Addiction and Engagement: An Explorative Study Toward Classification Criteria for Internet Gaming Disorder

Abstract

The DSM-5 introduced Internet gaming disorder (IGD) as a condition needing more research. Proposed criteria include tolerance, preoccupation, deceiving, or continued excess despite psychosocial problems. However, studies suggest differences between addicted and engaged players. Therefore, this study investigated differences between engagement and addiction in a German-speaking sample of expert World of Warcraft players. Using an online-based questionnaire, 682 participants were surveyed (M_age=23.26 years; 84.9% male) from German-speaking areas. An adapted version of the “Asheron's call” questionnaire (which covers six addiction criteria, including salience, euphoria, and tolerance), the WHOQOL-BREF, the Gaming Motivation Scale, the BDI, the SPIN, and a brief version of the personality questionnaire BFI-10 were used. The average gamer in the sample played on level 87.93 and had been playing for 5.42 years. Addicted players had higher scores on the BDI and SPIN and significantly lower scores in all dimensions of quality of life. Addicted gamers played for 39.25 hours per week (engaged players: 11.93 hours per week) with significantly higher scores in items tapping achievement and immersion. There were differences regarding the BFI-10 in terms of “agreeableness,” “conscientiousness,” and “neuroticism.” The results suggest that factors such as achievement and immersion set engaged and addicted users apart. Addiction seems to be significantly more connected to other psychopathologies such as depression and social anxiety. The results suggest that euphoria, tolerance, and cognitive salience should be handled with caution when it comes to a classification of IGD similar to (behavioral) addiction.

Introduction

Millions of people play games online without any serious effects. However, a minority seems to have problems maintaining a healthy amount of gaming. Negative consequences caused by excessive gaming seem to be comparable to consequences caused by behavioral addiction.^1–4 As a result, the American Psychiatric Association introduced Internet gaming disorder (IGD) in the recent edition of the Diagnostic and Statistical Manual of Mental Disorders—5th Edition (DSM-5) as a condition that needs further research.^4,5 Petry and O'Brien³ state that more than 250 publications exist on IGD. However, strong empirical data are needed to include IGD in future versions of the DSM.⁶

The DSM-5 proposes nine criteria for the assessment of IGD: preoccupation, withdrawal, tolerance, unsuccessful attempts to control, loss of interests, continued excessive use despite psychosocial problems, deceiving, escape, and functional impairment.⁴ Unfortunately, most studies use inconsistent classification criteria to address the same phenomenon, such as criteria derived from “Pathological Gambling” or “Substance Use Dependence” of the DSM-IV.^7–13 Nevertheless, an excessive use of the Internet seems to be linked to a variety of comorbid psychopathological and personality differences.^14–17 For example, users suffering from depression tend to spend significantly more time online compared to nondepressed individuals.^18–21 Ceyhan and Ceyhan²² report that depression is a significant predictor of an excessive use of the Internet. Stetina et al.²³ compared different gaming genres and found that only users of massively multiplayer online role-playing games (MMORPGs) suffer significantly more often from depression.

In addition to depression, anxiety disorders (such as social anxiety disorder) seem to be another significant predictor of excessive or problematic Internet use; coping strategies involve the use of online games to modify negative feelings.^24–27 Floros et al.¹⁶ found a high comorbidity with other disorders of the DSM-IV-TR. They report positive correlations of an excessive use of the Internet with personality traits such as psychoticism, sensation seeking, and neuroticism, and negative correlations with personality traits of extraversion, conscientiousness, reward dependence, agreeableness, and self-directedness.^16,28

Another significant predictor seems to be the motivational factor of immersion. To examine gaming motivation factors, Yee²⁹ developed a gaming motivation scale. He found three factors explaining gaming motivations such as achievement (with the desire for advancement and gaining power, understanding the game mechanics), sociability (interested in helping and chatting with others, teamwork) and immersion (exploring and finding hidden things, customization of the character, and escaping from real life problems). Yee³⁰ and Caplan et al.³¹ found that immersion in particularly (with escapism, discovery, and role-playing) is a significant predictor of problematic Internet use. Similarly, Hsu et al.³² report that addicted players score higher in items related to immersion. Therefore, addiction seems to be highly correlated with items tapping immersion.^29–31,33

