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ORIGINAL ARTICLE
Year : 2019  |  Volume : 28  |  Issue : 1  |  Page : 107-114  Table of Contents     

Gaming disorder among medical college students from India: Exploring the pattern and correlates


1 Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India
2 Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India
3 Department of Community of Medicine, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
4 Department of Community Medicine, Maulana Azad Medical College, New Delhi, India
5 Department of Psychiatry, National Drug Dependence Treatment Centre, Behavioral Addictions Clinic, All India Institute of Medical Sciences, New Delhi, India

Date of Submission25-Dec-2018
Date of Acceptance19-Aug-2019
Date of Web Publication11-Dec-2019

Correspondence Address:
Dr. Yatan Pal Singh Balhara
Department of Psychiatry, National Drug Dependence Treatment Centre, Behavioral Addictions Clinic, All India Institute of Medical Sciences, New Delhi
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ipj.ipj_96_18

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   Abstract 


Background: In the extant literature, apart from few published case reports describing patients with severe form of gaming disorder (GD), there is a lack of studies describing the pattern and correlates of GD existing in the Indian settings. Thus, the present study aimed to explore the extent and pattern of gaming behavior in a sample of medical college students from India and explore its association with the sociodemographic, psychological (depressive symptoms), and Internet gaming characteristics. Materials and Methods: This Internet-based cross-sectional study was conducted as an online survey among 306 medical students by the Behavioral Addictions Clinic at a tertiary care teaching college in India. The severity of problematic gaming behavior and depressive symptoms was assessed using the Internet GD Scale-Short Form (IGDS9-SF) and Patient Health Questionnaire-9 (PHQ-9). A semi-structured questionnaire was used to collect information regarding sociodemographic and Internet gaming characteristics of the participants. Statistical analysis was done using SPSS software version 21.0, with two-tailed P < 0.05 taken as significant and P < 0.01 as highly significant results. Results: We identified 173 (55.6%) current gamers, with 11 (3.6%) Internet GD gamers based on the Diagnostic and Statistical Manual of Mental Disorders -5 criteria in the current study sample. A preference for multiplayer online gaming pattern (β =0.17, P= 0.005), spending greater amount of time in playing digital games (β = 0.53, P < 0.01), and higher PHQ-9 scores (β =0.25, P < 0.01**) representing greater depressive symptom severity were associated with statistically significantly greater scores on the IGDS9-SF, indicative of a higher risk for having GD. Conclusions: GD is a cause of concern among medical students in India. There is an urgent need to create awareness about it among students and concerned authorities. Further, there is a need to develop effective screening and treatment strategies suited for our population. The risk factors identified in the current study can be utilized to screen those at high risk of developing the same.

Keywords: Depression, gaming disorder, Internet Gaming Disorder Scale-Short Form, medical students, multiplayer online games


How to cite this article:
Singh S, Dahiya N, Singh AB, Kumar R, Balhara YP. Gaming disorder among medical college students from India: Exploring the pattern and correlates. Ind Psychiatry J 2019;28:107-14

How to cite this URL:
Singh S, Dahiya N, Singh AB, Kumar R, Balhara YP. Gaming disorder among medical college students from India: Exploring the pattern and correlates. Ind Psychiatry J [serial online] 2019 [cited 2020 Jan 24];28:107-14. Available from: http://www.industrialpsychiatry.org/text.asp?2019/28/1/107/272704



The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), describes gaming disorder (GD) (mentioned as Internet GD [IGD]) as a persistent and recurrent pattern of playing digital or video games (both online and offline), leading to a clinically significant distress or impairment.[1] The advances in the field of technology including the gaming industry; the advent of high-tech handheld gaming devices such as smartphones, gaming consoles, or tablets; and the increased penetration of the Internet that is accessible at an increasingly affordable price have made gaming more engaging, attractive, accessible, and affordable.[2] Although gaming is a harmless leisure activity for most players, at least a subset of them experiences one of the more adverse consequences consequent to engagement in this behavior. The published literature has documented physical, psychological, social, and work-related problems such as disturbed sleep pattern, dehydration, pressure sores, increased irritability and aggression, depressive and/or anxiety symptoms, poor academic performance, and neglect of interpersonal relationships and work-related commitments among persons with excessive and problematic gaming.[3] Publications have reported cases of game-induced seizures and even death reported among individuals with severe GD.[4]

