past 7 days I had to read something several times to understand it" and " How much difficulty do you cur- rently have reading and following complex instructions (e.g., directions for a new medication)?"

2.3.4. Mental health Mental health was measured using the mental health subscale of the

PROMIS Global Health Scale (Hayes et al., 2009). The mental health subscale includes four items and range on a five-point scale from 1 = excellent to 5 = poor, with higher scores being indicative of poorer mental health. Example items include "In general, how would you rate your mental health, including your mood and ability to think?" and "In general, please rate how well you carry out your usual social activities and roles (This includes activities at home, at work, and in your com- munity, and responsibilities as a parent, child, spouse, employee, friend, etc.)". The mental health subscale of the PROMIS Global Health scale has been shown to be a reliable and valid measure of mental health (α = .86, Hayes et al., 2009).

2.3.5. Physical health Physical health was measured using the physical health subscale of

the PROMIS Global Health scale (Hayes et al., 2009). PROMIS is a system of questionnaires developed by the U.S. Department of Health and Human Services intended to monitor and evaluate physical, social, and emotional health in adults and children and can be used with the general population and individuals living with chronic conditions. The physical health subscale includes four items and range on a five-point scale from 1 = excellent to 5 = poor, with higher scores being in- dicative of poorer physical health. Example items include "In general how would you rate your physical health?" and How would you rate your fatigue on average (scale none to very severe)?" The physical health subscale of the PROMIS Global Health scale has been shown to be a reliable measure of physical health (α = .81, Hayes et al., 2009).

2.3.6. Somatic disturbance Somatic disturbances were measured using the Neuro-QOL Sleep

Disturbances-Short Form. This eight-item measure is designed to mea- sure difficulty sleeping in the general population and as a result of neurological disorders or conditions. Participants answer on a five- point scale from 1 = never to 5 = always, with higher scores being indicative of greater somatic difficulties. Example items include "In the past 7 days I had to force myself to get up in the morning" and "In the past 7 days I had trouble falling asleep." This measure has been shown to be a reliable and valid measure of somatic difficulties (α= .93, Perez et al., 2007).

2.3.7. Body Mass Index (BMI) BMI was measured as a proxy of physical health. Participants re-

ported their height in feet and inches and their weight in pounds. The National Heart, Lung, and Blood Institute's online BMI calculator was used to computer each participants BMI (http://www.nhlbi.nih.gov/ health/educational/lose_wt/BMI/bmicalc.htm).

2.3.8. Anxiety Anxiety was measured using the PROMIS Emotional Distress-

Anxiety-Short Form 8a. This scale is an eight-item scale designed to measure anxiety in the general population and in clinical samples. Participants answer on a five-point scale of 1 = never to 5 = always, with higher scores being indicative of increased anxiety. Example items include "In the past 7 days I have felt uneasy" and "In the past 7 days I have felt tense." The PROMIS Anxiety Short Form 8a has been shown to be a reliable and valid measure of anxiety (α =.95, Pilkonis et al., 2011).

2.3.9. Depression Depression was measured using the PROMIS Emotional Distress-

Depression-Short Form 8a. This scale is an eight-item scale designed to measure depression in the general population and in clinical sample. Participants answer on a five-point scale of 1 = never to 5 = always with higher scores being indicative of increased depressive symptoms. Example items include "In the past 7 days I have felt depressed" and "In the past 7 days I felt I had no reason for living." This measure has been shown to be a reliable and valid measure of depressive symptoms (.93, Pilkonis et al., 2011).

2.3.10. Positive affect and well-being Positive affect and well-being were measured using the Neuro-QOL

Positive Affect and Well-Being-Short Form (Salsman et al., 2013). This nine-item measure takes into account feelings of hope, worth, happi- ness, and satisfaction in general and with life. Participants respond on a five-point scale from 1 = never to 5 = always with higher scores being indicative of more positive affect and greater well-being. Example items include "Lately I felt hopeful" and "Lately my life had purpose." This measure has been shown to be a reliable and valid measure of positive affect and well-being (α = .94, Salsman et al., 2013).

L. Stockdale, S.M. Coyne Journal of Affective Disorders 225 (2018) 265–272

267

2.3.11. Aggression Aggression was measured using the Short-Form Buss Perry

Aggression Questionnaire (BPAQ-SF; Buss and Perry, 1992). This twelve-item measure takes into account physical and verbal aggression, anger, and hostility. Participants respond on a seven-point scale of 1 = extremely uncharacteristic of me to 7 = extremely characteristic of me with higher scores being indicative of more aggression. Example items include "There are people who have pushed me so far that we have come to blows" and "Sometimes I fly off the handle for no good reason." This short-form has been shown to be a reliable and valid measure of aggression (α = .83, Diamond and Magaletta, 2006).