As one of the first scholars to investigate this topic, Griffiths^34,35 applied six criteria of a (behavioral) addiction to heavy use of the Internet, similar to the criteria proposed by the American Psychiatric Association.^5,36 These criteria include (a) salience (domination of a person's life by the activity); (b) euphoria (a “buzz” or a “high” is derived from the activity; (c) tolerance (the activity has to be undertaken to a progressively greater extent to achieve the same “buzz”); (d) withdrawal symptoms (cessation of the activity leads to the occurrence of unpleasant emotions or physical effects); (e) conflict (the activity leads to conflict with others or self-conflict); and (f) relapse and reinstatement (resumption of the activity with the same vigor subsequent to attempts to abstain).^37,38 Charlton³⁹ developed an addiction questionnaire with items tapping these six criteria. By conducting a factor analysis, he found two different factors in his samples of Internet users; an “addiction” factor loaded on items tapping core criteria of addiction (conflict, withdrawal symptoms, relapse and reinstatement, behavioral salience), while another factor labeled “engagement” loaded on items tapping peripheral criteria of addiction such as cognitive salience, tolerance, and euphoria. Charlton and Danforth confirmed these results in further studies.^37,38,40 Similarly, Lehenbauer-Baum and Fohringer³⁶ replicated these results in a sample of World of Warcraft (WoW) players. They suggest that criteria items tapping euphoria, tolerance, and cognitive salience are of limited use for a classification of IGD similar to (behavioral) addiction. Rather, they are expressions of a (nonproblematic) highly engaged gaming behavior. They suggest that engaged players should not be labeled as addicted. They advocate further research examining addiction and engagement, especially regarding classification criteria and comorbidities with other psychiatric disorders.

Therefore, the aim of the present explorative study was to examine psychopathological differences between addiction (with items tapping conflict, withdrawal symptoms, relapse and reinstatement, and behavioral salience) and high engagement (with items tapping cognitive salience, tolerance, and euphoria) in respect of gaming motivation, quality of life, and personality differences in a German sample of MMORPG (WoW) expert players (defined as gamers who play on a high level; the highest possible level at the time of the pilot study was level 90). The results contribute to recent discussions about diagnostic classification criteria of IGD.

Methods

Participants and study procedure

This study focused on experienced players of WoW (an MMORPG with the highest numbers of players). Participants were recruited in expert discussion forums by posting a link to the online-based questionnaire. All participants viewed a brief explanation of the study on the first page. The ethics committee of the main author's research institution approved the study. Furthermore, all participants were informed of their rights on the first Web page, according to the Ethical Principles for the treatment of participants from the American Psychological Association.⁴¹ A total of 3,895 users visited our Web site. Of these, 2,186 participants dropped out after reading the instructions before the start of the questions, 594 dropped out after the first questionnaire part with sociodemographic questions, and 379 participants dropped out during the other parts of our online questionnaire. Twenty-five participants were excluded because of their age (<18 years), and a further 18 were excluded because of missing or implausible data entries (e.g., an age of 99 years). The final sample size was therefore 682.

Measurements

Engagement and addiction: WoW questionnaire

To measure addiction and engagement, the questionnaire from Charlton and Danforth was used.³⁸ For the current sample of WoW players, the words “Asheron's Call” were changed to “World of Warcraft.” Charlton and Danforth³⁸ found two distinct factors by using principal axis factoring with direct oblimin (oblique) rotation. These results were confirmed in other studies.^37–40 Therefore, they recommend their questionnaire for measuring (a) “high engagement” with items tapping cognitive salience, tolerance, and euphoria (sample items include peripheral criteria of addiction; e.g., “I would hate to go without playing World of Warcraft for more than a few days,” “I rarely think about playing World of Warcraft when I am not using a computer,” or “It is important to me to be good at World of Warcraft”); and (b) “addiction” with items tapping conflict, withdrawal symptoms, behavioral salience, and relapse and reinstatement (with items tapping core criteria of addiction, e.g., “I often fail to get enough sleep because of playing World of Warcraft,” “I never miss meals because of playing World of Warcraft,” or “I have never used World of Warcraft as an escape from socializing”).^37–40 The German version by Lehenbauer-Baum and Fohringer was used in the present study.³⁴ Charlton and Danforth⁴⁰ reported Cronbach's alpha values of 0.79 and 0.80 for the addiction and engagement scale, respectively. In the present sample, Cronbach's alpha values for the addiction and engagement subscales were 0.86 and 0.79, respectively.