GD (IGD) was included in the DSM-5 as a condition warranting further study in 2013. More recently, the World Health Organization has also included “gaming disorder” as a mental health disorder in the upcoming 11th revision of the International Classification of Diseases-11.[5]

A recent market survey reported that India is currently ranked fifth among the list of top countries by game downloads globally.[6] Further, with the world's second-largest online population and the fastest growing smartphone user base, India is expected to surpass other countries and become one of the largest markets for gaming in the near future. This makes a subset of the population of India, especially the young and adolescents, extremely vulnerable to the harms associated with excessive digital gaming. However, in the extant literature, apart from few case reports describing persons with severe form of GD,[7],[8] there is a lack of studies documenting the pattern and correlates of GD in the Indian settings.

While GDs have been explored among different population groups, published literature on the pattern and correlates of gaming behavior among medical students remains scarce.[9] The present study aimed at exploring the pattern and correlates of gaming among a sample of medical college students from India. Medical students were chosen for this study as the presence of GD in this population could have a deleterious effect on their academic performance and professional career subsequently. Further, the study shall be helpful as it shall add valuable information to the limited pool of published literature on this theme among medical students.


   Materials and Methods Top


Study participants and procedure

This was an Internet-based, cross-sectional study conducted as an online survey among medical students pursuing their medical course. The study was part of a larger study that explored the pattern of Internet use among university students. The findings pertaining to the pattern and correlated to gaming behavior among medical students have been reported here.

The study participants were medical students pursuing their degree course in medicine, those in medical internship, or those pursuing residency in a medical specialty. They were all able to read, comprehend, and respond in English. All the eligible participants were approached for participation in the study. These settings included the lecture theaters, academic discussion groups, and departmental settings. All the students present during the data collection were approached by one of the authors. This was done keeping in mind the variability in attendance across various settings in medical colleges. The students were explained about the purpose of the study and the voluntary nature of participation in the survey. They were also assured regarding the maintenance of confidentiality and anonymity of the information provided by them during the study. Those consenting to participate in the study were invited to fill the online study questionnaire via a link shared with them. The students who participated in the survey were asked to share the link to the online study questionnaire with their acquaintance pursuing their degree course in medicine, doing medical internship, or pursuing residency in a medical specialty. This was done with an aim to reach out to those medical students who were not present in the lecture theater during the administration of the study questionnaire. The study protocol was approved by the Ethics Committee of All India Institute of Medical Sciences (AIIMS).

The current study aimed at collecting the preliminary data on this theme. Hence, no predefined sample size was fixed at the beginning of the study.

Instruments

The study questionnaire consisted of the following three parts: a semi-structured questionnaire to assess the sociodemographic profile and gaming behavior of the participants; the IGD Scale-Short Form (IGDS9-SF) to assess the severity of IGD; and the Patient Health Questionnaire-9 (PHQ-9) to assess the depressive symptoms.

The semi-structured questionnaire collected sociodemographic information including the age, gender, marital status, current living arrangement, and the medical course pursued by the participants. Further, information related to Internet use and gaming behaviors over the past 1 month was collected based on the self-report by the participants. This was done to reduce the recall bias. It contained questions assessing the gaming behavior of the participants such as average amount of time spent online per day, average amount of time spent gaming per day, and the most played (preferred) game in the past 1 month.

The IGDS9-SF was used to assess the IGD behaviors. It is a brief psychometric scale comprising nine items, based on the nine core IGD criteria described in the DSM-5. The nine items of the IGDS9-SF load on to a single-factor structure in studies and demonstrate adequate reliability and validity for measuring IGD. The IGDS9-SF had been shown to have adequate construct and criterion validity, as well as an excellent reliability with an internal consistency coefficient (Cronbach's α) of 0.88.[10] It assesses the degree of severity of IGD and its detrimental effects by assessing both online and offline gaming activities over the last 12-month period. The responses to the nine items were self-rated by the participants on a 5-point Likert scale as follows: 1= “Never,” 2= “Rarely,” 3= “Sometimes,” 4= “Often,” and 5= “Very Often.” The total score was calculated by adding the scores obtained on all the nine items. It ranged from 9 to 45, with higher scores suggestive of a greater severity of IGD. Further, for research purpose, it may be used to classify disordered gamers and nondisordered gamers by considering IGD in all those participants who had endorsed at least five or more items on the scale. This corresponds with the DSM-5 threshold of at least five out of the nine proposed criteria for diagnosing IGD. In this study, a score of 5 or “very often” on an item was considered an endorsement of that particular item by the participant.[11]

