Biopsychosocial factors of gaming disorder: a systematic review employing screening tools with well-defined psychometric properties

Abstract

Background and aims:

Considering the growing number of gamers worldwide and increasing public concerns regarding the negative consequences of problematic gaming, the aim of the present systematic review was to provide a comprehensive overview of gaming disorder (GD) by identifying empirical studies that investigate biological, psychological, and social factors of GD using screening tools with well-defined psychometric properties.

Materials and methods:

A systematic literature search was conducted through PsycINFO, PubMed, RISS, and KISS, and papers published up to January 2022 were included. Studies were screened based on the GD diagnostic tool usage, and only five scales with well-established psychometric properties were included. A total of 93 studies were included in the synthesis, and the results were classified into three groups based on biological, psychological, and social factors.

Results:

Biological factors (n = 8) included reward, self-concept, brain structure, and functional connectivity. Psychological factors (n = 67) included psychiatric symptoms, psychological health, emotion regulation, personality traits, and other dimensions. Social factors (n = 29) included family, social interaction, culture, school, and social support.

Discussion:

When the excess amount of assessment tools with varying psychometric properties were controlled for, mixed results were observed with regards to impulsivity, social relations, and family-related factors, and some domains suffered from a lack of study results to confirm any relevant patterns.

Conclusion:

More longitudinal and neurobiological studies, consensus on a diagnostic tool with well-defined psychometric properties, and an in-depth understanding of gaming-related factors should be established to settle the debate regarding psychometric weaknesses of the current diagnostic system and for GD to gain greater legitimacy in the field of behavioral addiction.

1. Introduction

Gaming is a widely and commonly enjoyed leisure activity. The number of active video gamers worldwide has marked 2.69 billion by the end of 2020 and is expected to continue its growing pattern (1). The recent trend towards more gaming engagement has been partly attributed to the widespread COVID-19 lockdown, which has hindered engagement in other interpersonal connections (2). While healthy usage of gaming brings certain emotional, social, and educational benefits, problematic gaming has been associated with negative consequences (3–6). Taking the inevitable link between the perils of addiction to games and mental health into account, Internet gaming disorder was included in the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as a “condition for further study” (7). Furthermore, gaming disorder (GD) was recently included in the 11th Revision of the International Classification of Diseases [ICD-11; (8)].

However, there has been an ongoing debate among experts and researchers in the field on the issue of GD being officially recognized as a non-substance addiction disorder (9, 10). One of the main difficulties in settling this debate stems from the implementation of different GD screening and assessment tools with varying psychometric properties. King et al. (11) reported that more than 40 diagnostic tools with different evaluative properties were being employed in GD research studies. The number of screening tools continues to grow due to the adaption or development of new tools in lieu of utilizing already established ones. The plethora of new tools with questionable psychometric properties led many researchers in the field to criticize assessment and measure inconsistencies in GD papers and to argue that adequate psychometric properties of the scales need to be established for the effective comparability of the study results (12–14). Therefore, in order to systematically review GD papers effectively, it is crucial to start from similar, if not the same, criteria for GD as much as possible. In other words, the excessive tool usage in GD research needs to be controlled in an attempt to accurately compare and analyze the results of existing GD studies.

King et al. (11) evaluated all available GD screening tools according to their DSM/ICD coverage, empirical evidence, and psychometric properties. While no single tool was found to be superior, they reported five scales with greater evidential support for their psychometric properties: (1) Assessment of Internet and Computer Addiction Scale-Gaming (AICA-Sgaming), (2) Seven-Item Game Addiction Scale (GAS-7), (3) Ten-Item Internet Gaming Disorder Test (IGDT-10), (4) Internet Gaming Disorder Scale-Short Form (IGDS9-SF), and (5) Internet Gaming Disorder Scale (Lemmens IGD-9). In a recent meta-analysis, all five instruments were found to have good internal consistency and test–retest reliability (15). Thus, in order to control for the abundance of GD tools in the field, the present review sought to implement an incisive approach of gathering and comparing results of studies that have utilized one of these five GD tools with relatively greater evidential support.

Another difficulty in settling the debate on the issue of GD being officially recognized as an addictive disorder stems from the lack of systematic reviews of scientific literature on GD that identify comprehensive factors associated with gaming. To our knowledge, there exist a small number of comprehensive systematic reviews. Mihara and Higuchi (16) reviewed cross-sectional and longitudinal epidemiological studies of GD that were published up to May 2016. They reported that the comparison of the findings was hindered by insufficient longitudinal studies along with diversified methodologies utilized in each study. Paulus et al. (17) reviewed literature that investigated GD factors in children and adolescents that were published up to August 2016. They concluded that while GD can be characterized as a complex and endangering disorder, its concept and pathways leading to it cannot be fully analyzed due to the lack of longitudinal studies. Similarly, Sugaya et al. (18) reviewed literature that investigated biopsychosocial factors of GD in children and adolescents that were published up to February 2018. They summarized various factors that were associated with the presence of GD yet acknowledged that diverse methods of classifications yielded differences in results.

None of the prior reviews have imposed restrictions on the diagnostic tool usage, which hindered the comparison of the findings. Furthermore, there is a need for an updated literature search considering the increased attention GD has received since the ICD-11 release year as well as the rapid growth of novel coronavirus disease 19 (COVID-19). Therefore, the aim of the present review was to overcome the limitations of existing reviews and to provide a more up-to-date, comprehensive overview of GD by systematically identifying and summarizing the findings of studies that used one of five aforementioned diagnostic tools to investigate biopsychosocial factors of GD. To clarify, the purpose of the present review is not to evaluate the best GD diagnostic tools that must be used in investigations but rather to analyze GD patterns, if they exist, after controlling for various diagnostic tools used in articles.

2. Materials and methods

The present systematic review sought to collect the findings of all published studies reporting biological, psychological, or social factors related to gaming disorder. Included literature used various terms for describing problematic gaming behaviors; to maintain consistency and to avoid confusion, we use the term GD for all classification styles. A systematic approach following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were utilized (19).

2.1. Search process and eligibility criteria

Figure 1 presents a summary of the search process for the systematic review. The search included all publication years (up to January 2022) using four large electronic databases: PsycINFO, PubMed, RISS (Research Information Sharing Service; http://www.riss.kr/index.do), and KISS (Koreanstudies Information Service System; https://kiss.kstudy.com/index.asp). The latter two are large South Korean research databases and were implemented in the search for the inclusion of relevant literature published in Korean; this step was added, considering the high internet penetration rate in Asia and the importance of exploring international databases (20). The title or abstract terms used for the search for PsycINFO and PubMed were: (“pathology*” OR “problem*” OR “compulsive” OR “overuse” OR “abuse” OR “dependen*” OR “disorder*” OR “excess*” OR “addict*”) AND (“video” OR “computer” OR “internet” OR “online” OR “offline”) AND (“gaming” OR “game”). The terms used for the search for RISS and KISS were: “gaming addiction” OR “gaming disorder” OR “excessive gaming” OR “gaming use disorder” OR “gaming dependence” translated in Korean. The discrepancy in search terms was due to language and search setting differences. The initial search yielded 5,297 results. After removing the duplicates, the titles and abstracts of the remaining results (n = 4,855) were evaluated according to the following inclusion criteria: (1) published in peer-reviewed journals, (2) written in English or Korean, (3) empirical studies with primary data, (4) full-text availability, (5) investigated biopsychosocial characteristics of GD, and (6) utilized one of five scales (GAS-7, AICA-Sgaming, IGDS9-SF, Lemmens IGD-9, or IGDT10) to assess GD symptoms. The remaining full-text articles (n = 721) were read thoroughly. Six studies were not empirical studies, 587 studies did not utilize GD scales of choice, and 35 studies did not investigate biopsychosocial factors of GD thus were excluded. This resulted in 93 articles being included in the qualitative synthesis. The quality of each article was evaluated using the Kmet quality checklist [(21); see Appendix A in Supplementary material]. Throughout the search process, three researchers independently selected articles that met the inclusion criteria and any disagreements were resolved by consensus.

Figure 1

2.2. Scale descriptions

Studies were selected based on the GD screening tools recommended by King et al. (11): IGDS9-SF, GAS-7, Lemmens IGD-9, AICA-Sgaming, and IGDT-10. These were chosen upon a critical evaluation of all available GD tools in terms of their DSM/ICD coverage, quantitative evidence base, and psychometric properties. See Appendix B in the Supplementary material for the detailed descriptions for each scale.

2.3. Data extraction and synthesis

Information from 93 studies was extracted regarding the participant characteristics, GD diagnostic information, key findings, and statistical analyses used. Results were then grouped by categories (biological, psychological, or social) and corresponding subcategories.