2.3.12. Hypermasculinity Hyper-masculinity was measured using the Hypermasculinity

Inventory-Revised (HMI-R, Peters et al., 2007). This 27-item measure assesses over subscription to cultural norms and values that emphasize masculinity and "machismoism" while diminishing norms associated with compassion, kindness, gentleness, and meekness. Participants an- swer questions on a ten-point scale (scale items changed from statement to statement, e.g. for the statement "After I have gone through a really dangerous experience" 1 = my knees feel weak and a shake all over and 10 = I feel high and for the statement "Call me a name" 1 = I'll pretend not to hear you and 10 = I'll call you another with higher scores being indicative of higher hyper-masculinity. Example items include "After I have gone through a really dangerous experience my knees feel weak and I shake all over (or I feel high)" and "I like dependable cars and faithful lovers (or fast cars and fast lovers)." The HMI-R has been shown to be a reliable and valid measure of hyper-masculinity (α= .90, Peters et al., 2007).

2.3.13. Companionship Companionship was measured using the PROMIS Companionship-

Short Form. This four-item scale has participants answer on a five-point scale of 1 = never to 5 = always with higher scores being indicative of more feelings of companionship. Example items include "Do you have someone with whom to have fun?" and "Can you find companionship when you want it?" This has been shown to be a reliable and valid measure of companionship and social health (Tucker et al., 2014).

2.3.14. Emotional support Emotional support was measured using the PROMIS Emotional

Support-Short Form 4a scale. This four-item scale has participants an- swer on a five-point scale of 1 = never to 4 = always with higher scores being indicative of increased emotional support. Example items include "I have someone who will listen to me when I need to talk" and "I have someone to talk with when I have a bad day." This measure has been shown to be a valid and reliable measure of emotional support in the general population and clinical populations (Tucker et al., 2014).

2.3.15. Social isolation Social isolation was measured using the PROMIS Social Isolation

Short Form 4a. This four-item scale has participants answer on a five- point scale of 1 = never to 5 = always with higher scores being in- dicative of increased social isolation. Example items include "I feel left out" and "I feel isolated from others." This measure has been shown to be a valid and reliable measure of social isolation (Tucker et al., 2014).

2.3.16. Tobacco use Tobacco use was measured using a self-report version of the Adult

Tobacco Survey (ATS; King et al., 2012a) problem tobacco use section. Participants answered fourteen yes or no questions regarding their to- bacco use with higher scores being indicative of greater difficulty be- cause of tobacco. Example items include "Did you ever have times when you smoked even though you promised yourself you wouldn't?" and "Did tobacco ever cause you any physical problems like coughing, difficulty breathing, lung trouble, or problems with your heart or blood

pressure?"

2.3.17. Drug use Drug use was measured using the Drug Abuse Screening Test (DAST-

10; Skinner, 1982), developed by a cross-cultural collaboration by the World Health Organization. This is a ten-item screening tool that can be self-administered. Participants respond yes or no regarding their drug use in the past 12 months (not including tobacco and alcohol use). Higher scores are indicative or more problematic drug use. Example items include "Are you always able to stop using drugs when you want to?" and "Have you engaged in illegal activities in order to obtain drugs?"

2.3.18. Alcohol use Alcohol use was measured using the self-report version of the

Alcohol Use Disorders Identification Test (AUDIT, Saunders et al., 1993) a measure developed through the World Health Organization. The AUDIT is a ten-question assessment of problematic alcohol use. Example items include "How often do you have six or more drinks on one occasion?" and "Have you or someone else been injured as a result of you drinking?" Higher scores on the AUDIT are indicative of more problematic alcohol use.

2.3.19. Pornography Use Pornography use was measured using the Cyber Pornography Use

Inventory-9 (CPUI; Grubbs et al., 2010). The CPUI is a nine-item questionnaire used to assess problematic internet pornography use. Participants answer on a dichotomous yes/no scale with higher scores being indicative of more problematic internet pornography use. Ex- ample items include "Even when I do not want to use pornography online, I feel drawn to it" and "I have put off important priorities to view pornography." The CPUI has been shown to be a reliable and valid measure of problematic internet pornography use in religious and nonreligious populations (α = .83, Grubbs et al., 2010).

2.3.20. Gambling Pathological gambling was assessed using an adapted version of the

DSM IV Checklist for addiction (Kessler et al., 2008). Participants an- swer ten questions on a dichotomous scale regarding their gambling behavior in the last 12 months. Higher scores are indicative of greater difficulties. Example items include "Do you rely on others to provide money to relieve a desperate financial situation caused by gambling?" and "Do you need to gamble with increasing amounts of money in order to achieve the desired excitement?"

3. Results

In order to compare IGD video game addicts to matched healthy controls in terms of physical, emotional, social, mental health, and comorbidity of substance use and internet addictions, and take into account potential gender differences and interactions, a series of 2 × 2MANOVAs were conducted in SPSS version 22. The means and stan- dard deviations and the video game addiction main effects for the MANOVAs for each major variable are reported in Table 1 and Fig. 1. Gender interactions are only noted and explored when significant. Main effects for gender are not reported here but can be obtained by con- tacting the first author.