The PHQ-9 was used to assess depression. In addition to its use for diagnosing depression, the PHQ-9 has been shown to be a reliable and valid measure of assessing depression severity.[12] It is a self-administered questionnaire which assesses the severity of depressive symptoms over the past 2-week period. It consists of nine items answered on a 4-point Likert scale as follows: 0= “not at all,” 1= “several days,” 2= “more than half of the days,” and 3= “nearly every day.” The item scores were combined to obtain a total score ranging between 0 and 27, with higher scores indicative of greater depressive symptom severity. A total score of >9 has been used for diagnosing major depression with a sensitivity of 88% and a specificity of 88% in primary health-care settings. Further, PHQ-9 scores of 5, 10, 15, and 20 have also been used to represent cut-off for mild, moderate, moderately severe, and severe depression, respectively.[12] In the present study, we used PHQ-9 scores as a proxy marker for assessing the psychological distress, especially depression severity among participants.

Statistical analysis

Statistical analysis was done using SPSS software version 21.0 (IBM Corp, Armonk, NY, USA). Descriptive statistics using mean, standard deviation, median, range, frequency, and percentage were used to describe the sample characteristics, Internet use and gaming pattern, gaming behaviors, and depressive symptom severity. Bivariate analysis using Pearson's correlation for continuous study variables and independent t-test or analysis of variance (ANOVA) for continuous study variables was conducted to examine their associations with the total IGDS9-SF score, representing the severity of IGD among the participants. Post hoc analysis with Bonferroni correction method was used for subgroup analysis in the ANOVA. Multiple linear regression analysis was conducted to determine whether variables with significant bivariate relationship could be used to independently predict the IGDS9-SF score. The level of statistical significance was set at P < 0.05 for all the tests. No missing value imputation was conducted.


   Results Top


The final study sample comprised 306 medical students, of which 227 (74.2%) were pursuing undergraduate course and 79 (25.8%) were postgraduate students. The detailed description of the study sample is given in [Table 1]. A total of 173 out of 306 (56.5%) participants reported playing digital games in the last 1 month and were classified as “current gamers.” A positive finding of IGD (endorsing “very often” on 5 or more items of the IGDS9-SF) was found for 11 out of the 306 study participants. Thus, the prevalence of IGD was 3.6% (11/306) in the present study sample.
Table 1: Description of the study sample (n=306)

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The genre of the preferred games reported by the participants were classified into the following categories based on the review of the existing literature: simulation or strategy games; role-playing games (RPGs); sports and racing games; shooting and action games; and puzzle, arcade, and board games.[13] Out of the 173 participants who reported their most preferred game in the past 1 month, the genre of simulation or strategy games; RPGs; sports and racing games; shooting and action games; and puzzle, arcade, and board games were endorsed by 66 (38.2%), three (1.7%), 36 (20.8%), nine (5.2%), and 59 (34.1%) participants, respectively. Thus, the genre of RPGs, sports and racing games, and shooting and action games were clubbed together as one group for further analysis, making the three groups based on the genre of game to be comparable.

Subsequently, bivariate and multiple linear regression analyses were conducted among the group of current gamers to examine the pattern and correlates of GD. The results of bivariate analysis examining the relationship between various study variables and the IGDS9-SF score are described in [Table 2] and [Table 3]. There was a significant correlation observed between the IGDS9-SF score and average time spent online per day (r = 0.20, P = 0.008), average time spent on gaming per day (r = 0.70 P < 0.01), and PHQ-9 score (r = 0.43, P < 0.01), representing the depressive symptom severity among the participants. The results of independent t-test showed male gender and living alone in hostel room to be associated with significantly greater IGDS9-SF scores. The results of ANOVA showed both gaming genre and mode of playing for the most preferred game reported by the participants to be significantly associated with the IGDS9-SF scores. The post hoc analysis with Bonferroni correction revealed that participants playing puzzle, arcade, and board games had statistically significantly lower IGDS9-SF scores as compared to participants playing the other two genre of gaming groups (pair 1–3, P = 0.015*; pair 2–3, P = 0.015*, with no statistically significant difference reported among those playing simulation or strategy games, RPGs, or sports and racing games, or shooting and action games. Further, multi-player online gaming style was associated with statistically significantly higher IGDS9-SF scores as compared to single-player online and offline modes of gaming (pair 1–2, P < 0.01; pair 1–3, P < 0.01). However, there was no significant difference in IGDS9-SF scores among participants with single-player online and offline groups of gaming style. Thus, based on the results of post hoc subgroup comparisons of ANOVA with Bonferroni correction, the study variables of gaming genre and mode of playing were reclassified into two groups with significant differences between them, prior to being entered into the multiple linear regression in the next step. The study variable of gaming genre was recoded into high-risk genre versus puzzles, arcades, and board games, whereas the mode of gaming was recorded multiplayer online gaming versus other styles of gaming.
Table 2: Correlation between the study variables and Internet Gaming Disorder Scale-Short Form score among current gamers (n=173)