3. Results

3.1. Study characteristics

Information extracted from the studies included in the review are presented in Tables 1–3. Results were classified into three groups: biological (n = 8), psychological (n = 67), or social (n = 29). Since psychological and social factors were often studied together, 11 studies were included in both groups. Approximately 70% of the studies were published within the past 4 years (2019–2022). Geographically, studies were carried out in Europe (n = 40), Asia (n = 38 including n = 13 from the Middle East), North America (n = 3), South America (n = 2), and worldwide (n = 10). As for the gender of study samples, six studies reported samples of only males while the remaining reported samples of all genders. The study samples included young adults/adult population (n = 51), adolescents/children (n = 31), or all age ranges (n = 11). Following the inclusion criteria, all studies utilized one of the five diagnostic scales: IGDS9-SF (n = 40), GAS-7 (n = 23), Lemmens IGD-9 (n = 17), AICA-Sgaming (n = 7), and IGDT10 (n = 6). When assessed for quality, the summary score (total score divided by the total possible score) of all studies ranged from 0.75 to 1.00, which met the conservative cut-point (0.75) suggested by the quality assessment guidelines (21).

3.2. Biological factors associated with IGD

A total of eight studies have investigated biological factors related to GD (Table 1). Among them, three studies investigated reward-related activities and two studies investigated self-concept. The remaining three were specifically related to neurobiology, with one focusing on the brain structure and the other two focusing on functional connectivity (FC).

3.2.1. Reward activity

Using electroencephalography (EEG), one study reported reduced peak amplitudes and longer latencies in response to rewards in pathological computer game players compared to casual players, suggesting a reduced reward sensitivity to gaming rewards in GD (23). Another study using functional magnetic resonance imaging (fMRI) found deficits in the reward and self-control brain systems in response to video gaming cues (24). Specifically, intensive gamers showed stronger activation in the ventral striatum and weaker activation in the dorsolateral prefrontal cortex (DLPFC) compared to controls when watching game-related videos, and GD scores of the gamers were positively associated with the right ventral striatum activity and negatively associated with the right DLPFC activity. Moreover, in a game-deprived state, gamers showed activation in the left insula when exposed to video gaming cues, and the insular activation in the deprivation condition was associated with increased striatal activity and decreased prefrontal activity, which showed similarity to other addictive behaviors. Lower dopamine transporter level has also been associated with more severe GD symptoms in those with GD (22). These studies suggest distinctive functioning of brain regions related to reward in GD.

3.2.2. Self-concept

Two studies consistently reported anomalies in various aspects of self-concept in GD individuals (25, 26). Individuals with GD considered their game avatar significantly superior to their self in terms of social and emotional competencies and they showed hyperactivation in the left angular gyrus (AG), a region that has previously been found to be associated with self-concept-related processing (113, 114), during avatar reflection relative to self-reflection compared to healthy controls. Furthermore, a significant positive correlation was found between the left AG activation and GD severity (26).

3.2.3. Brain structure

GD individuals exhibited decreased gray matter density in the left DLPFC compared to healthy controls and non-problematic game players (27). Moreover, lower gray matter density in the DLPFC was associated with longer lifetime usage of gaming and more severe GD symptoms.

3.2.4. Resting-state fMRI functional connectivity

In an fMRI study that assessed functional connectivity (FC) during resting state, the GD individuals displayed lower FC from the bilateral orbitofrontal cortex to the overall brain (frontal, striatal, temporal, and occipital) regions compared to the healthy controls (29). Another study with a larger sample size reported weaker FC in the central executive network, salience network, and default mode network during resting state in GD individuals compared to healthy controls (28). Specifically, GD individuals showed lower FC between the ventrolateral prefrontal cortex and DLPFC and between the posterior parietal cortex and DLPFC in the central executive network, between the dorsal anterior cingulate cortex and fronto-insular cortex and ventral striatum in the salience network, and in the medial prefrontal cortex of the anterior default mode network compared to healthy controls. Taken together, altered functional connectivity found in these studies might suggest impairments in the capacity of the core brain networks in GD, although more studies are needed to confirm these patterns.

3.3. Psychological factors associated with GD

A total of 67 studies have investigated psychological factors related to GD (Table 2). Among them, 24 studies investigated psychiatric symptoms. Seven papers investigated psychological health, with a focus on general well-being. Six investigated emotion regulation and the other five investigated rewards, discounting and impulsivity. Five investigated personality traits, and three investigated stress. Cognitive approach, self-concept, and sleep each included two studies. Considering the current COVID-19 pandemic, a section was designated for COVID-19 which included 8 studies. The remaining studies were categorized as miscellaneous, with one study investigating flow and the other psychological needs.

3.3.1. Psychiatric symptoms

Stockdale and Coyne (50) reported that individuals with GD presented a higher degree of ADHD, anxiety, depression, aggression, and pornography addiction than those without GD. In terms of ADHD, in addition to a bidirectional relationship between GD and ADHD (48), a unidirectional relationship from ADHD to GD (34, 52) as well as from GD to ADHD were found (30, 31) in regression analyses.

In terms of depression and anxiety, a positive association with GD was consistently reported (32, 33, 40–42, 47) with some studies reporting that GD positively predicted the levels of depression and anxiety (36, 54). When focusing on depression or anxiety independently, Severo et al. (45) found a positive association between GD symptoms and depressive symptoms, Singh et al. (46) found that depressive symptoms predicted GD, and Stavropoulos et al. (49) found that GD predicted higher depressive symptoms. One study revealed that depressive symptoms fully mediated the association between children’s emotional trauma and GD (39). In one study, GD scores had positive correlations with anxiety (53) while no association was found in another study (38). Overall, these suggest that GD is associated with ADHD, depression, and anxiety. However, there is a lack of temporal findings to confirm the directionalities.

With regards to other psychiatric symptoms, Musetti et al. (44) found that, compared to non-problematic gamers, problematic gamers displayed higher levels of psychotic symptoms. Murray et al. (43) found that individuals with autism spectrum disorder showed significantly higher GD scores compared to HC. There have been mixed results regarding the substance use patterns, with one study reporting an association between GD severity and polysubstance use (37), while another study reported significantly lower GD symptom scores in individuals with substance use disorder compared to those without the disorder (51).

3.3.2. Psychological health

In one study, GD predicted low self-esteem, perceived social support, and life satisfaction 6 months and 1-year later, suggesting that GD can possibly lead to decreased psychosocial well-being (61). Several studies have found a relationship between GD and antisocial behavior, anger control problems, and hyperactivity and inattention (40, 58, 59, 99). In a Spanish sample, self-esteem, life satisfaction, prosocial behavior, loneliness, and physical aggression predicted GD (55). Among Turkish adults, GD symptoms predicted the presence of lifetime non-suicidal self-injurious behaviors (30, 31). Some studies also found gender effects. Phan et al. (60) found that GD led to decreased quality of life in males, but not in females. Furthermore, the influence of GD tendency on suicide risk was higher in male college students, although female college students showed higher suicide risk on average (56). These results suggest that GD is generally associated with poor psychological health while more studies are needed to confirm gender effects.

3.3.3. Emotion regulation

The inability to manage one’s emotional experience has often been found to be associated with GD (64, 66). Kim and Kwon (63) confirmed that negative emotional experience along with the tendency to play games for mood modification positively predicted GD. Alexithymia, a difficulty describing feelings, and physical aggression also predicted GD symptoms (30, 31). Furthermore, T’ng et al. (65) reported that GD symptoms significantly predicted physical aggression, verbal aggression, anger, and hostility, while Wartberg et al. (62) found that GD significantly predicted subsequent emotional distress 1 year later. These suggest that GD is associated with a poor ability to manage negative emotional responses.

3.3.4. Personality traits

Personality traits are relatively stable characteristics of an individual. The Big Five personality traits are widely used–openness to new experiences, conscientiousness, extraversion, agreeableness, and neuroticism (115). High neuroticism has consistently been found to predict GD while there have been contrasting findings regarding other traits (73, 75, 76). With regards to other personality dimensions, Borzikowsky and Bernhardt (72) found that grit, the perseverance of effort, significantly reduced the GD likelihood, suggesting grit as a potential protective trait against GD. In addition, Müller et al. (74) confirmed that higher scores on maladaptive traits, such as negative affectivity, were significantly associated with GD. These suggest that high neuroticism is associated with GD while more studies are needed to confirm other predisposing traits related to GD.

3.3.5. Reward, discounting, and impulsivity

Wölfling et al. (69) performed a delay discounting task to investigate decision-making in individuals with GD and gambling disorder. They found that the former group showed a faster ability to adapt decision strategies than the latter while there was no significant correlation between GD severity and choice impulsivity. On the other hand, one study found a positive correlation between self-reported impulsivity levels and GD (68). Macur and Pontes (70) reported that gamers with a high GD risk presented significantly lower levels of self-control compared to low-risk gamers or non-gamers. In terms of reward-related decisions, Moudiab and Spada (71) found that overvaluing of gaming rewards predicted GD severity [c.f. (67)]. When combined with biological findings, these suggest that GD is associated with aberrant reward activities and cognition, while a specific pattern is yet to be confirmed.

3.3.6. Stress

All included studies confirmed a positive association between stress and GD (77, 79) and perceived stress predicted more GD symptoms (78).