3.1. Mental health

A one-way MANOVA was conducted to examine the effect of video game addiction status on ADHD symptoms, cognitive functioning, and global measures of mental health. The overall MANOVA for video game status was significant (Wilk's Λ = .83, F (3, 204) = 13.60, p< .001, η2p = .17). Follow-up ANOVAs revealed that video game addicts ex- perienced greater ADHD symptoms (F (1, 209) = 17.11, p< .001, η2p

L. Stockdale, S.M. Coyne Journal of Affective Disorders 225 (2018) 265–272

268

= .08), poorer cognitive functioning (F (1, 209) = 37.46, p< .001, η2p = .15), and scored worse on global measures mental health (F (1, 209) = 20.63, p< .001, η2p = .09). Together these results suggest that video game addicts display poorer cognitive functioning and mental health compared to non-addicts.

3.2. Physical health

A one-way MANOVA was conducted to examine the effect of video game addiction status on physical health, BMI, and somatic difficulties. The overall MANOVA for video game status was significant (Wilk's Λ= .80, F (3, 203) = 16.91, p< .001, η2p = .20). Follow-up ANOVAs revealed that video game addicts did not differ from non-addicts in their physical health (F (1, 209) = 1.85, p = .18, η2p = .009), or BMI (F (1, 209) = .39, p = .53, η2p = .002), but addicts did display sig- nificantly more somatic difficulties (F (1, 209) = 51.06, p< ; .001, η2p = .20).

The gender X addiction status interaction was significant (Wilk's Λ = .94, F (3, 203) = 4.46, p = .005, η2p = .06). A series of 2 × 2 ANOVA was conducted to examine the effects of video game addiction and gender on physical health (see Table 1). There was no difference between addicts and non-addicts on a global measure of physical health (F (1, 209) = 4.15, p = .14, η2p = .01) or BMI (F (1, 209) = .01, p = .91, η2p< .001). However the interaction between gender and video game addiction status was significant for somatic difficulties (F (1, 209) = 9.32, p = .003, η2p = .04). Probing post-hoc independent samples t- tests were conducted to examine the direction of the interaction. A t-test examining the effect of gender on somatic difficulties for addicts re- vealed that female addicts displayed significantly more somatic diffi- culties than male addicts (t (55.93) = − 4.81, p< .001, d = 1.03; addict male mean = 18.81 SD = 4.49; addict female mean = 23.88 SD = 5.33). A t-test examining gender differences in somatic difficulties in non-addicts revealed no significant differences in somatic difficulties between males and females (t (49.25) = − .47, p = .64, d = .15; male mean = 16.00 SD = 4.16 female mean = 16.53 SD = 5.86).

Taken together these results suggest that video game addicts did not report poorer physical health than non-addicts, but that female addicts may be uniquely at risk for negative physical health outcomes and sleep disturbances.

3.3. Emotional health

A one-way MANOVA was conducted to examine the effect of video game addict status on anxiety, depression, aggression, and positive af- fect and well-being. The overall MANOVA for video game status was significant (Wilk's Λ = .81, F (4, 201) = 12.18, p< .001, η2p = .20).

Table 1 Means, standard deviations, and ANOVA results for comparisons between addicts and non-addicts.

Addicts Non-Addicts

M SD M SD F df Effect size

Mental Health ADHD symptoms*** 6.07 3.21 3.97 2.73 17.11 1, 206 .08 Cognitive

functioning*** 21.30 5.42 16.98 4.64 39.39 1, 208 .15

Global measure of mental health*

10.81 3.24 8.70 2.98 4.01 1, 208 .09

Physical Health Global measure of

physical health 8.25 1.60 7.95 1.47 2.74 1, 205 .009

BMI 23.90 4.10 23.56 3.64 .39 1, 205 .002 Somatic

difficulties*** 20.39 5.30 16.12 4.75 52.36 1, 208 .20

Emotional Health Anxiety*** 21.18 6.89 16.29 5.63 35.71 1, 207 .15 Depression*** 17.58 7.00 12.76 5.26 36.96 1, 207 .15 Aggression*** 3.22 .86 2.75 .82 17.50 1, 207 .08 Positive affect and

well-being*** 33.79 5.90 37.60 5.94 22.03 1, 207 .10

Social Health Companionship 15.62 3.31 16.43 3.43 2.57 1, 207 < .001 Emotional support 16.69 3.71 17.30 3.11 2.37 1207 .003 Social isolation*** 11.44 3.48 9.76 3.02 13.25 1208 .003 Hypermasculinity Hypermasculinity 4.23 1.05 4.15 1.04 1.64 1, 210 .008 Comorbidity Pornography

addiction* 4.50 2.37 2.81 2.17 5.84 1,70 .08

Note: ** p<.01. * p< .05. *** p< .001.

Fig. 1. Means and standard deviations for addicts and non-addicts. Note: * p< .05; ** p<.01; ***p< .001 for differences between addicts and non-addicts.

L. Stockdale, S.M. Coyne Journal of Affective Disorders 225 (2018) 265–272

269

Follow-up ANOVAs revealed that video game addicts displayed in- creased anxiety (F (1, 207) = 35.71, p< .001, η2p = .15), depression (F (1, 207) = 36.96, p< ;.001, η2p = .15), increased aggression (F (1, 207) = 17.50, p< .001, η2p = .08), and decreased positive affect and well-being (F (1, 207) = 22.03, p< .001, η2p = .10).