Psychopathological questionnaires

To assess depression, the German version of the well-known Beck Depression Inventory—Second Edition (BDI–II) was used.⁴² The BDI-II is a brief 21-item self-report measure that was completed by all participants. It is a widely used indicator of the severity of depression and is used in several online-based studies.⁴² In their study about problematic Internet use, Bozoglan et al.²¹ reported a Cronbach's alpha of 0.91. In the present sample, the Cronbach's alpha was 0.89.

Employing the Social Phobia Inventory (SPIN) by Connor et al.,⁴³ social anxiety disorder (SAD) was assessed. The SPIN is a widely used screening instrument for social fears. It is a 17-item instrument that uses a 5-point Likert-type rating scale (see http://psychology-tools.com/spin for the complete questionnaire).⁴⁴ Higher scores indicate a higher degree of social fear. In their online study, Lehenbauer et al.⁴⁵ found a Cronbach's alpha of 0.91. In the present sample, the Cronbach's alpha was 0.93.

Quality of life, gaming motivation, and personality

Quality of life was measured using the World Health Organization—Quality of Life Inventory (WHOQOL-BREF). Developed by the World Health Organization, the WHOQOL-BREF is an abbreviated “Quality of Life Scale.”^46,47 Twenty-six statements address four domains of quality of life: (a) physical health, (b) psychological health, (c) social relationships, and (d) environment. Two items measure overall quality of life and the general health of the participants. The items use a 5-point Likert-type rating scale. Higher scores indicate a higher quality of life. Hsiao et al.⁴⁶ found Cronbach's alphas for the physical, psychological, social, and environmental scales of 0.76, 0.76, 0.51, and 0.76, respectively. In the present sample, Cronbach's alphas for the physical, psychological, social, and environmental scales were 0.79, 0.85, 0.72, and 0.80, respectively.

Gaming motivation that was on the borderline between being addicted and being engaged was examined using the Gaming Motivation Scale by Yee.²⁹ The items are rated on a 5-point Likert-type scale. The items measure three components of gaming motivation: (a) achievement (desire to gain power and progress rapidly, to challenge and compete with others) with items such as “How important is it for you to level up your character as fast as possible?” or “How important is it for you to know as much about the game mechanics and rules as possible?”; (b) sociability (interested in helping and chatting with others and forming meaningful relationships) with items such as “How much do you enjoy getting to know other players?” or “How much do you enjoy chatting with other players?”; and (c) immersion (finding and knowing things in the game, creating a person with a background story and having an interest in customizing the appearance of the character) with items such as “How much do you enjoy exploring the world just for the sake of exploring it?” or “How often do you play to relax from the day's work?”. In his study, Yee²⁹ found a Cronbach's alpha of 0.77. In the present sample, the Cronbach's alpha was 0.85.

Differences regarding the personality of engaged and addicted players as the last parameter of the study was evaluated on the basis of the Big-Five concept with the Big-Five Inventory with 10 items (BFI-10). The BFI-10 is based on the model of McCrae and Costa⁴⁸ and was developed by Rammstedt and John.⁴⁹ This short version was used to assess personality differences. Ten items measure the Big-Five factors of extraversion, agreeableness, openness, conscientiousness, and neuroticism. Structural and convergent validity with the Revised NEO Personality Inventory (NEO-PI-R) and its facets, and external validity indicate that the BFI-10 scales retain significant levels of reliability and validity.^50,51 In the present sample, however, the Cronbach's alpha was only 0.58.

Statistical analyses

Statistical analyses were carried out using SPSS Statistics for Windows v19 (IBM Corp.). The Bonferroni correction method was used to counteract problems of multiple comparisons. Chi-square and t tests were used to measure differences between addicted and engaged players in sociodemographic variables, and analyses of variance were used to find differences between engaged and addicted players in variables such as immersion, quality of life, or personality traits.

Results

General sample characteristics

The age of the 676 participants (84.9% male) ranged from 16 to 51 years (M=23.26, SD=5.66; 6 participants did not disclose their age). A total of 87.1% (n=594) of participants came from Germany, 9.8% (n=67) from Austria, 1.8% (n=12) from Switzerland, and 1.2% (n=8) from other countries, with 0.1% (n=1) not answering the question. A total of 12.0% (n=82) had finished university, and 32.6% (n=222) had finished high school. A total of 86.2% (n=588) of the sample were playing on the maximum level of 90. The average time per week spent online playing WoW was 20.5 hours (SD=15.86).