3.3.7. Cognitive impairment

Bodi et al. (80) found that cognitive salience (e.g., planning what to do next in games) and completion (e.g., feeling the need to achieve objectives as soon as possible) were strong predictors of both online and offline gaming addiction. In addition, Efrati et al. (81) found a positive association between GD and metacognition, an awareness of one’s own thinking, which was mediated by thought suppression. This could signify that a lack of cognitive confidence and beliefs about the need to control thoughts are two metacognitions closely aligned with GD.

3.3.8. Self-concept

Concetta De Pasquale et al. (82) found a positive association between GD and dissociative experiences (e.g., depersonalization and derealization, absorption and imaginative involvement), suggesting that gamers’ predominant immersion in the virtual world could potentially lead to GD. Furthermore, Stavoropoulos et al. (116) found that problematic gaming was more prevalent in gamers highly fused with their game avatars compared to those who successfully differentiated themselves from their avatars. These add to the biological findings that suggest self-concept deficits in GD.

3.3.9. Sleep

Nakayama et al. (84) reported that problematic gamers had significantly later bedtime and wake-up time. Furthermore, Wang et al. (85) found that problematic gaming led individuals to feel that they have poorer sleep quality in general. These suggest the association between poor sleep quality and GD.

3.3.10. Miscellaneous factors

One study has investigated the relationship between GD and psychological flow, defined by the authors as “the feeling of enjoyment and pleasure arising from deep immersion in an activity” (86). They found that flow fully mediated the relationship between the preference for social games and GD, suggesting that GD behaviors may derive from a need to experience flow, especially in a social setting.

Another study has investigated the relationship between GD and need frustration (e.g., when somebody is excluded or rejected by others) (87). They found that game expectancies and time spent on games had a mediating effect on the relationship between the frustration of psychological needs and GD, suggesting that gamers’ need frustration may lead to a greater probability of experiencing GD.

3.3.11. COVID-19

School closures, lockdowns, and social distancing due to the COVID-19 pandemic have profoundly impacted the daily lives of people, leading to increased indoor activities. Time spent on online gaming (91, 93) as well as probable GD prevalence and GD symptoms (90) have constantly increased during the quarantine periods. A longitudinal study found that the levels of video game use and GD severity significantly increased in young populations during the pandemic, and depressive and anxiety symptom scores were associated with such videogame use (91, 92). In addition, COVID-19 related fear, COVID-19 infection status, and psychological distress were found to be associated with GD (90, 93). Loneliness, potentially stemming from the lockdown, was associated with GD behaviors as well (94). Regarding mediating roles, one study reported that GD was a mediator in the association between psychological distress and increased game time during the school hiatus (117). Another study found that psychological distress mediated the relationship between GD and insomnia and quality of life during the pandemic (89). Interestingly, GD behaviors were associated with higher levels of psychological distress only in children who were not overweight (88). These suggest a certain degree of impact COVID-19 has on GD, yet more studies are needed to confirm its long-term effects.

3.4. Social factors associated with GD

A total of 29 studies have investigated social factors related to GD (Table 3). Among them, nine studies investigated family-related variables. Eight papers investigated social interactions. Four investigated cultural factors and the other four investigated school-related factors. Lastly, four studies investigated social support.

3.4.1. Family

Bonnaire and Phan (95) found a significantly lower family cohesion, more family conflicts, and a poorer family relationship in problematic gamers compared to non-problematic gamers. Irmak and Erdogen (96) also found that negative family environments predicted GD behaviors but in females only. Sung et al. (101) reported that young adults with adverse childhood experiences had significantly higher GD tendencies compared to those without such experiences. These suggest the link between GD and a family environment and adverse childhood events.

The association between GD and the mental health of family members were often studied together (40, 59, 99) found that self-reported parental anxiety and depression were positively correlated with adolescents’ GD behaviors. Stockdale and Coyne (100) found a direct relationship between problematic gaming and parental efficacy, which was mediated by their depressive symptoms. Lin et al. (98) focused on the mental health of siblings and found that adolescents’ and their siblings’ GD behaviors had significant effects on each other’s depression and anxiety levels. These suggest a role that family members’ mental health plays in the development and maintenance of GD.

Several studies examined the relationship between parenting and GD. In a two-wave study, Koning et al. (97) found that, regardless of gender, GD symptoms predicted more internet-related reactive rules and lower communication quality. Among boys, more frequent internet-related communication predicted GD symptoms. Throuvala et al. (102) found a significant effect of perceived parental rejection on GD symptoms via the mediating factor of low core self-evaluations (e.g., low self-esteem). In a longitudinal study, Teng et al. (103) found that GD negatively predicted children’s subsequent attachment with parents, and the link was stronger in males. These findings suggest a role of parenting on GD, while gender effects are not consistent.

3.4.2. Social interaction

Two studies have reported a significant association between GD and lower levels of sociability as well as less perceived social support (41, 105). Interestingly, Duman and Ozkara (104) found that fear of missing out was a critical predictor of GD. Both the lack of tendency to engage in interpersonal relationships and the need to belong seem to be important factors of GD, although their potential interactions are yet to be studied.

Several studies have investigated social engagements in individuals with GD. In one study, participants were asked “How often do you neglect social contacts because of computer game playing?” and more frequent neglects were found to predict GD (40). Stavropoulos et al. (107) found a positive association between GD and symptoms of Hikikomori, prolonged self-imposed home isolation in addition to avoidance of social engagements. Similarly, De Pasquale et al. (33) reported a significant relationship between GD symptoms as well as a decrease in social relationships and the presence of difficulties in social adaptation. Tullett-Prado et al. (106) found that a high GD risk profile was associated with higher unemployment and a tendency to live with divorced parents or friends. Meanwhile, T’ng et al. (65) found that greater loneliness predicted GD symptoms. These suggest that GD is related to a lack of social engagement and feelings of loneliness.

3.4.3. Culture/ethnicity

Wittek et al. (76) conducted a national survey in Norway and found that individuals born in Africa, Asia, South- or Central America were 4.9 times more likely to belong to the GD group compared to those born in Norway. While it could be inferred that the participants from races/cultures mentioned in the study might experience a higher degree of GD as a result of their culture or race, it is equally plausible that these experiences are more attributable to the challenges they face in being accepted within the host culture. In terms of cultural orientations, Andreetta et al. (77) and Stavropoulos et al. (83) found a positive association between GD symptoms and vertical individualism, suggesting that gamers who endorse more individualistic cultural orientation potentially have a higher risk of GD. Similarly Stavropoulos et al. (49, 108), reported that gamers aversive to collectivism displayed more GD behaviors and addiction-related symptoms (e.g., withdrawal) compared to those who were neutral. These imply that individualistic cultural orientation is more related to GD compared to collectivism, while its association with specific ethnic groups is yet to be found.

3.4.4. School

Haghbin et al. (35) reported a significant negative relationship between GD and high school students’ grade point average. Along the same lines, in a two-wave longitudinal study, Brunborg et al. (109) found that GD at time point 1 was negatively correlated with academic achievement both at time points 1 and 2. Specifically, a 10% increase in GD symptoms was associated with a 1.7 point decrease in average grades. Wang et al. (110) also found that children with poor self-reported academic performance were significantly more likely to have GD compared to those with good academic performance.

In addition to academics, peer relations take up a significant proportion of children and adolescents’ school life. In the above study, they found that children who reported having more friends (7 or more) were more likely to have GD than others. On the other hand, Richard et al. (111) reported a significant relationship between bullying experiences and GD. These suggest that poor academic achievements are consistently found in GD, but the effect of peer relations needs further investigation.

3.4.5. Social support

No significant interdependencies between GD and perceived social support nor social capital was found (59, 112). Similarly, Stockdale and Coyne (50) found no differences in social support or feelings of companionship between individuals with and without GD. Contrary to these null findings, Ucur and Donmez (66) found that perceived social support was significantly lower in adolescents with GD. These suggest that there is little evidence that GD is related to negative changes in psychosocial status.

4. Discussion and conclusion

Using the PRISMA guidelines (19), we performed a systematic review to provide an up-to-date, comprehensive review of the empirical evidence of GD. We explored biopsychosocial factors of GD by systematically identifying studies that have utilized one of five GD screening tools that possess greater evidential support. Our review elucidated various factors in the biological, psychological, and social domains that were associated with GD. On the other hand, we have identified critical gaps in the literature related to study designs and assessment tool usage, in a way that most were cross-sectional and have utilized GD assessment tools with varying properties.

Notwithstanding the importance of neurobiological explorations in understanding a psychiatric condition, biological mechanisms underlying GD are relatively poorly understood (118, 119). Studies in the review compared reward activation, self-concept, brain structure, and functional connectivity between individuals with and without GD. GD was generally associated with reduced reward sensitivity to gaming-related rewards, heightened activation in the reward-related brain regions to gaming-related cues, and deficiencies in dopaminergic activities. However, one study included in the review suggested the absence of an association between reward sensitivity and gaming behaviors but rather found its association with harmful smartphone use, suggesting a possible effect of gaming method when investigating reward-related behaviors (67). These different results were in line with previous studies that have reported conflicting results regarding reward-related activations in individuals with GD (120, 121). In addition, reward networks in individuals with GD and those with other addictive disorders also yielded inconsistent results (122, 123). Although many studies in the review suggest certain reward patterns in GD, the lack of studies makes it difficult to draw a conclusion regarding the resemblance of GD to other well-defined addictive disorders.