Taken together these results suggest that video game addicts report poorer emotional health than non-addicts.

3.4. Social health

A one-way MANOVA was conducted to examine the effect of video game addict status on feelings of social isolation, companionship, and emotional support. The overall MANOVA for video game status was significant (Wilk's Λ = .95, F (3, 205) = 4.77, p = .003, η2p = .07). Follow-up ANOVAs revealed that video game addicts did not differ from non-addicts in terms of feelings of companionship (F (1, 210) = 2.57, p = .11, η2p = .01) and emotional support (F (1, 210) = 2.37, p = .13, η2p = .01), but addicts did report feeling significantly more socially isolated (F (1, 210) = 14.08, p< .001, η2p = .06).

3.5. Hypermasculinity

An ANOVA was conducted to examine the effect of video game addict status and gender on hypermasculinity. The main effect of video game addiction (F (1, 210) = 1.64, p = .20, η2p = .008, or gender was not significant (F (1, 210) = .33, p = .56, η2p = .002, but there was a significant interaction (F (1, 210) = 4.26, p = .04, η2p = .02). Probing post-hoc independent samples t-tests were conducted to examine the direction of the interaction. For non-addicts, males were significantly more hypermasculine than females (t (80.90) = 2.08, p = .04, d = .42; male mean = 4.27 SD = 1.10, female mean = 3.87 SD = .83). For addicts, there was no significant difference between males and females in terms of hypermasculinity (t (62.67) = −1.03, p = .31, d = .22; male mean = 4.15 SD = 1.04, female mean = 4.38 SD = 1.08). These results suggest that female addicts have hypermasculinity scores similar to male addicts and nonaddicts.

3.6. Video game addiction and comorbidity with other addictions

Finally, we examined the comorbidity of video game addiction with other substance use and behavioral addiction. Due to the universities surveyed there were low rates of tobacco, drug, alcohol, and gambling behaviors in addicts and non-addicts. Thus, only pornography use was examined. Video game addicts showed higher levels of problematic pornography use (F (1, 70) = 5.84, p = .02, η2p = .08) and was no interactions with gender.

4. Discussion

In line with previous research, approximately 7% of the young adults who played video games met the IGD criteria for video game addiction (Gentile, 2009), with males being more likely to be video game addicts than females (King et al., 2012b; Young, 1998). In gen- eral, video game addicts reported poorer mental, physical, and emo- tional heath and being a female video game addict placed individuals at particular risk for certain negative outcomes. Video game addiction was also comorbid with problematic internet pornography use.

4.1. Mental health

Video game addicts, regardless of gender, displayed increased ADHD symptoms, poorer overall cognitive functioning, and poorer mental health. Previous researchers have shown that poor impulse control (Li et al., 2009) and cognitive functioning (Bickel et al., 2014) are risk factors for addiction. It is argued that poor cognitive control and impulsivity make it more difficult for individuals to disengage from

rewarding and arousing stimuli, making individuals more likely to continuously seek and abuse such stimuli. The current study lends support for these findings by showing poor impulse control and cog- nitive functioning in video game addicts. This finding also adds support to IGD being used as a measure of video game addiction and that video game addiction may have similar risk factors to other addictions. However, it is impossible to tell from the current data the direction on effects. It is possible that video game addiction leads to biological changes in the neural network that weaken cognitive functioning and impulse control. Future researchers should examine the development of video game addiction and how this relates to cognitive functioning and overall mental health. The little work that has been done suggests that poor impulse control and executive functioning are risk factors for the development of video game addiction (Ko et al., 2013).

4.2. Physical health

In general, being a video game addict was not related to poorer physical health. Video game addicts were no different than controls in terms of their body mass index or self-report measures of physical health. Video game players are frequently portrayed in the media as over-weight, socially isolated, individuals who play games alone in a darkened room (Sukkau, 2012). However, the current study does not support this pervasive stereotype. Video game addicts appear to be physically healthy and as physically active as their non-addict peers. Dispelling this stereotype is important as it might help draw attention to the reality of video game addiction. It may be more difficult for video game addicts to recognize their own pathology when they do not see themselves in the same light as a stereotypical video game addict (Koordeman et al., 2010). Changing this stereotype may make it easier for video game addicts to recognize their own pathology.

Video game addiction was related to increased somatic difficulties. Previous studies have reported increased sleep disturbances and diffi- culties in pathological gamers (Gentile et al., 2011). It is likely that video game play is directly interfering with sleep and sleep patterns in this population. Adding to previous research, this study identified that female video game addicts were at particular risk for somatic difficul- ties. Males are significantly more likely to be video game addicts than females and it is possible that female video game addicts feel more shame and need to hide their behavior than males. Previous research has shown that it is more socially acceptable for males to play video games and this social acceptance makes male video game play more public (Lucas and Sherry, 2004). It is possible that female video game addicts feel a greater need to hide their behavior and as a result spend more time playing during nighttime hours. Conversely, it is theoreti- cally possible that females with somatic difficulties are more likely to seek video games as a way to deal with their sleep disturbances. Future researchers should look at these relationships longitudinally.