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Table 3: Relationship between the study variables and Internet Gaming Disorder Scale-Short Form score among current gamers (n=173)

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Multiple linear regression analysis was conducted, with all the variables showing significant relationship with the IGDS9-SF score in bivariate analysis considered as independent variables and the IGDS9-SF score as the dependent variable. To check for multicollinearity, the tolerance statistic and variance inflation factor were examined, which did not reveal significant multicollinearity. The model [Table 4] was statistically significant (F = 36.31; P < 0.01**) and explained 60.6% of the variance in the IGDS9-SF scores. It was observed that greater amount of average time spent on gaming per day, playing multi-player online games (MPOGs), and greater PHQ-9 scores representing increased severity of depressive symptoms in the participants were significantly associated with greater scores on the IGDS9-SF, suggestive of a higher risk for having GD.
Table 4: Multiple linear regression equation for correlates of Internet Gaming Disorder Scale-Short form score among current gamers (n=173)

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   Discussion Top


The present study, to the best of our knowledge, is the first study to report the pattern and correlates of GD among medical college students from India. The results showed that playing digital games was a relatively common activity among medical students, with more than half of the participants (56.5%) reporting to play video or Internet games regularly in the last 1-month period. However, relatively, a small proportion of the total study sample (3.6%) met at least five out of the nine criteria needed for the DSM-5 diagnosis of IGD in the last 12-month period.

The estimated prevalence of 3.6% (11 out of 306) in a mixed sample of gamers and nongamers, reported in the present study, was obtained by applying the IGDS9-SF based on the nine criteria of IGD given in the DSM-5. This is in line with the findings of a recent systematic review of epidemiological studies on the prevalence of IGD from different countries, with a reported prevalence of IGD in the range of 0.7%–27.5%.[9] This review further pointed out that this large variation observed in the prevalence of IGD was likely to be due to the methodological differences among various studies, such as use of different assessment tools, cutoff scores, study sample characteristics, and survey methods employed for data collection. In a recent review, the authors examined the fit of eight different instruments used for the assessment of prevalence or pattern of IGD in various studies prior to the release of the DSM-5 in 2013 and found none of them to cover all the nine criteria of IGD described in the DSM-5.[14] Subsequently, several different studies using the DSM-5 criteria for the assessment of IGD have provided empirical evidence in the support for the use of at least five out of the nine DSM-5 criteria in differentiating disordered gamers with clinically significant impairments from healthy gamers.[15],[16] Thus, we used the IGDS9-SF based on the DSM-5 criteria of IGD for the assessment of both online and offline gaming behaviors over the past 12 months in the present study.

A recent review reported the prevalence of DSM-5-based IGD to be around 1% in four different population-based surveys,[17] mostly among adolescent students from Western settings. This is lower than the 3.6% prevalence obtained in the present study. This might be explained by the sociocultural differences existing between the populations of Western and the Eastern or South-East Asian countries. It has been hypothesized that unlike the students of Western countries, the students of Asian/Eastern countries face greater competition in the school and college environment because the academic success is highly valued and sought after in these societies.[18] Thus, Internet use and gaming might provide an escape into a virtual world where they could relieve the stress from academic competition. This is supported by the findings of another recent study conducted among a nationally representative sample of young adolescents in South-Korea using the same IGDS9-SF tool for assessment as done in the present study.[19] This study reported a prevalence of 5.9% for DSM-5-based IGD, which is comparable to the 3.6% reported in our study and way higher than the 1% reported from Western settings. Further, the lower prevalence of IGD reported from Western countries might be partly explained by the differences in the characteristics of study population among these studies. Most of the studies from Western settings were conducted among school students, whose Internet use and gaming behaviors are more likely to be checked by the parental guidance and supervision, unlike college students included in the present study.[20]