There was no significant sex difference concerning the weekly amount of time spent online playing WoW, t(674)=−0.762, p=0.447. Male players use WoW for an average of 20.62 hours (SD=15.94) per week; females play for an average of 19.29 hours (SD=15.10) per week. On average, participants had played WoW for 5.42 years (SD=2.50; see Table 1 for further details).

Table 1.

General Sample Characteristics

Variables	M	SD
Age	23.26	5.66
Hours per week playing WoW	20.50	15.86
WoW experience (years)	5.42	2.50
WoW experience (level)	87.93	8.35
SPIN
Total sum score	17.78	3.17
BDI
Total sum score	9.74	3.12
WHOQOL-BREF
Physical health	28.81	4.42
Psychological health	22.39	4.80
Social relationships	10.64	2.90
Environment	32.44	5.16
BFI-10
Extraversion	13.98	4.19
Agreeableness	14.41	3.27
Conscientiousness	11.11	3.96
Neuroticism	16.22	3.35
Openness	15.39	3.47
Motivation Scale by Yee
Achievement	39.36	9.20
Social	36.94	5.80
Immersion	38.14	9.88

SPIN, Social Phobia Inventory; BDI, Beck Depression Inventory; WHOQOL-BREF, World Health Organization—Quality of Life Inventory (abbreviated version); BFI-10, Big-Five Inventory with 10 items.

Addiction and high engagement

Using the German WoW-Questionnaire from Lehenbauer-Baum and Fohringer,³⁶ two sum scores were calculated by using the two subscales “addiction” and “engagement,” as recommended by Charlton and Danforth.³⁸ In their pilot study, Lehenbauer-Baum and Fohringer³⁶ used a median split within these two groups to find differences between addicted and engaged users. To examine psychopathological and other differences between addicted and engaged users, the present study used a more conservative approach with a percentile-split method. A cutoff of the 90th percentile was chosen for the two subscales to build two groups within each factor (high engagement vs. low engagement; high addiction vs. low addiction). Participants with >50 points on the addiction scale and <80 points on the engagement scale were assigned to the “addiction” group (n=67); participants with <50 points on the addiction scale and >80 points on the engagement scale were assigned to the “high engagement” group (n=60). Using this method, users scoring high on the engagement scale could be compared with users scoring high on the addiction scale.

No significant differences were found between engaged and addicted players with regard to sex, education, family status, age, and the level of experience in months. However, a significant difference was found concerning the weekly amount of time spent online playing WoW (see Table 2).

Table 2.

Sample Characteristics of Engaged and Addicted Players

Variable	High engagement (n=60)	Addiction (n=67)	Statistics	Effect size, d
Sex
Male (n)	50	61	χ² _(1,N=126)=2.477; p=0.116	N/A
Female (n)	10	5
No answer (n)	0	1
Education
None (n)	1	2	χ²_(5,N=126)=2.911; p=0.714	N/A
Compulsory school	37	42
High school (n)	17	19
University (n)	4	4
No answer (n)	1	0
Family status
Single (n)	36	40	χ²_(3,N=127)=3.260; p=0.353	N/A
Married/partnered (n)	23	27
Divorced (n)	1	0
No answer (n)	0	0
Age, M (SD)	24.10 (5.88)	22.87 (5.77)	t₍₁₂₅₎=1.191; p=0.236	0.21
Hours per week playing, M (SD)	11.93 (9.13)	39.25 (28.57)	t₍₁₂₅₎=−7.087; p<0.001	1.27
WoW experience (level), M (SD)	84.35 (15.14)	89.32 (3.38)	t₍₁₂₄₎=−2.596; p=0.11	0.47
WoW experience (years), M (SD)	5.25 (2.74)	5.42 (2.50)	t₍₁₂₅₎=−0.361, p=0.719	0.06

WoW, World of Warcraft.

The SPIN is an effective questionnaire for the screening and measurement of the severity of social anxiety disorder. By using the total sum score, significant differences between addicted and engaged players were found. Addicted users scored significantly higher (M=45.97, SD=15.62) compared with engaged users (M=29.98, SD=11.52; see Table 3).

Table 3.