4.3. Emotional health

Video game addicts consistently displayed poorer emotional health than non-addicts. Video game addicts reported more anxiety, depres- sion, aggression, and lower positive affect and well-being. Previous researchers have repeatedly shown addictions are related to poorer emotional health and in particular depression and anxiety are fre- quently comorbid in addiction (Bruchas et al., 2011). Addiction has also been associated with poorer life-satisfaction and overall measures of well-being (Murphy et al., 2005). This pattern in video game addiction further supports the validity of the IGDS as a measure of video game addiction and pathological video game use, and adds to the growing literature that pathological video game use is a small, but real per- centage of video game players. Video game addiction in particular has been associated with increased aggression in previous research (Gentile, 2009; Gentile et al., 2011). The results of this study replicate these previous results.

L. Stockdale, S.M. Coyne Journal of Affective Disorders 225 (2018) 265–272

270

4.4. Hypermasculinity

Furthermore, female video game addicts displayed hyper-masculine tendencies and gender atypical behavior. It is possible that gender stereotypical females are less likely to play video games. However, it is also possible that frequently playing video games changes scripts and schemes regarding gender for females. Previous research has shown that video games frequently portray women as sexualized and ag- gressive (Dill and Thill, 2007) and that playing video games with sex- ualized females lead to decreased self-efficacy in women (Behm- Morawitz and Mastro, 2009). Similarly, research has shown that women may experience greater increases in aggressive behavior after playing video games than men (Eastin, 2006). Therefore, it is possible that repeatedly playing video games leads women to be more ag- gressive and display more hyper-masculinity. It is important to note that very little research has been conducted specifically examining fe- male gamers and female video game addicts. Future research should address this limitation.

4.5. Social health

Video game addicts displayed mixed results regarding social health and functioning. Addicts did not report any difference in social support or feelings of companionship compared to non-addicts. Addicts feel that they have people to turn to when they need it and that they have friends and a support system. However, it is unclear from these measures if these social relationships are online with other video game addicts, or face-to-face relationships. Future research should specifically examine friendships and social support systems in video game addicts. Conversely, addicts did report feeling more isolated than non-addicts. This is supported by past research that has shown that social isolation is a risk factor for the development of addiction (Lovic et al., 2011) Likewise, internet addiction has been associated with increased social isolation and feelings of loneliness (Yao and Zhong, 2014). Video game addiction may follow similar patterns to internet addiction, where ad- dicts report many friends and online relationships, but these relation- ships cannot replace face-to-face contacts in terms of reducing feelings of loneliness and isolation. Taken together these results suggest that video game addicts may be at risk for some markers of poorer social health and functioning compared to non-addicts who play video games.

4.6. Comorbidity of addiction

Most the sample did not report using drugs, tobacco, alcohol, or gambling. Thus, comorbidity could not be assessed in this sample. However, video game addicts did show increased symptoms of pro- blematic internet pornography use compared to non-addicts. There is a vast body of literature that suggests that addicts of one type are at in- creased risk for developing addictions in other areas (Bruchas et al., 2011). The results of the current study lend support to this notion. It is likely that addiction is a result of underlying cognitive, biological, psychological, social, and emotional difficulties that place people at increased risk for addiction. These difficulties, such as poor impulse control, seem to be the same in video game addiction.

The present study contributes to the growing body of literature on video game addiction by using measures of health outcomes re- commended by leading health organizations and by matching addicts to age, gender, race-ethnicity, and marital status controls. Most past re- search on video game addiction has compared addicts to the greater population, thus potentially confounding health outcomes and falsely attributing them to video game addiction. The current study addresses this limitation in past research and clearly identifies video game ad- diction as a significant risk factor for poorer emotional, physical, mental, and social health. The disparities were found using measures recommended by leading health organizations.

4.7. Limitations

While the current study adds vital information to the growing body of literature regarding video game addiction, it is not without limita- tions. The current study employed self-report measures of all behavior and was cross-sectional. Individuals may not be accurate reporters of their own behavior and self-report measures may lead to biased re- porting. However, it should be noted that people typically underreport negative behaviors so it is possible that using other measured would lead to increased effects. The cross-sectional nature of the current study does not allow for longitudinal examinations and does not allow for examining causation. Future researchers should examine video game addiction longitudinally to better understand risk and protective factors to the development of video game addiction. Perhaps the biggest lim- itation of the current study is the use of undergraduate college students. While college students are known to spend a significant amount of time playing video games (Stockdale et al., 2015) and may be at increased risk for developing pathological video game play (King et al., 2012b; Young, 1998), this population is educated and relatively functional and may have better overall health. Future researchers should examine video game addiction in a non-college population.

4.8. Conclusions

Even with the above noted limitations the present study lends fur- ther support to IGDS as a valid measure of pathological video game use and potentially as a measure of video game addiction. Significant re- search attention needs to be given to video game addiction to improve understanding of risk factors to development, appropriate treatment and intervention strategies, and biological effects of video game ad- diction. Interest needs to be invested in female video game addicts to understand the unique risk factors and treatment options for female addicts.

Limitations

The current study employs self-report, cross-sectional data with undergraduate college students.