In terms of self-concept in GD, the gamers’ tendency to identify with their game avatars increased as they transitioned from normal to problematic users. This follows the past review that reported self-concept deficits and increased identification with the gaming character in individuals with GD (124), further highlighting the discrepancy between the real self and the virtual self in GD. With regards to brain structure and connectivity, lower gray matter density in the left DLPFC and lower FC between bilateral orbitofrontal cortex and overall brain regions were observed but due to the lack of studies included, it is premature to establish these as a general pattern of the GD.

Most of the studies have investigated psychological aspects of GD, suggesting abnormalities in multiple domains. The most robust association was found between GD and other psychiatric conditions (e.g., ADHD, depression, and anxiety). Studies investigating the effect of COVID-19 on GD have also discovered a significant role depression and anxiety play in GD. This is in accordance with previous studies that have proposed comorbidities between GD and a range of psychological symptoms including anxiety, depression, and attention problems, both in adults and adolescents (125–127). While this could be suggestive of the relevant impact that mood disturbances and attention problems have on GD, this also raises a question as to whether GD possesses a unique profile or whether the symptoms stem from underlying conditions (128, 129). While a mere existence of comorbidities does not automatically explain the observed health conditions better, this still highlights the need to control for comorbidities when investigating unique features of the GD. In terms of overall psychological health, GD was associated with low self-esteem, low life satisfaction, high suicidality, high maladaptive personality traits, and high levels of stress yet direct casualties could not be drawn due to the cross-sectional designs of the studies.

Differential results were found with regards to cognitive approach and patterns. While higher impulsivity and maladaptive cognition were found to be associated with GD in one study (68), another study had found no such association and even found that individuals with GD adopted more advantageous decision strategies compared to those with gambling disorder or healthy individuals (69). The lack of consistency in the results contradicts many GD models that emphasize higher impulsivity and difficulties of decision-making (17, 130) as important features of GD. This warrants more studies to settle the accuracy of these models, especially since impulsivity and abnormal decision-making processes are considered significant features of addiction (131, 132).

For social factors, family members’ mental health, individualistic cultural orientation, and poor academic achievement were consistently found to be associated with GD. On the other hand, some noticeable variabilities were observed, specifically with regards to family-related factors. This included certain gender effects in terms of how family rules and attachment affect children’s GD symptoms as well as the directionality of the association between GD and family issues. Furthermore, it could not be concluded whether the family issues directly affect children’s GD, or whether there is a mediating factor concerning the traits of the children themselves. The contributions of social support and extra-family relationships to GD symptoms also yielded mixed results (e.g., having many friends vs. neglect of social contacts, low social support vs. no significant differences), making it difficult to conceptualize the role of social factors in the development and maintenance of GD. As family factors hugely impact children’s and adolescents’ development, studies should track temporal patterns of GD symptoms in order to unravel the complicated relationships (105).

Several gaps in the field of GD research were detected in the present review. First, more longitudinal and long-term follow-up studies are needed. Most studies included in this review were cross-sectional thus causal relationships nor predictive functions of each biopsychosocial variable could not be drawn. As mere correlations are not adequate to support a formalization of a disorder, a more sophisticated and deeper level of evidence of GD is needed (133). As one way to examine longitudinal aspects of gaming, we advise researchers to actively utilize large-scale projects, such as the ABCD [Adolescent Brain and Cognitive Development; (134)] study or the Project M.E.D.I.A (Media Effects on Development from Infancy to Adulthood; https://www.projectmediadenver.com/) to name a few, which are ongoing, longitudinal studies on child development.

Second, standardized approaches to GD assessment tools are essential. The problems that arise from the variability in GD screening tools in addition to the need for a unified approach have been highlighted by many researchers (13, 14, 135, 136). Even though we have made an attempt to control for these issues by only including studies that have utilized one of five scales with greater evidential support, we could not fully rule out the differences that exist between these five scales. Therefore, rather than constantly developing new diagnostic tools with varying conceptual properties, assessment consistency should initially be established to improve the comparability of different research studies.

Third, it was apparent from the review that there were not enough studies that have investigated biological factors of GD using screening tools of choice. During the literature search, we found that scales developed to measure general internet addiction rather than GD were being widely used in studies that collected neural and behavioral data, while it has constantly been argued that internet addiction should be conceptually and clinically distinguished from GD (137, 138). More studies focusing on the neurobiology and genetics of GD while using screening tools with evidential support or clinical interview are needed to uncover its underlying biological mechanisms. This will allow researchers to gain insight into the biological mechanisms, especially since animal models that have facilitated understanding of substance use disorders are lacking for GD (139).

Lastly, game-related factors (e.g., game genres, game time) should be taken into consideration when designing studies. It has been argued that the ‘social’ aspect of gaming is what leads to various problems instead of ‘gaming’ itself (140). As mixed and varying results were observed with regards to social factors, future studies should control for game genres as much as possible. Furthermore, there have been contrasting results regarding whether the amount of game time could be considered a reliable predictor of GD, questioning the idea that intense gaming itself is problematic (141). Colder Carras and Kardefelt-Winther (142) have also raised concerns that highly involved gamers could be misclassified simply due to the time they engage in gaming. Future studies should examine the association between game time and GD for more accurate clinical diagnostic criteria.

In conclusion, this was the first systematic review on GD to control for the excess amount of assessment tools with varying psychometric properties to provide an up-to-date and comprehensive overview of biopsychosocial factors associated with GD. While several biological, psychological, and social factors--impaired self-concept, comorbidities, emotional dysregulation, and poor academic performance--were consistently confirmed, mixed results were observed mainly with regards to reward activities, impulsivity, social relations, and family relationships. To settle the debate in terms of psychometric weaknesses of the current diagnostic system, collaborative approaches among experts in education, mental health, and the gaming industry seem crucial (143, 144). We conclude that more longitudinal and neurobiological studies, consensus on a diagnostic tool with well-defined psychometric properties, and an in-depth understanding of gaming-related factors should be established.

Funding

This work was supported by the Ministry of Health and Welfare, Republic of Korea and the Ministry of Culture, Sports and Tourism, Republic of Korea, a grant from the National Research Foundation (NRF) of Korea funded by the Korean government (NRF-2018R1C1B3007313), and the Creative-Pioneering Researchers Program through Seoul National University (W-YA).

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

• 1.

  FernandesG. Half a billion dollars in 2020: The cloud gaming market evolves as Consumer Engagement & Spending Soar. Newzoo. (2020). Available at: https://newzoo.com/insights/articles/global-cloud-gaming-market-report-consumer-engagement-spending-revenues-2020-2023/ (Accessed March 28, 2023).

  • Google Scholar

• 2.

  More People Are Gaming (2020). In the U.S., and They’re doing so across more platforms. npd. Available at: https://www.npd.com/news/press-releases/2020/more-people-are-gaming-in-the-us/ (Accessed March 28, 2023).

  • Google Scholar

• 3.

  FergusonCJ. Blazing angels or resident evil? Can violent video games be a force for good?Rev Gen Psychol. (2010) 14:68–81. doi: 10.1037/a0018941

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4.

  FergusonCJOlsonCK. Friends, fun, frustration and fantasy: child motivations for video game play. Motiv Emot. (2013) 37:154–64. doi: 10.1007/s11031-012-9284-7

  • CrossRef
  • Google Scholar

• 5.

  GranicILobelAEngelsRC. The benefits of playing video games. Am Psychol. (2014) 69:66. doi: 10.1037/a0034857

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6.

  SubletteVAMullanB. Consequences of play: A systematic review of the effects of online gaming. Int J Ment Heal Addict. (2012) 10:3–23. doi: 10.1007/s11469-010-9304-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7.

  American Psychiatric Association. Diagnostic and statistical manual of mental disorders, vol. DSM-5, 10. Washington, DC: American psychiatric association (2013).

  • Google Scholar

• 8.

  World Health Organization (2018). International classification of diseases for mortality and morbidity statistics (11th revision).

  • Google Scholar

• 9.

  HellmanMSchoenmakersTMNordstromBRVan HolstRJ. Is there such a thing as online video game addiction? A cross-disciplinary review. Addict Res Theory. (2013) 21:102–12. doi: 10.3109/16066359.2012.693222

  • CrossRef
  • Google Scholar

• 10.

  PetryNMRehbeinFGentileDALemmensJSRumpfHJMößleTet al. An international consensus for assessing internet gaming disorder using the new DSM-5 approach. Addiction. (2014) 109:1399–406. doi: 10.1111/add.12457

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11.

  KingDLChamberlainSRCarragherNBillieuxJSteinDMuellerKet al. Screening and assessment tools for gaming disorder: A comprehensive systematic review. Clin Psychol Rev. (2020) 77:101831. doi: 10.1016/j.cpr.2020.101831

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12.