Male gender, living alone, and spending more time online per day showed significant bivariate associations with greater scores on the IGDS9-SF. However, they failed to show a significant association with the IGDS9-SF scores when other variables such as Internet gaming characteristics and depressive symptom severity were also considered in a multivariate regression model. This suggests that both males and females are equally vulnerable to develop IGD, provided they have similar access to the Internet and are in a similar socioculturally sanctioned role of students as in the present study. Further, it was observed that the greater duration of gaming time per day rather than the total time spent online per day was a significant independent factor associated with greater scores on the IGDS9-SF. This is supported by the bulk of the existing evidence, which suggests that time spent on gaming is positively correlated with the severity of IGD.[9] Thus, interventions directed at controlling the time spent on gaming rather than on the overall time of Internet use might be a more effective strategy for the prevention and management of IGD among students.

A preference for MPOGs among participants was found to be associated with significantly higher chance of having IGD as compared to those playing single-player online or offline games. This is supported by the existing literature, which suggests playing MPOGs is associated with an increased risk of IGD.[21],[22] This may be because of the social nature of these games, which allows the individual to interact with other gamers in a virtual space. There might be an element of peer pressure experienced by the students engaged in MPOGs that leads to continued playing of these games for prolonged periods of time, which in turn increases the risk for development of IGD. Further, these MPOGs regularly update their content to maintain the novelty of gaming experience for the players and provide them with opportunities for in-game socialization, in-game progression, and/or character development in the game.[23],[24] These unique structural characteristics of MPOGs act as positive reinforcements for gamers and promote increased frequency and duration of gaming behavior in them. Further, it has been proposed that MPOGs include multiple different subgenres of games such as simulation and real-time strategy games, first-person shooting games, RPGs, or fighting and action games.[25],[26] All these game genres have been reported to be associated with an increased risk of IGD in literature. This has also been seen in the present study, where the effect of genre of games was not significant when MPOG factor was considered in the multivariate regression model, suggesting that MPOGs subsumed the effect of these individual genre of games.

Further, the present study showed higher PHQ-9 scores to be significantly associated with greater scores on the IGDS9-SF, suggestive of higher risk of having IGD with increasing depressive symptom severity. This effect remained significant even when other factors were considered in the multivariate regression model, unlike some previous studies where the significant bivariate association between IGD and depression turned into a nonsignificant association in a multivariate regression model adjusted for the sociodemographic and Internet-gaming characteristics of the study sample.[27] A possible explanation proposed for this association is that people with depressive symptoms might indulge in excessive gaming behaviors as a coping strategy for the elevation of low mood associated with depression, similar to the “self-medication hypothesis” described in the context of GDs.[8] Thus, the present study adds to the existing literature in support of the association between GD and depression and highlights the importance of screening for and treatment of depressive symptoms and depression in medical students with problematic gaming behaviors or IGD.

Strengths and limitations

The students were recruited via a combination of both offline classroom approach and further sharing of online survey link among other eligible students themselves in a “snow-balling” pattern. This would help in including students absent on the day of data collection and reduce sampling bias, which had been a major limitation of several previous studies. Further, it would be especially useful for detecting hidden cases of severe GD or depression, who are more likely to be absent from the college setting and be missed by conventional recruitment strategy.[28]

However, the results of our study should be interpreted keeping in mind some of its limitations. The cross-sectional study design precludes one from assessing temporal relationships and establishing causal associations between the study variables and IGD. The study findings should be generalized with caution, in view of the modest sample size from a single medical college and nonrandom purposive sampling strategy employed. The information pertaining to the assessment of Internet gaming behaviors and depressive symptoms was collected through self-reported questionnaires and was not confirmed by the use of any other means such as a diagnostic interview. Thus, there is a possibility of misreporting of the actual prevalence rates due to the possibility of both underreporting and overreporting by participants.[29] However, self-reported questionnaires have been used for the assessment of IGD in several other studies, which has been described as an acceptable method.[30]