Acknowledgement

We would like to that the Loyola University Office of the Provost for funding this research.

References

Behm-Morawitz, E., Mastro, D., 2009. The effects of the sexualization of female video game characters on gender stereotyping and female self-concept. Sex Roles 61, 808–823. http://dx.doi.org/10.1007/s11199-009-9683-8.

Bickel, W.K., Jamolowicz, D.P., Mueller, E.T., Gatchalian, K.M., McClure, S.M., 2014. Are executive function and impulsivity antipodes? A conceptual reconstruction with special reference to addiction. Psychopharmacology 221, 361–387. http://dx.doi. org/10.1007/s00213-012-2689-x.

Bruchas, M.R., Schindler, A.G., Shankar, H., Messinger, D.I., Miyatake, M., Land, B.B., Chavkin, C., 2011. Selective p38α MAPK deletion in serotoergic neurons produces stress resilience in models of depression and addiction. Neuron 71, 498–511. http:// dx.doi.org/10.1016/j.neuron.2011.06.011.

Brunborg, G.S., Mentzoni, R.A., Frøyland, L.R., 2014. Is video gaming, or video game addiction, associated with depression, academic achievement, heavy episodic drinking, or conduct problems? J. Behav. Addict. 3, 27–32. http://dx.doi.org/10. 1556/JBA.3.2014.002.

Buss, A.H., Perry, M.P., 1992. The aggression questionnaire. J. Personal. Soc. Psychol. 63, 452–459. http://dx.doi.org/10.1037/0022-3514.63.3.452.

Carroll, J.F., Chiapa, A.L., Rodriquez, M., Phelps, D.R., Cardarelli, K.M., Vishwanatha, J.K., Bae, S., Cardarelli, R., 2008. Visceral fat, waist circumference, and BMI: impact of race/ethnicity. Obesity 16, 600–607. http://dx.doi.org/10.1038/oby.2007.92.

Cella, D., Lai, J.S., Nowinski, C.J., Victorson, D., Peterman, A., Miller, D., Moy, C., 2012. Neuro-QOL: brief measures of health related quality of life for clinical research in neurology. Neurology 78, 1860–1867. http://dx.doi.org/10.1212/WNL. 0b013e318258f744.

Dani, J.A., Harris, R.A., 2005. Nicotine addiction and comorbidity with alcohol abuse and

L. Stockdale, S.M. Coyne Journal of Affective Disorders 225 (2018) 265–272

271

mental illness. Nat. Neurosci. 8, 1465–1470. http://dx.doi.org/10.1038/nn1580. Diamond, P.M., Magaletta, P.R., 2006. The short-form Buss-Perry Aggression

Questionnaire (BPAQ-SF): a validation study. Assessment 13, 227–240. http://dx.doi. org/10.1177/1073191106287666.

Dill, K.E., Thill, K.P., 2007. Video game characters and the socialization of gender roles: young people's perceptions mirror sexist media depictions. Sex Roles 57, 851–864. http://dx.doi.org/10.1007/s11199-1007.

Dong, G., Lu, Q., Zhou, H., Zhao, X., 2010. Impulsive inhibition in people with internet addiction disorder: electrophysiological evidence from a Go/NoGo study. Neurosci. Lett. 485, 138–142. http://dx.doi.org/10.1016/j.neulet.2010.09.002.

Dong, G., Shen, Y., Huang, J., Du, X., 2013. Impaired error-monitoring function in people with internet addiction disorder: an event-related fMRI study. Addict. Res. 19, 269–275. http://dx.doi.org/10.1159/000346783.

Eastin, M.S., 2006. Video game violence and the female game player: self-and opponent gender effects on presence and aggressive thoughts. Hum. Commun. Res. 32, 351–372. http://dx.doi.org/10.1111/j.1468-2958.2006.00279.x.

Gentile, D., 2009. Pathological video-game use among youth ages 8 to 18: a national study. Psychol. Sci. 594, 594–602. http://dx.doi.org/10.1111/j.1467-9280.2009. 02340.x.

Gentile, D.A., Choo, H., Liau, A., Sim, T., Li, D., Fung, D., Khoo, A., 2011. Pathological video game use among youths: a two-year longitudinal study. Pediatrics 127, 319–329. http://dx.doi.org/10.1542/peds.2010-1353.

Gentile, D.A., Gentile, J.R., 2005. Violent Video Games And Exemplary Teachers. Griffiths, M.D., Kuss, D.J., King, D.L., 2012. Video game addiction: past, present, and

future. Curr. Psychiatry Rev. 8, 308–318. http://dx.doi.org/10.2174/ 157340012803520414.

Griffiths, M.D., Meredith, A., 2009. Videogame addiction and its treatment. J. Contemp. Psychother. http://dx.doi.org/10.1007/s10879-009-9118-4.

Grubbs, J.B., Sessoms, J., Wheeler, D.M., Volk, F., 2010. The cyber-pornography use in- ventory: the development of a new assessment instrument. Sex. Addict. Compuls. 17, 106–126. http://dx.doi.org/10.1080/10720161003776166.