  GomezRStavropoulosVBeardCPontesHM. Item response theory analysis of the recoded internet gaming disorder scale-short-form (IGDS9-SF). Int J Ment Heal Addict. (2019) 17:859–79. doi: 10.1007/s11469-018-9890-z

  • CrossRef
  • Google Scholar

• 13.

  KircaburunKPontesHMStavropoulosVGriffithsMD. A brief psychological overview of disordered gaming. Curr Opin Psychol. (2020) 36:38–43. doi: 10.1016/j.copsyc.2020.03.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14.

  StevensMWDorstynDDelfabbroPHKingDL. Global prevalence of gaming disorder: A systematic review and meta-analysis. Aust N Z J Psychiatry. (2021) 55:553–68. doi: 10.1177/0004867420962851

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15.

  YoonSYangYRoEAhnW-YKimJShinS-Het al. Reliability, and convergent and discriminant validity of gaming disorder scales: A Meta-analysis. Front Psychol. (2021) 12:764209. doi: 10.3389/fpsyg.2021.764209

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16.

  MiharaSHiguchiS. Cross-sectional and longitudinal epidemiological studies of I nternet gaming disorder: A systematic review of the literature. Psychiatry Clin Neurosci. (2017) 71:425–44. doi: 10.1111/pcn.12532

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17.

  PaulusFWOhmannSVon GontardAPopowC. Internet gaming disorder in children and adolescents: a systematic review. Dev Med Child Neurol. (2018) 60:645–59. doi: 10.1111/dmcn.13754

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 18.

  SugayaNShirasakaTTakahashiKKandaH. Bio-psychosocial factors of children and adolescents with internet gaming disorder: a systematic review. Bio Psycho Soc Med. (2019) 13:1–16. doi: 10.1186/s13030-019-0144-5

  • CrossRef
  • Google Scholar

• 19.

  LiberatiAAltmanDGTetzlaffJMulrowCGøtzschePCIoannidisJPet al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. (2009) 62:e1–e34. doi: 10.1016/j.jclinepi.2009.06.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20.

  ChiaDXYNgCWLKandasamiGSeowMYLChooCCChewPKHet al. Prevalence of internet addiction and gaming disorders in Southeast Asia: A Meta-analysis. Int J Environ Res Public Health. (2020) 17. doi: 10.3390/ijerph17072582

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21.

  KmetL. M.CookL. S.LeeR. C. (2004). Standard quality assessment criteria for evaluating primary research papers from a variety of fields.

  • Google Scholar

• 22.

  AriatamaBEffendyEAminMM. Relationship between internet gaming disorder with depressive syndrome and dopamine transporter condition in online games player. Open Access Macedonian J Med Sci. (2019) 7:2638–42. doi: 10.3889/oamjms.2019.476

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23.

  DuvenECPMüllerKWBeutelMEWölflingK. Altered reward processing in pathological computer gamers–ERP-results from a semi-natural gaming-design. Brain Behav. (2015) 5:e00293. doi: 10.1002/brb3.293

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24.

  TurelOHeQWeiLBecharaA. The role of the insula in internet gaming disorder. Addict Biol. (2021) 26:e12894. doi: 10.1111/adb.12894

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25.

  DieterJHillHSellMReinhardIVollstädt-KleinSKieferFet al. Avatar’s neurobiological traces in the self-concept of massively multiplayer online role-playing game (MMORPG) addicts. Behav Neurosci. (2015) 129:8. doi: 10.1037/bne0000025

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26.

  LemenagerTDieterJHillHHoffmannSReinhardIBeutelMet al. Exploring the neural basis of avatar identification in pathological internet gamers and of self-reflection in pathological social network users. J Behav Addict. (2016) 5:485–99. doi: 10.1556/2006.5.2016.048

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27.

  ChoiJChoHKimJ-YJungDJAhnKJKangH-Bet al. Structural alterations in the prefrontal cortex mediate the relationship between internet gaming disorder and depressed mood. Sci Rep. (2017) 7:1–10. doi: 10.1038/s41598-017-01275-5

  • CrossRef
  • Google Scholar

• 28.

  ChunJ-WParkC-HKimJ-YChoiJChoHJungDJet al. Altered core networks of brain connectivity and personality traits in internet gaming disorder. J Behav Addict. (2020). doi: 10.1556/2006.2020.00014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29.

  KimJ-YChunJ-WParkC-HChoHChoiJYangSet al. The correlation between the frontostriatal network and impulsivity in internet gaming disorder. Sci Rep. (2019) 9:1–9. doi: 10.1038/s41598-018-37702-4

  • CrossRef
  • Google Scholar

• 30.

  EvrenBEvrenCDalbudakETopcuMKutluN. Neuroticism and introversion mediates the relationship between probable ADHD and symptoms of internet gaming disorder: results of an online survey. Psychiatry Clin Psychopharmacol. (2019a) 29:90–6. doi: 10.1080/24750573.2018.1490095

  • CrossRef
  • Google Scholar

• 31.

  EvrenCEvrenBDalbudakETopcuMKutluN. Relationship of internet gaming disorder severity with symptoms of anxiety, depression, alexithymia, and aggression among university students. Dusunen Adam. (2019b) 32:227. doi: 10.14744/DAJPNS.2019.00032

  • CrossRef
  • Google Scholar

• 32.

  BonnaireCBaptistaD. Internet gaming disorder in male and female young adults: the role of alexithymia, depression, anxiety and gaming type. Psychiatry Res. (2019) 272:521–30. doi: 10.1016/j.psychres.2018.12.158

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33.

  De PasqualeCSciaccaFMartinelliVChiappediMDinaroCHichyZ. Relationship of internet gaming disorder with psychopathology and social adaptation in Italian young adults. Int J Environ Res Public Health. (2020) 17. doi: 10.3390/ijerph17218201

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34.

  ConcertoCRodolicoAAvanzatoCFusar-PoliLSignorelliMSBattagliaFet al. Autistic traits and attention-deficit hyperactivity disorder symptoms predict the severity of internet gaming disorder in an Italian adult population. Brain Sci. (2021) 11. doi: 10.3390/brainsci11060774

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35.

  HaghbinMShaterianFHosseinzadehDGriffithsMD. A brief report on the relationship between self-control, video game addiction and academic achievement in normal and ADHD students. J Behav Addict. (2013) 2:239–43. doi: 10.1556/JBA.2.2013.4.7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36.

  PontesHM. Investigating the differential effects of social networking site addiction and internet gaming disorder on psychological health. J Behav Addict. (2017) 6:601–10. doi: 10.1556/2006.6.2017.075

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37.

  HorváthZKirályODemetrovicsZNémethÁVárnaiDUrbánR. Polysubstance use is positively associated with gaming disorder symptom severity: A latent class analytical study. Eur Addict Res. (2021) 1-11. doi: 10.1159/000517042

  • CrossRef
  • Google Scholar

• 38.

  IsmailNTajjudinAIJaafarHNik JaafarNRBaharudinAIbrahimNet al. The relationship between internet addiction, internet gaming and anxiety among medical students in a Malaysian public university during COVID-19 pandemic. Int J Environ Res Pub Health. (2021) 18:11870. doi: 10.3390/ijerph182211870

  • CrossRef
  • Google Scholar

• 39.

  KircaburunKGriffithsMDBillieuxJ. Psychosocial factors mediating the relationship between childhood emotional trauma and internet gaming disorder: a pilot study. Eur J Psychotraumatol. (2019) 10:1565031. doi: 10.1080/20008198.2018.1565031

  • CrossRef
  • Google Scholar

• 40.

  WartbergLKristonLThomasiusR. The prevalence and psychosocial correlates of internet gaming disorder: analysis in a nationally representative sample of 12-to 25-year-olds. Dtsch Arztebl Int. (2017c) 114:419. doi: 10.3238/arztebl.2017.0419

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41.

  MännikköNBillieuxJKääriäinenM. Problematic digital gaming behavior and its relation to the psychological, social and physical health of Finnish adolescents and young adults. J Behav Addict. (2015) 4:281–8. doi: 10.1556/2006.4.2015.040

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42.

  MentzoniRABrunborgGSMoldeHMyrsethHSkouverøeKJMHetlandJet al. Problematic video game use: estimated prevalence and associations with mental and physical health. Cyberpsychol Behav Soc Netw. (2011) 14:591–6. doi: 10.1089/cyber.2010.0260

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43.

  MurrayAMannionAChenJLLeaderG. Gaming disorder in adults with autism spectrum disorder. J Autism Dev Disord. (2021). doi: 10.1007/s10803-021-05138-x

  • CrossRef
  • Google Scholar

• 44.

  MusettiAManciniTCorsanoPSantoroGCavalliniMCSchimmentiA. Maladaptive personality functioning and psychopathological symptoms in problematic video game players: A person-centered approach. Front Psychol. (2019) 10:2559. doi: 10.3389/fpsyg.2019.02559

  • CrossRef
  • Google Scholar

• 45.