Differences Between Addicted and Engaged Players

	Engagement (n=60)		Addiction (n=67)
Variables	M	SD	M	SD	p	F-statistics	Effect size, d
SPIN
Total sum score	12.98	11.52	28.97	15.62	<0.001	42.22	1.18
BDI
Total sum score	5.76	6.40	19.56	13.52	<0.001	52.02	1.34
WHOQOL-BREF
Physical health	30.16	3.69	25.47	5.24	<0.001	33.1	1.05
Psychological health	24.10	4.38	18.94	5.40	<0.001	34.36	1.05
Social relationships	10.93	2.80	9.05	3.18	0.001	12.27	0.63
Environment	33.66	4.70	28.76	7.05	<0.001	20.72	0.83
BFI-10
Extraversion	13.68	4.30	13.59	4.28	0.910	0.45	0.02
Agreeableness	16.31	3.98	14.04	4.30	0.003	12.26	0.55
Conscientiousness	15.36	3.15	13.17	3.55	<0.001	19.27	0.65
Neuroticism	10.28	4.00	13.40	3.80	<0.001	8.67	0.80
Openness	15.30	3.51	15.33	3.74	0.953	0.004	0.01
Motivation Scale by Yee
Achievement	30.41	7.81	48.92	8.33	<0.000	165.47	2.30
Social	35.56	7.39	36.14	5.91	0.623	0.243	0.09
Immersion	31.78	8.74	44.82	11.41	<0.000	51.33	1.28

Results of the BDI in the surveyed sample indicate significant differences between engaged and addicted players in respect of depression scores. Addicted users scored higher (M=40.56, SD=13.52) compared with engaged users (M=26.76, SD=6.40; see Table 3).

Charlton and Danforth³⁸ hypothesized differences between addicted and engaged users regarding quality of life. To examine these differences, the sum scores of the WHOQOL-BREF subscales were employed. Overall, engaged users scored significantly higher in physical health (M=30.16, SD=3.69), psychological health (M=24.10, SD=4.38), social relationships (M=10.93, SD=2.80), and environment (M=33.66, SD=4.70; see Table 3).

Furthermore, significant differences were found between addicted and engaged players when it came to their personality, measured by the BFI-10. There were significant differences in the dimensions of agreeableness (M=16.31, SD=3.98), conscientiousness (M=15.36, SD=3.15), and neuroticism (M=10.28, SD=4.00). Engaged gamers displayed higher means in all three dimensions (see Table 3).

To examine gaming motivation, the Gaming Motivation Scale by Yee²⁹ was employed. There were significant differences between engaged and addicted players in the factors “achievement” and “immersion.” Addicted players displayed higher achievement and immersion scores (see Table 3).

Discussion

While there are some similarities between the negative outcomes of an excessive gaming behavior and the cluster of cognitive–behavioral symptoms of nonsubstance use disorders,^7,9,11 with so many people playing online without any negative consequences, it is not clear where to draw the line. IGD is currently listed as a condition needing further research, especially regarding classification criteria and comorbidities with other psychiatric disorders.⁶ The proposed classification criteria include (a) preoccupation, (b) withdrawal, tolerance, (c) unsuccessful attempts to control, (d) loss of interests, (e) continued excessive use despite psychosocial problems, (f) deceiving, (g) escape, and (h) functional impairment.^4,11 However, according to Charlton and Danforth³⁸ and Lehenbauer-Baum and Fohringer,³⁶ there are differences between a healthy engagement with games (with items tapping cognitive salience, tolerance, and euphoria) and addiction (with items tapping conflict, withdrawal symptoms, relapse and reinstatement, and behavioral salience). Unfortunately, most studies to date do not distinguish between these, and label engaged users as addicted.³⁶ Accordingly, Pontes et al.¹³ found that high engagement with games does not automatically link to the more severe outcomes of an addiction with games. Therefore, the present study investigated differences between engaged and addicted gaming behavior.

The present explorative study was able to attract high-level players with experience. The average player in our sample played for 20.5 hours per week on level 87, and had been playing for 5.42 years. By applying Charlton and Danforth's³⁸ “addiction” (with items tapping conflict, withdrawal symptoms, relapse and reinstatement, and behavioral salience) and “high engagement” (with items tapping cognitive salience, tolerance, and euphoria) subscales, highly significant differences were found. On average, addicted gamers play for 39.25 hours per week, whereas engaged players only play for 11.93 hours per week. Statistical analysis did not reveal any significant differences between engaged and addicted players regarding the level played and the experience in years, similar to Lehenbauer-Baum and Fohringer.³⁶ Addicted gamers played on level 89.32, and engaged players on level 84.34, both a very high level. Both groups had been playing for 5 years. Therefore, the results suggest that high gaming results or a higher gaming experience do not automatically link to addiction; engaged players play on a similarly high level.