Hayes, R.D., Bjomer, J.B., Revicki, D.A., Spritzer, K.L., Cella, D., 2009. Development of physical and mental health summary scores for the patient-report outcome mea- surement information system (PROMIS) global items. Quality Life Res. 18, 873–880. http://dx.doi.org/10.1007/s11136-1009.

Hellman, M., Schoenmakers, T.M., Nordstrom, B.R., van Holst, R.J., 2013. Is there such a thing as online video game addiction? A cross-disciplinary review. Addicti. Res. Theory 21, 102–112. http://dx.doi.org/10.3109/16066359.2012.693222.

Inaba, A., Thoits, P.A., Ueno, K., Grove, W.R., Evenson, R.J., Sloan, M., 2005. Depression in the United States and Japan: gender, marital status, and SES patterns. Soc. Sci. Med. 61, 2280–2292. http://dx.doi.org/10.1016/j.socscimed.2005.07.014.

Kessler, R.C., Adler, L., Ames, M., Demler, O., Faraone, S., Hiripi, E., Walters, E.E., 2004. The World Health Organization adult ADHD self-report scale (ASRS): a short screening scale for use in the general population. Psychol. Med. 35, 245–256. http:// dx.doi.org/10.1017/S0033291704002892.

Kessler, R.C., Adler, L., Gruber, M.J., Sarawate, C.A., Spencer, T., Van Brunt, D.L., 2007. Validity of the World Health Organization adult ADHD self-report scale (ASRS) screener in a representative sample of health plan members. Int. J. Methods Psychiatr. Res. 16, 52–65. http://dx.doi.org/10.1002/mpr.208.

Kessler, R.C., Hwang, I., LaBrie, R., Petukhova, M., Sampson, N.A., Winters, K.C., Shaffer, H.J., 2008. The prevalence and correlates of DSM-IV pathological gambling in the National Comorbidity Survey Replication. Psychol. Med. 38, 1351–1360. http://dx. doi.org/10.1017/S0033291708002900.

King, B.A., Dube, S.R., Tynan, M.A., 2012. Current tobacco use among adults in the United States: findings from the National Adult Tobacco Survey. Am. J. Public Health 102, 93–100. http://dx.doi.org/10.2105/AJPH.2012.301002.

King, D.L., Delfabbro, P.H., Griffiths, M.D., 2012. Clinical interventions for technology- based problems: excessive internet and video game use. J. Cogn. Psychother. 26, 43–56. http://dx.doi.org/10.1891/0889-8391.26.1.43.

King, D., Delfabbro, P., Griffiths, M., 2010. Video game structural characteristics: a new psychological taxonomy. Int. J. Ment. Health.: Addict. 8, 90–106. http://dx.doi.org/ 10.1007/s11469-009-9206-4.

Ko, C., Lui, G., Yen, J., Chen, C., Yen, C., Chen, C., 2013. Brain correlates of craving for online gaming under cue exposure in subjects with internet gaming addiction and in remitted subjects. Addict. Biol. 18, 559–569. http://dx.doi.org/10.1111/j.1369- 1600.2011.00405. x.

Koordeman, R., Anschutz, D.J., van Baaren, R.B., Engels, R.C.M.E., 2010. Effects of al- cohol portrayals in movies on actual alcohol consumption: an observational experi- mental study. Addiction 106, 547–554. http://dx.doi.org/10.1111/j.1360-0443. 2010.03224.x.

Lemmens, J.S., Valkenburg, P.M., Gentile, D.A., 2015. The internet gaming disorder scale. Psychol. Assess. 27, 567–582. http://dx.doi.org/10.1037/pas0000062.

Lemmens, J.S., Valkenburg, P.M., Peter, J., 2009. Development and validation of a game addiction scale for adolescents. Media Psychol. 12, 77–95. http://dx.doi.org/10. 1080/15213260802669458.

Li, C.R., Lou, X., Yan, P., Bergquist, K., Sinha, R., 2009. Altered impulse control in alcohol dependence: neural measures of stop signal performance. Alcohol Clin. Exp. Res. 33, 740–750. http://dx.doi.org/10.1111/j.1530-0277.2008.00891.x.

Lorains, F.K., Cowlishaw, S., Thomas, S.A., 2011. Prevalence of comorbid disorders in problem and pathological gambling: systematic review and meta-analysis of popu- lation surveys. Addiction 106, 490–498. http://dx.doi.org/10.1111/j.1360-0443. 2010.03300.x.

Lovic, V., Kee, D., Fletcher, P.J., Fleming, A.S., 2011. Early-life maternal separation and social isolation produce an increase in impulsive action but not impulsive choice. Behav. Neurosci. 125, 481–491. http://dx.doi.org/10.1037/a0024367.

Lucas, K., Sherry, J.L., 2004. Sex differences in video game play: a communication-based explanation. Commun. Res. 31, 499–523. http://dx.doi.org/10.1177/ 0093650204267930.

McGrath, D.S., Barrett, S.P., 2009. The comorbidity of tobacco smoking and gambling: a review of the literature. Drug Alcohol Rev. 28, 676–681. http://dx.doi.org/10.1111/ j.1465-3362.2009.00097.x.