  SeveroRBSoaresJMAffonsoJPGiustiDAde Souza JuniorAAde FigueiredoVLet al. Prevalence and risk factors for internet gaming disorder. Brazilian J Psychiatry. (2020) 42:532–5. doi: 10.1590/1516-4446-2019-0760

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 46.

  SinghSDahiyaNSinghABKumarRBalharaYPS. Gaming disorder among medical college students from India: exploring the pattern and correlates. Ind Psychiatry J. (2019) 28:107. doi: 10.4103/ipj.ipj_96_18

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 47.

  SisteKHanafiESenLTWahjoepramonoPOPKurniawanAYudistiroR. Potential correlates of internet gaming disorder among Indonesian medical students: cross-sectional study. J Med Internet Res. (2021) 23:e25468. doi: 10.2196/25468

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 48.

  StavropoulosVAdamsBLMBeardCLDumbleETrawleySGomezRet al. Associations between attention deficit hyperactivity and internet gaming disorder symptoms: is there consistency across types of symptoms, gender and countries?Addict Behav Rep. (2019a) 9:100158. doi: 10.1016/j.abrep.2018.100158

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49.

  StavropoulosVVassalloJBurleighTLGomezRColder CarrasM. The role of internet gaming in the association between anxiety and depression: A preliminary cross-sectional study. Asia Pac Psychiatry. (2021b) e12474. doi: 10.1111/appy.12474

  • CrossRef
  • Google Scholar

• 50.

  StockdaleLCoyneSM. Video game addiction in emerging adulthood: cross-sectional evidence of pathology in video game addicts as compared to matched healthy controls. J Affect Disord. (2018) 225:265–72. doi: 10.1016/j.jad.2017.08.045

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51.

  Turhan GürbüzPÇobanÖErdoğanAKopuzHYAdanirASÖnderA. Evaluation of internet gaming disorder, social media addiction, and levels of loneliness in adolescents and youth with substance use. Subst Use Misuse. (2021) 1-6. doi: 10.1080/10826084.2021.1958856

  • CrossRef
  • Google Scholar

• 52.

  VallyZ. Symptoms of internet gaming disorder, inattention, and impulsivity: a cross-sectional study conducted in the United Arab Emirates. Psychiatry Q. (2021) 92:301–10. doi: 10.1007/s11126-020-09799-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53.

  ChoiW-S. The mediating effect of anxiety on the relationship between internet game addiction and life satisfaction: focusing on male university students in Harbin, China. Korean J Fam Welfare. (2018) 23:613–32. doi: 10.13049/kfwa.2018.23.4.4

  • CrossRef
  • Google Scholar

• 54.

  WongHYMoHYPotenzaMNChanMNMLauWMChuiTKet al. Relationships between severity of internet gaming disorder, severity of problematic social media use, sleep quality and psychological distress. Int J Environ Res Public Health. (2020) 17:1879. doi: 10.3390/ijerph17061879

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55.

  Buiza-AguadoCAlonso-CanovasAConde-MateosCBuiza-NavarreteJJGentileD. Problematic video gaming in a young Spanish population: association with psychosocial health. Cyberpsychol Behav Soc Netw. (2018) 21:388–94. doi: 10.1089/cyber.2017.0599

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 56.

  KimCKJungDKChoMK. Structural analysis among university Students' intrusive rumination, deliberate rumination, game addiction tendency and suicidal risk: multi-group analysis according to gender. J Emot Behav Disord. (2021) 37:283–307. doi: 10.33770/JEBD.37.1.13

  • CrossRef
  • Google Scholar

• 57.

  EvrenCEvrenBDalbudakETopcuMKutluN. Relationship of internet gaming disorder symptom severity with non-suicidal self-injury among young adults. Dusunen Adam. (2020) 33:79–86. doi: 10.14744/DAJPNS.2019.00063

  • CrossRef
  • Google Scholar

• 58.

  WartbergLZieglmeierMKammerlR. An empirical exploration of longitudinal predictors for problematic internet use and problematic gaming behavior. Psychol Rep. (2021) 124:543–54. doi: 10.1177/0033294120913488

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59.

  WartbergLKristonLKammerlR. Associations of social support, friends only known through the internet, and health-related quality of life with internet gaming disorder in adolescence. Cyberpsychol Behav Soc Netw. (2017a) 20:436–41. doi: 10.1089/cyber.2016.0535

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 60.

  PhanOPrieurCBonnaireCObradovicI. Internet gaming disorder: exploring its impact on satisfaction in life in PELLEAS adolescent sample. Int J Environ Res Public Health. (2020) 17:3. doi: 10.3390/ijerph17010003

  • CrossRef
  • Google Scholar

• 61.

  TengZPontesHMNieQXiangGGriffithsMDGuoC. Internet gaming disorder and psychosocial well-being: A longitudinal study of older-aged adolescents and emerging adults. Addict Behav. (2020b) 110:106530. doi: 10.1016/j.addbeh.2020.106530

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 62.

  WartbergLKristonLZieglmeierMLincolnTKammerlR. A longitudinal study on psychosocial causes and consequences of internet gaming disorder in adolescence. Psychol Med. (2019) 49:287–94. doi: 10.1017/S003329171800082X

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63.

  KimSKwonJ-H. Effects of emotion experiences and gaming motivations on internet gaming behaviors in daily life: an ecological momentary assessment study. Korean J Clin Psychol. (2018) 37:573–89. doi: 10.15842/kjcp.2018.37.4.009

  • CrossRef
  • Google Scholar

• 64.

  MüllerTBonnaireC. Intrapersonal and interpersonal emotion regulation and identity: A preliminary study of avatar identification and gaming in adolescents and young adults. Psychiatry Res. (2021) 295:113627. doi: 10.1016/j.psychres.2020.113627

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 65.

  T'ngSTHoKHSimDEYuCHWongPY. The mediating effect of internet gaming disorder's symptoms on loneliness and aggression among undergraduate students and working adults in Malaysia. PsyCh J. (2020) 9:96–107. doi: 10.1002/pchj.320

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 66.

  UçurÖDönmezYE. Problematic internet gaming in adolescents, and its relationship with emotional regulation and perceived social support. Psychiatry Res. (2021) 296:113678. doi: 10.1016/j.psychres.2020.113678

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 67.

  AcuffSFPilattiACollinsMHidesLThingujamNSChaiWJet al. Reinforcer pathology of internet-related behaviors among college students: data from six countries. Exp Clin Psychopharmacol. (2021). doi: 10.1037/pha0000459

  • CrossRef
  • Google Scholar

• 68.

  CernigliaLGuicciardiMSinatraMMonacisLSimonelliACiminoS. The use of digital technologies, impulsivity and psychopathological symptoms in adolescence. Behav Sci. (2019) 9:82. doi: 10.3390/bs9080082

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69.

  WölflingKDuvenEWejberaMBeutelMEMüllerKW. Discounting delayed monetary rewards and decision making in behavioral addictions–A comparison between patients with gambling disorder and internet gaming disorder. Addict Behav. (2020) 108:106446. doi: 10.1016/j.addbeh.2020.106446

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70.

  MacurMPontesHM. Internet gaming disorder in adolescence: investigating profiles and associated risk factors. BMC Public Health. (2021) 21:1547. doi: 10.1186/s12889-021-11394-4

  • CrossRef
  • Google Scholar

• 71.

  MoudiabSSpadaMM. The relative contribution of motives and maladaptive cognitions to levels of internet gaming disorder. Addict Behav Rep. (2019) 9:100160. doi: 10.1016/j.abrep.2019.100160

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72.

  BorzikowskyCBernhardtF. Lost in virtual gaming worlds: grit and its prognostic value for online game addiction. Am J Addict. (2018) 27:433–8. doi: 10.1111/ajad.12762

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 73.

  MüllerKWBeutelMEEgloffBWölflingK. Investigating risk factors for internet gaming disorder: a comparison of patients with addictive gaming, pathological gamblers and healthy controls regarding the big five personality traits. Eur Addict Res. (2014) 20:129–36. doi: 10.1159/000355832

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74.

  MüllerKWWerthmannJBeutelMEWölflingKEgloffB. Maladaptive personality traits and their interaction with outcome expectancies in gaming disorder and internet-related disorders. Int J Environ Res Public Health. (2021) 18. doi: 10.3390/ijerph18083967

  • CrossRef
  • Google Scholar

• 75.

  Sánchez-LlorensMMarí-SanmillánMIBenitoARodríguez-RuizFCastellano-GarcíaFAlmodóvarIet al. Personality traits and psychopathology in adolescents with videogame addiction. Adicciones. (2021) 35:1629. doi: 10.20882/adicciones.1629

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76.

  WittekCTFinseråsTRPallesenSMentzoniRAHanssDGriffithsMDet al. Prevalence and predictors of video game addiction: A study based on a national representative sample of gamers. Int J Ment Heal Addict. (2016) 14:672–86. doi: 10.1007/s11469-015-9592-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 77.