Furthermore, statistical analyses suggest differences between engaged and addicted players in respect of depression, social fears, quality of life, personality, and motivation. Addicted users (scoring high on items tapping conflict, withdrawal symptoms, relapse and reinstatement, and behavioral salience) have significantly higher depression scores on the BDI. These findings are in line with previous studies^18–20; addiction is significantly more linked to depression than engagement. Furthermore, the results present other evidence that addiction is correlated to psychopathologies; addicted users report significantly higher scores on the Social Phobia Inventory. This outcome is similar to other studies.^17,23–27 However, in addition to previous studies, the presented analysis indicates that engaged players (scoring high on items tapping cognitive salience, tolerance, and euphoria) have significantly lower scores on the BDI and SPIN, and have significantly higher scores in their quality of life such as physical and psychological health, social relationships, and environment. Furthermore, significant differences were found when it came to personality traits, similar to previous studies.^14–16,40 While Lehenbauer-Baum and Fohringer³⁶ were able to find differences in agreeableness and conscientiousness (with addicted players having higher scores), the current study found differences in agreeableness, conscientiousness, and neuroticism, similar to reports by Floros et al.²⁸ Addicted players seem to be less agreeable and thorough, and less emotionally stable; they are more likely to experience feelings such as anxiety, anger, guilt, or envy. The current results suggest that there are significant differences between engaged and addicted players, with higher comorbidities in the latter group.

To the authors' knowledge, this is the first study to examine motivational differences between engaged and addicted players by using the Gaming Motivation Scale by Yee.²⁹ Significant differences were found between addicted and engaged players in two out of three subscales. The results suggest that the motivation of achievement in particular sets engaged and addicted users apart (with a very large effect size). According to the presented analysis, addicted players are more motivated to gain power, progress rapidly, accumulate in-game symbols of wealth or status, have an interest in analyzing the underlying rules to optimize performance, and be more motivated to compete with others. Another motivational factor that sets engaged and addicted players apart is immersion. Addicted players spend more time finding and knowing things that most other players do not know about and have more interests in escaping from reality.⁴² However, no statistically relevant differences were found in the social dimension; addicted players do not have more interest in helping and chatting with other players. Hsu et al.³² reported similar results, stating that immersion is a significant predictor of MMORPG addiction. Thus, the present results suggest that immersion and particularly achievement are factors that set addiction apart from engagement. Including motivational questionnaires in future research is recommended when defining the diagnostic criteria for IGD.

However, there are certain limitations to the current study. The findings are based on a self-selected online sample of WoW players. The results have to be viewed with caution. No conclusions can be drawn about the overall population of WoW or MMORPG players. A translated version of the Asheron's Call questionnaire was employed, and labeled users as addicted or engaged after Charlton and Danforth's³⁸ study revealed two factors. Therefore, the outcomes concerning addiction and high engagement should also be viewed with caution.^36,38,40

Nevertheless, the main strength of this study is the high number of experienced participants. The results support the findings of Lehenbauer-Baum and Fohringer.³⁶ The study is limited to a German sample and players of MMORPG only. Therefore, further studies addressing differences between engagement and addiction in cross-cultural samples, as well as comparisons between different types of games (e.g., MMORPG, shooter games, strategy games, sport games, etc.) are advocated. Nevertheless, for future versions of the DSM, criteria such as euphoria, tolerance, and cognitive salience should be viewed with caution when it comes to finding criteria for IGD similar to (behavioral) addiction. The current results suggest that items tapping euphoria, tolerance, and cognitive salience are rather expressions of a (nonproblematic) highly engaged gaming behavior and should not be confused with addiction.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

References

Blaszczynski

, Nower

. A pathways model of problem and pathological gambling. Addiction, 2002; 97:487–499.

Blaszczynski

. Internet use: in search of an addiction. International Journal of Health & Addiction, 2006; 4:7–9.

Petry

, O'Brien

. Internet gaming disorder and the DSM-5. Addiction, 2013; 108:1186–1187.