Mehroof, M., Griffiths, M.D., 2010. Online gaming addiction: the role of sensation seeking, self-control, neuroticism, aggression, state anxiety, and trait anxiety. Cyberpsychol. Behav. Soc. Netw. 13, 313–316 (doi: 10.1089=cyber.2009.0229).

Mentzoni, R.A., Brunborg, G.S., Molde, H., Myrseth, H., Mǻr Skouverøe, K.J., Hetland, J., Pallesen, S., 2011. Problematic video game use: estimated prevalence and associa- tions with mental and physical health. Cyber. Behav. Soc. Netw. 14, 591–596. http:// dx.doi.org/10.1089/cyber.2010.0260.

Murphy, J.G., McDevitt-Murphy, M.E., Barnett, N.P., 2005. Drink and be merry? Gender, life satisfaction, and alcohol consumption among college students. Psychol. Addict. Behav. 19, 184–191. http://dx.doi.org/10.1037/0893-164X.19.2.184.

Orford, J., 2001. Addiction as excessive appetite. Addiction 96, 15–31. http://dx.doi.org/ 10.1046/j.1360-0443.2001.961152.x.

Paeratakul, S., White, M.A., Williamson, D.A., Ryan, D.H., Bray, G.A., 2002. Sex, race/ ethnicity, socioeconomic status, and BMI in relation to self-perception of overweight. Obesity 10, 345–350. http://dx.doi.org/10.1038/oby.2002.48.

Perez, L., Huang, J., Jansky, L., Nowinski, C., Victorson, D., Peterman, A., Cella, D., 2007. Using focus groups to inform the Neuro-QOL measurement tool: exploring patient -centered, health-related quality of life concepts across neurological conditions. J. Neurosci. 39 (doi: 172597388).

Peters, J., Nason, C., Turner, W.M., 2007. Development and testing of a new version of the Hypermasculinity Index. Soc. Work Res. 31, 171–182. http://dx.doi.org/10.1093/ swr/31.3.171.

Pilkonis, P.A., Choi, S.W., Reise, S.P., Stover, A.M., Riley, W.T., Cella, D., 2011. Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS): depression, anxiety, and anger. Assessment 18, 263–283. http://dx.doi.org/10.1177/1073191111411667.

Rehbein, F., Baier, D., 2013. Family-, media-, and school-related risk factors of video game addiction: a 5-year longitudinal study. J. Media Psychol. 25, 118–128. http:// dx.doi.org/10.1027/1864-1105/a000093.

Rehbein, F., Kleimann, M., Mößle, T., 2010. Prevalence and risk factors of video game dependency in adolescence: results of a German nationwide survey. Cyberpsychol. Behav. Soc. Netw. 13, 1–9 (doi: 10.1089=cpb.2009.0227).

Salsman, J.M., Victorson, D., Choi, S.W., Peterman, A.H., Heinemann, A.W., Nowinski, C., Cella, D., 2013. Development and validation of the positive affect and well-being scale of the neurology quality of life (Neuro-QOL) measurement system. Qual. Life Res. 22. http://dx.doi.org/10.1007/s11136-013-0382-0.

Saunders, J.B., Aasland, O.G., Babor, T.F., De La Fuente, J.R., Grant, M., 1993. Development of the Alcohol Use Disorders Identification Test (AUDIT): who colla- borative project on early detection of persons with harmful alcohol consumption-II. Addiction 88, 791–804.

Skinner, H.A., 1982. The drug abuse screening test. Addict. Behav. 7, 363–371. Stein, D.J., Black, D.W., Shapira, N.A., Spitzer, R.L., 2001. Hypersexual disorder and

preoccupation with internet pornography. Psychiatry 158, 1590–1594. Stockdale, L., Morrison, R.M., Kmiecik, M., Garbarino, J., Silton, R.L., 2015. Emotionally

anesthetized: media violence induces neural changes during emotional face proces- sing. Soc. Cogn. Affect. Neurosci. http://dx.doi.org/10.1093/scan/nsv025.

Sukkau, S., 2012. Embarrassing and Dishonest: TV’s Portrayal of Gaming, March 16. Retrieved from: ⟨http://venturebeat.com/community/2012/03/16/embarrassing- and-dishonest-tvs-portrayal-of-gaming/⟩.

Tucker, C.A., Escoropizo, R., Cieza, A., Lai, J.S., Stucki, G., Ustun, T.B., Kostanjsek, N., Cella, D., Forrest, C.B., 2014. Mapping the content of the Patient Reported Outcomes Measurement Information System (PROMIS) using the international classification of functioning, health, and disability. Qual. Life Res. 23, 2431–2438. http://dx.doi.org/ 10.1007/s11136-014-0691-y.

Yao, M.Z., Zhong, Z., 2014. Loneliness, social contracts, and internet addiction: a cross- lagged panel study. Comput. Hum. Behav. 30, 164–170. http://dx.doi.org/10.1016/j. chb.2013.08.007.

Young, K., 1998. Caught in the Net. Wiley, Chichester.

L. Stockdale, S.M. Coyne Journal of Affective Disorders 225 (2018) 265–272

272