  AndreettaJTehJBurleighTLGomezRStavropoulosV. Associations between comorbid stress and internet gaming disorder symptoms: are there cultural and gender variations?Asia Pac Psychiatry. (2020) 12:e12387. doi: 10.1111/appy.12387

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 78.

  CanaleNMarinoCGriffithsMDScacchiLMonaciMGVienoA. The association between problematic online gaming and perceived stress: the moderating effect of psychological resilience. J Behav Addict. (2019) 8:174–80. doi: 10.1556/2006.8.2019.01

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79.

  RajabAMZaghloulMSEnabiSRajabTMAl-KhaniAMBasalahAet al. Gaming addiction and perceived stress among Saudi adolescents. Addict Behav Rep. (2020) 11:100261. doi: 10.1016/j.abrep.2020.100261

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80.

  BodiGMaintenantCPennequinV. The role of maladaptive cognitions in gaming disorder: differences between online and offline gaming types. Addict Behav. (2021) 112:106595. doi: 10.1016/j.addbeh.2020.106595

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81.

  EfratiYKolubinskiDCMarinoCSpadaMM. Modelling the contribution of metacognitions, impulsiveness, and thought suppression to Behavioural addictions in adolescents. Int J Environ Res Public Health. (2021) 18. doi: 10.3390/ijerph18073820

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 82.

  De PasqualeCDinaroCSciaccaF. Relationship of internet gaming disorder with dissociative experience in Italian university students. Ann General Psychiatry. (2018) 17:1–7. doi: 10.1186/s12991-018-0198-y

  • CrossRef
  • Google Scholar

• 83.

  StavropoulosVBaynesKLO’FarrelDLGomezRMuellerAYucelMet al. Inattention and disordered gaming: does culture matter?Psychiatry Q. (2020a) 91:333–48. doi: 10.1007/s11126-019-09702-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84.

  NakayamaHMatsuzakiTMiharaSKitayuguchiTHiguchiS. Relationship between problematic gaming and age at the onset of habitual gaming. Pediatr Int. (2020) 62:1275–81. doi: 10.1111/ped.14290

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 85.

  WangQMatiKCaiY. The link between problematic internet use, problematic gaming, and psychological distress: does sleep quality matter?BMC Psychiatry. (2021) 21:103. doi: 10.1186/s12888-021-03105-5

  • CrossRef
  • Google Scholar

• 86.

  HuEStavropoulosVAndersonAScerriMCollardJ. Internet gaming disorder: feeling the flow of social games. Addict Behav Rep. (2019) 9:100140. doi: 10.1016/j.abrep.2018.10.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87.

  ChamarroAOberstUCladellasRFusterH. Effect of the frustration of psychological needs on addictive behaviors in Mobile Videogamers—the mediating role of use expectancies and time spent gaming. Int J Environ Res Public Health. (2020) 17:6429. doi: 10.3390/ijerph17176429

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88.

  ChenCYChenIHO'BrienKSLatnerJDLinCY. Psychological distress and internet-related behaviors between schoolchildren with and without overweight during the COVID-19 outbreak. Int J Obes. (2021a) 45:677–86. doi: 10.1038/s41366-021-00741-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89.

  FazeliSZeidiIMLinC-YNamdarPGriffithsMDAhorsuDKet al. Depression, anxiety, and stress mediate the associations between internet gaming disorder, insomnia, and quality of life during the COVID-19 outbreak. Addict Behav Rep. (2020) 12:100307. doi: 10.1016/j.abrep.2020.100307

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90.

  OkaTHamamuraTMiyakeYKobayashiNHonjoMKawatoMet al. Prevalence and risk factors of internet gaming disorder and problematic internet use before and during the COVID-19 pandemic: A large online survey of Japanese adults. J Psychiatr Res. (2021) 142:218–25. doi: 10.1016/j.jpsychires.2021.07.054

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91.

  SallieSNRitouVJEBowden-JonesHVoonV. Assessing online gaming and pornography consumption patterns during COVID-19 isolation using an online survey: highlighting distinct avenues of problematic internet behavior. Addict Behav. (2021) 123:107044. doi: 10.1016/j.addbeh.2021.107044

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92.

  TengZPontesHMNieQGriffithsMDGuoC. Depression and anxiety symptoms associated with internet gaming disorder before and during the COVID-19 pandemic: A longitudinal study. J Behav Addict. (2021). doi: 10.1556/2006.2021.00016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93.

  TingCHEssauC. Addictive behaviours among university students in Malaysia during COVID-19 pandemic. Addict Behav Rep. (2021) 14:100375. doi: 10.1016/j.abrep.2021.100375

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94.

  ZhuSZhuangYLeePLiJC-MWongPWC. Leisure and problem gaming behaviors among children and adolescents during school closures caused by COVID-19 in Hong Kong: quantitative cross-sectional survey study. JMIR Serious Games. (2021) 9. doi: 10.2196/26808

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95.

  BonnaireCPhanO. Relationships between parental attitudes, family functioning and internet gaming disorder in adolescents attending school. Psychiatry Res. (2017) 255:104–10. doi: 10.1016/j.psychres.2017.05.030

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96.

  IrmakAYErdoganS. Predictors for digital game addiction among Turkish adolescents: a Cox’s interaction model-based study. J Addict Nurs. (2019) 30:49–56. doi: 10.1097/JAN.0000000000000265

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97.

  KoningIMPeetersMFinkenauerCVan Den EijndenRJJM. Bidirectional effects of internet-specific parenting practices and compulsive social media and internet game use. J Behav Addict. (2018) 7:624–32. doi: 10.1556/2006.7.2018.68

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98.

  LinCYPotenzaMNBroströmAPakpourAH. Internet gaming disorder, psychological distress, and insomnia in adolescent students and their siblings: an actor-partner interdependence model approach. Addict Behav Rep. (2021) 13:100332. doi: 10.1016/j.abrep.2020.100332

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99.

  WartbergLKristonLKramerMSchwedlerALincolnTMKammerlR. Internet gaming disorder in early adolescence: associations with parental and adolescent mental health. Eur Psychiatry. (2017b) 43:14–8. doi: 10.1016/j.eurpsy.2016.12.013

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100.

  StockdaleLCoyneSM. Parenting paused: pathological video game use and parenting outcomes. Addict Behav Rep. (2020) 11:100244. doi: 10.1016/j.abrep.2019.100244

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 101.

  SungJ-HKimC-KChoM-K. A structural analysis among intrusive rumination, externalizing and game addiction tendency: multi-group analysis according to adverse childhood experiences. Korean Assoc Learner-Cent Curriculum Instruction. (2020) 20:471–91. doi: 10.22251/jlcci.2020.20.21.471

  • CrossRef
  • Google Scholar

• 102.

  ThrouvalaMAJanikianMGriffithsMDRennoldsonMKussDJ. The role of family and personality traits in internet gaming disorder: A mediation model combining cognitive and attachment perspectives. J Behav Addict. (2019) 8:48–62. doi: 10.1556/2006.8.2019.05

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 103.

  TengZGriffithsMDNieQXiangGGuoC. Parent–adolescent attachment and peer attachment associated with internet gaming disorder: A longitudinal study of first-year undergraduate students. J Behav Addict. (2020a) 9:116–28. doi: 10.1556/2006.2020.00011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 104.

  DumanHOzkaraBY. The impact of social identity on online game addiction: the mediating role of the fear of missing out (FoMO) and the moderating role of the need to belong. Curr Psychol. (2019) 1-10. doi: 10.1007/s12144-019-00392-w

  • CrossRef
  • Google Scholar

• 105.

  FestlRScharkowMQuandtT. Problematic computer game use among adolescents, younger and older adults. Addiction. (2013) 108:592–9. doi: 10.1111/add.12016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 106.

  Tullett-PradoDStavropoulosVMuellerKSharplesJFootittTA. Internet gaming disorder profiles and their associations with social engagement behaviours. J Psychiatr Res. (2021) 138:393–403. doi: 10.1016/j.jpsychires.2021.04.037

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 107.

  StavropoulosVAndersonEEBeardCLatifiMQKussDGriffithsM. A preliminary cross-cultural study of hikikomori and internet gaming disorder: the moderating effects of game-playing time and living with parents. Addict Behav Rep. (2019b) 9:100137. doi: 10.1016/j.abrep.2018.10.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 108.

  StavropoulosVFrostTMJBrownTGillPFootittTAKannis-DymandL. Internet gaming disorder behaviours: a preliminary exploration of individualism and collectivism profiles. BMC Psychiatry. (2021a) 21:262. doi: 10.1186/s12888-021-03245-8

  • CrossRef
  • Google Scholar

• 109.

  BrunborgGSMentzoniRAFrøylandLR. Is video gaming, or video game addiction, associated with depression, academic achievement, heavy episodic drinking, or conduct problems?J Behav Addict. (2014) 3:27. doi: 10.1556/JBA.3.2014.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 110.

  WangC-WChanCLWMakK-KHoS-YWongPWCHoRTH. Prevalence and correlates of video and internet gaming addiction among Hong Kong adolescents: A pilot study. Sci World J. (2014) 2014. doi: 10.1155/2014/874648

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111.