Cho

, Kwon

, Choi

, et al. Development of the Internet addiction scale based on the Internet Gaming Disorder criteria suggested in DSM-5. Addictive Behaviors, 2014; 39:1361–1366.

American Psychiatric Association. (2013) Internet gaming disorder. www.dsm5.org/Documents/Internet Gaming Disorder Fact Sheet.pdf (accessed Nov. 5, 2014).

Petry

, Rehbein

, Gentile

, et al. An international consensus for assessing Internet gaming disorder using the new DSM-5 approach. Addiction, 2014; 109:1399–1406.

Dong

, Potenza

. A cognitive-behavioral model of Internet gaming disorder: theoretical underpinnings and clinical implications. Journal of Psychiatric Research, 2014; 58C:7–11.

King

, Delfabbro

. The cognitive psychology of Internet gaming disorder. Clinical Psychology Review, 2014; 34:298–308.

King

, Haagsma

, Delfabbro

, et al. Toward a consensus definition of pathological video-gaming: a systematic review of psychometric assessment tools. Clinical Psychology Review, 2013; 33:331–342.

10.

C-H

, Yen

J-Y

, Chen

S-H

, et al. Evaluation of the diagnostic criteria of Internet gaming disorder in the DSM-5 among young adults in Taiwan. Journal of Psychiatric Research, 2014; 53:103–110.

11.

Tonioni

, Mazza

, Autullo

, et al. Is Internet addiction a psychopathological condition distinct from pathological gambling?. Addictive Behaviors, 2014; 39:1052–1056.

12.

Wang

, Ho

RTH

, Chan

CLW

, et al. Exploring personality characteristics of Chinese adolescents with Internet-related addictive behaviors: trait differences for gaming addiction and social networking addiction. Addictive Behaviors, 2015; 42:32–35.

13.

Pontes

, Király

, Demetrovics

, et al. The conceptualisation and measurement of DSM-5 Internet gaming disorder: the development of the IGD-20 test. PLoS One, 2014; 9:e110137.

14.

Ebeling-Witte

, Frank

, Lester

. Shyness, Internet use, and personality. CyberPsychology & Behavior, 2007; 10:713–716.

15.

Teng

C-I

. Personality differences between online game players and nonplayers in a student sample. CyberPsychology & Behavior, 2008; 11:232–234.

16.

Floros

, Siomos

, Stogiannidou

, et al. Comorbidity of psychiatric disorders with Internet addiction in a clinical sample: the effect of personality, defense style and psychopathology. Addictive Behaviors, 2014; 39:1839–1845.

17.

Tang

, Yu

, Du

, et al. Prevalence of Internet addiction and its association with stressful life events and psychological symptoms among adolescent Internet users. Addictive Behaviors, 2014; 39:744–777.

18.

, Liau

, Khoo

. Examining the influence of actual-ideal self-discrepancies, depression, and escapism, on pathological gaming among massively multiplayer online adolescent gamers. Cyberpsychology, Behavior & Social Networking, 2011; 14:535–539.

19.

Shapira

, Goldsmith

, Keck

, et al. Psychiatric features of individuals with problematic Internet use. Journal of Affective Disorders, 2000; 57:267–272.

20.

Wei

, Chen

, Huang

, et al. The association between online gaming, social phobia, and depression: an Internet survey. BMC Psychiatry, 2012; 12:92.

21.

Bozoglan

, Demirer

, Sahin

. Problematic Internet use: functions of use, cognitive absorption, and depression. Computers in Human Behavior, 2014; 37:117–123.

22.

Ceyhan

, Ceyhan

. Loneliness, depression, and computer self-efficacy as predictors of problematic Internet use. CyberPsychology & Behavior, 2008; 11:699–701.

23.

Stetina

, Kothgassner

, Lehenbauer

, et al. Beyond the fascination of online-games: Probing addictive behavior and depression in the world of online-gaming. Computers in Human Behavior, 2011; 27:473–479.

24.

Caplan

. Relations among loneliness, social anxiety, and problematic Internet use. CyberPsychology & Behavior, 2007; 10:234–242.

25.

Cole

, Griffiths

. Social interactions in massively multiplayer online role-playing gamers. CyberPsychology & Behavior, 2007; 10:575–583.

26.

Morahan-Martin

, Schumacher

. Loneliness and social uses of the Internet. Computers in Human Behavior, 2003; 19:659–671.