  RichardJMarchicaLIvoskaWDerevenskyJ. Bullying victimization and problem video gaming: the mediating role of externalizing and internalizing problems. Int J Environ Res Public Health. (2021) 18. doi: 10.3390/ijerph18041930

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 112.

  ScharkowMFestlRQuandtT. Longitudinal patterns of problematic computer game use among adolescents and adults—A 2-year panel study. Addiction. (2014) 109:1910–7. doi: 10.1111/add.12662

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113.

  DecetyJGrèzesJ. The power of simulation: imagining one's own and other's behavior. Brain Res. (2006) 1079:4–14. doi: 10.1016/j.brainres.2005.12.115

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 114.

  GaneshSVan SchieHTDe LangeFPThompsonEWigboldusDH. How the human brain goes virtual: distinct cortical regions of the person-processing network are involved in self-identification with virtual agents. Cereb Cortex. (2012) 22:1577–85. doi: 10.1093/cercor/bhr227

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115.

  CostaPTMcCraeR. Professional manual for the NEO PI-R and NEO-Ffi. Odessa, FL: Psychological Assessment Resources (1992).

  • Google Scholar

• 116.

  StavropoulosVGomezRMuellerAYucelMGriffithsM. User-avatar bond profiles: how do they associate with disordered gaming?Addict Behav. (2020b) 103:106245. doi: 10.1016/j.addbeh.2019.106245

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 117.

  ChenCYChenIHPakpourAHLinCYGriffithsMD. Internet-related behaviors and psychological distress among schoolchildren during the COVID-19 school Hiatus. Cyberpsychol Behav Soc Netw. (2021b). doi: 10.1089/cyber.2020.0497

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 118.

  Fauth-BühlerMMannK. Neurobiological correlates of internet gaming disorder: similarities to pathological gambling. Addict Behav. (2017) 64:349–56. doi: 10.1016/j.addbeh.2015.11.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 119.

  PontesHMGriffithsMD. Assessment of internet gaming disorder in clinical research: past and present perspectives. Clin Res Regul Aff. (2014) 31:35–48. doi: 10.3109/10601333.2014.962748

  • CrossRef
  • Google Scholar

• 120.

  AntonsSBrandMPotenzaMN. Neurobiology of cue-reactivity, craving, and inhibitory control in non-substance addictive behaviors. J Neurol Sci. (2020) 415:116952. doi: 10.1016/j.jns.2020.116952

  • CrossRef
  • Google Scholar

• 121.

  ParkBHanDHRohS. Neurobiological findings related to internet use disorders. Psychiatry Clin Neurosci. (2017) 71:467–78. doi: 10.1111/pcn.12422

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 122.

  LeemanRFPotenzaMN. A targeted review of the neurobiology and genetics of Behavioural addictions: an emerging area of research. Can J Psychiatr. (2013) 58:260–73. doi: 10.1177/070674371305800503

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 123.

  WeinsteinALivnyAWeizmanA. New developments in brain research of internet and gaming disorder. Neurosci Biobehav Rev. (2017) 75:314–30. doi: 10.1016/j.neubiorev.2017.01.040

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 124.

  LemenagerTNeissnerMSaboTMannKKieferF. Who am I and how should I be: a systematic review on self-concept and avatar identification in gaming disorder. Curr Addict Rep. (2020) 7:166–93. doi: 10.1007/s40429-020-00307-x

  • CrossRef
  • Google Scholar

• 125.

  MüllerKWJanikianMDreierMWölflingKBeutelMETzavaraCet al. Regular gaming behavior and internet gaming disorder in European adolescents: results from a cross-national representative survey of prevalence, predictors, and psychopathological correlates. Eur Child Adolesc Psychiatry. (2015) 24:565–74. doi: 10.1007/s00787-014-0611-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 126.

  OstinelliEGZanganiCGiordanoBMaestriDGambiniOD'AgostinoAet al. Depressive symptoms and depression in individuals with internet gaming disorder: A systematic review and meta-analysis. J Affect Disord. (2021) 284:136–42. doi: 10.1016/j.jad.2021.02.014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 127.

  YenJ-YLiuT-LWangP-WChenC-SYenC-FKoC-H. Association between internet gaming disorder and adult attention deficit and hyperactivity disorder and their correlates: impulsivity and hostility. Addict Behav. (2017) 64:308–13. doi: 10.1016/j.addbeh.2016.04.024

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 128.

  BeanAMNielsenRKVan RooijAJFergusonCJ. Video game addiction: the push to pathologize video games. Prof Psychol Res Pract. (2017) 48:378. doi: 10.1037/pro0000150

  • CrossRef
  • Google Scholar

• 129.

  MüllerKWWölflingK. Both sides of the story: addiction is not a pastime activity: commentary on: scholars’ open debate paper on the World Health Organization ICD-11 gaming disorder proposal (Aarseth et al.). J Behav Addict. (2017) 6:118–20. doi: 10.1556/2006.6.2017.038

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 130.

  ŞalvarlıŞİGriffithsMD. The association between internet gaming disorder and impulsivity: A systematic review of literature. Int J Ment Heal Addict. (2019). doi: 10.1007/s11469-019-00126-w

  • CrossRef
  • Google Scholar

• 131.

  BickelWKMellisAMSniderSEAthamnehLNSteinJSPopeDA. 21st century neurobehavioral theories of decision making in addiction: review and evaluation. Pharmacol Biochem Behav. (2018) 164:4–21. doi: 10.1016/j.pbb.2017.09.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 132.

  LeeRSCHoppenbrouwersSFrankenI. A systematic Meta-review of impulsivity and compulsivity in addictive behaviors. Neuropsychol Rev. (2019) 29:14–26. doi: 10.1007/s11065-019-09402-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133.

  Van RooijAJFergusonCJColder CarrasMKardefelt-WintherDShiJAarsethEet al. A weak scientific basis for gaming disorder: let us err on the side of caution. J Behav Addict. (2018) 7:1–9. doi: 10.1556/2006.7.2018.19

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 134.

  BarchDMAlbaughMDAvenevoliSChangLClarkDBGlantzMDet al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: rationale and description. Dev Cogn Neurosci. (2018) 32:55–66. doi: 10.1016/j.dcn.2017.10.010

  • CrossRef
  • Google Scholar

• 135.

  GriffithsMKingDDemetrovicsZ. DSM-5 internet gaming disorder needs a unified approach to assessment. Neuropsychiatry. (2014) 4:1–4. doi: 10.2217/npy.13.82

  • CrossRef
  • Google Scholar

• 136.

  StavropoulosVGomezRMotti-StefanidiF. Editorial: internet gaming disorder: A pathway towards assessment consensus. Front Psychol. (2019c) 10:1822. doi: 10.3389/fpsyg.2019.01822

  • CrossRef
  • Google Scholar

• 137.

  BalharaYPSSinghSSainiRDahiyaNSinghABKumarR. Should internet gaming disorder be considered a subtype of generalized problematic internet use? Findings from a study among medical college students. Perspect Psychiatr Care. (2021) 57:272–8. doi: 10.1111/ppc.12558

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 138.

  MontagCBeyKShaPLiMChenY-FLiuW-Yet al. Is it meaningful to distinguish between generalized and specific internet addiction? Evidence from a cross-cultural study from Germany, Sweden, Taiwan and China. Asia-Pacific Psychiatry. (2015) 7:20–6. doi: 10.1111/appy.12122

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 139.

  FengWRamoDEChanSRBourgeoisJA. Internet gaming disorder: Trends in prevalence 1998-2016. Addict Behav. (2017) 75:17–24. doi: 10.1016/j.addbeh.2017.06.010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 140.

  GongXZhangKZKChenCCheungCMKLeeMKO. Antecedents and consequences of excessive online social gaming: a social learning perspective. Inf Technol People. (2020) 33:657–88. doi: 10.1108/ITP-03-2018-0138

  • CrossRef
  • Google Scholar

• 141.

  KirályOTóthDUrbánRDemetrovicsZMarazA. Intense video gaming is not essentially problematic. Psychol Addict Behav. (2017) 31:807–17. doi: 10.1037/adb0000316

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 142.

  Colder CarrasMKardefelt-WintherD. When addiction symptoms and life problems diverge: a latent class analysis of problematic gaming in a representative multinational sample of European adolescents. Eur Child Adolesc Psychiatry. (2018) 27:513–25. doi: 10.1007/s00787-018-1108-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 143.

  Colder CarrasMPorterAMVan RooijAJKingDLangeACarrasMet al. Gamers’ insights into the phenomenology of normal gaming and game addiction: A mixed methods study. Comput Hum Behav. (2018) 79:238–46. doi: 10.1016/j.chb.2017.10.029

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 144.

  ZhaoMHaoW. Challenges of gaming disorder: suggestions from a public health perspective. Gen Psychiatry. (2019) 32. doi: 10.1136/gpsych-2019-100086

  • CrossRef
  • Google Scholar