27.

Yen

, Yen

, Chen

, et al. Social anxiety in online and real-life interaction and their associated factors. Cyberpsychology, Behavior, & Social Networking, 2012; 15:7–12.

28.

Floros

, Siomos

. Excessive Internet use and personality traits. Current Behavioral Neuroscience Reports, 2014; 1:19–26.

29.

Yee

. Motivations for play in online games. CyberPsychology & Behavior, 2006; 9:772–775.

30.

Yee

. The demographics, motivations, and derived experiences of users of massively multi-user online graphical environments. Presence: Teleoperators & Virtual Environments, 2006; 15:309–329.

31.

Caplan

, Williams

, Yee

. Problematic Internet use and psychosocial well-being among MMO players. Computers in Human Behavior, 2009; 25:1312–1319.

32.

Hsu

, Wen

, Wu

. Exploring user experiences as predictors of MMORPG addiction. Computers & Education, 2009; 53:990–999.

33.

Williams

, Yee

, Caplan

. Who plays, how much, and why? Debunking the stereotypical gamer profile. Journal of Computer-Mediated Communication, 2008; 13:993–1018.

34.

Griffiths

. Technological addiction. Clinical Psychology Forum, 1995; 76:14–19.

35.

Griffiths

. Behavioural addiction: an issue for everybody?. Employee Counselling Today, 1996; 8:19–25.

36.

Lehenbauer-Baum

, Fohringer

. Towards classification criteria for Internet gaming disorder: debunking differences between addiction and high engagement in a German sample of World of Warcraft players. Computers in Human Behavior, 2015; 45:345–351.

37.

Charlton

, Danforth

. (2004) Differentiating computer related addictions and high engagement. In Morgan

, Brebbia

, Sanchez

, Voiskounsky

, eds. Human perspectives in the Internet society: culture, psychology, gender. Southampton, United Kingdom: WIT Press, pp. 59–68.

38.

Charlton

, Danforth

IDW

. Distinguishing addiction and high engagement in the context of online game playing. Computers in Human Behavior, 2007; 23:1531–1548.

39.

Charlton

. A factor-analytic investigation of computer “addiction” and engagement. British Journal of Psychology, 2002; 93:329–344.

40.

Charlton

, Danforth

IDW

. Validating the distinction between computer addiction and engagement: online game playing and personality. Behaviour & Information Technology, 2010; 29:601–613.

41.

American Psychological Assocation. (2010) Ethical principles of psychologists and code of conduct. www.apa.org/ethics/code/index.aspx (accessed Nov. 5, 2014).

42.

Beck

, Steer

, Brown

. (1996) BDI-II, Beck depression inventory: manual. San Antonio, TX: Psychological Corporation.

43.

Connor

, Davidson

, Churchill

, et al. Psychometric properties of the Social Phobia Inventory (SPIN). New self-rating scale. British Journal of Psychiatry, 2000; 176:379–386.

44.

Ranta

, Kaltiala-Heino

, Rantanen

, et al. Screening social phobia in adolescents from general population: the validity of the Social Phobia Inventory (SPIN) against a clinical interview. European Journal of Psychiatry, 2007; 22:244–251.

45.

Lehenbauer

, Kothgassner

, Kryspin-Exner

, et al. An online self-administered social skills training for young adults: results from a pilot study. Computers & Education;, 61:217–224.

46.

Hsiao

Y-Y

, Wu

C-H

, Yao

. Convergent and discriminant validity of the WHOQOL-BREF using a multitrait-multimethod approach. Social Indicators Research, 2013; 116:971–988.

47.

World Health Organization. (2012) WHO Quality of Life-BREF (WHOQOL-BREF). www.who.int/mental_health/publications/whoqol/en/ (accessed Nov. 5, 2014).

48.

McCrae

, Costa

. Validation of the five-factor model of personality across instruments and observers. Journal of Personality & Social Psychology, 1987; 52:81–90.

49.

Rammstedt

, John

. Kurzversion des Big Five Inventory (BFI-K): Diagnostica. 2005; 51:195–206.

50.

Rammstedt

. The 10-item Big Five Inventory: norm values and investigation of German population representative sample. European Journal of Psychological Assessment, 2007; 23:193–201.

51.

Rammstedt

, John

. Measuring personality in one minute or less: a 10-item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 2007; 41:203–212.