   Conclusions and Implications Top


The analysis of the extent and pattern of gaming behaviors (both offline and online) among medical students showed that 3.6% of them have self-reported IGDs according to the DSM-5 criteria. This study strongly suggests that IGD among medical students in India is an important emerging mental health condition, keeping in mind the substantial amount of the existing evidence regarding the negative consequences of excessive gaming on the physical, psychological, and social well-being of individuals. The present study also showed that spending greater amount of time in playing digital games, preference for multi-player online gaming pattern, and higher PHQ-9 scores representing greater depressive symptom severity were associated with significantly greater scores on the IGDS9-SF, indicative of a higher risk for having GD. This provides support for the etiological model proposed for the development of GD, which emphasizes on the interplay of factors such as structural characteristics of digital games, psychological characteristics of the gamer, and gamer's motivation behind gaming.[31] Further, the correlates of excessive gaming reported in this study would be useful for developing effective screening strategies to identify students at risk of GD, which in turn lead to an early diagnosis and management. There is a need to create awareness among students, teachers, medical colleges, and concerned authorities about the harms associated with excessive gaming and the various risk and protective factors for the development of GD. Future research with a larger and more representative sample size and longitudinal study design is needed to generate better quality of evidence base for the prevalence and correlates of GD among the Indian population to guide the development of effective preventive and management strategies for GD. The findings also support the need to strengthen the services for addressing behavioral addiction by establishing more clinics like the Behavioral Addictions Clinic at AIIMS, New Delhi.[32]

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders – Text Revision. 5th ed. Washington DC: American Psychiatric Association; 2013.  Back to cited text no. 1
    
2.
Faust KA, Prochaska JJ. Internet gaming disorder: A sign of the times, or time for our attention? Addict Behav 2018;77:272-4.  Back to cited text no. 2
    
3.
Saunders JB, Hao W, Long J, King DL, Mann K, Fauth-Bühler M, et al. Gaming disorder: Its delineation as an important condition for diagnosis, management, and prevention. J Behav Addict 2017;6:271-9.  Back to cited text no. 3
    
4.
Lee H. A new case of fatal pulmonary thromboembolism associated with prolonged sitting at computer in Korea. Yonsei Med J 2004;45:349-51.  Back to cited text no. 4
    
5.
Rumpf HJ, Achab S, Billieux J, Bowden-Jones H, Carragher N, Demetrovics Z, et al. Including gaming disorder in the ICD-11: The need to do so from a clinical and public health perspective. J Behav Addict 2018;7:556-61.  Back to cited text no. 5
    
6.
Kola V. About the Future of Gaming in India. Web Report; 2017. Available from: https://yourstory.com/2017/05/future-gaming-india/. [Last accessed on 2019 Jan 15].  Back to cited text no. 6
    
7.
Sachdeva A, Verma R. Internet gaming addiction: A technological hazard. Int J High Risk Behav Addict 2015;4:e26359.  Back to cited text no. 7
    
8.
Balhara YP, Garg H, Kumar S, Bhargava R. Gaming disorder as a consequence of attempt at self- medication: Empirical support to the hypothesis. Asian J Psychiatr 2018;31:98-9.  Back to cited text no. 8
    
9.
Mihara S, Higuchi S. Cross-sectional and longitudinal epidemiological studies of internet gaming disorder: A systematic review of the literature. Psychiatry Clin Neurosci 2017;71:425-44.  Back to cited text no. 9
    
10.
Pontes HM, Griffiths MD. Measuring DSM-5 internet gaming disorder: Development and validation of a short psychometric scale. Comput Human Behav 2015;45:137-43.  Back to cited text no. 10
    
11.
Lemmens JS, Valkenburg PM, Gentile DA. The Internet Gaming Disorder Scale. Psychol Assess 2015;27:567-82.  Back to cited text no. 11
    
12.
Kroenke K, Spitzer RL, Williams JB. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med 2001;16:606-13.  Back to cited text no. 12
    
13.
Na E, Choi I, Lee TH, Lee H, Rho MJ, Cho H, et al. The influence of game genre on internet gaming disorder. J Behav Addict 2017;29:1-8.  Back to cited text no. 13
    
14.
Petry NM, Rehbein F, Gentile DA, Lemmens JS, Rumpf HJ, Mößle T, et al. An international consensus for assessing internet gaming disorder using the new DSM-5 approach. Addiction 2014;109:1399-406.  Back to cited text no. 14
    
15.
van Rooij AJ, Schoenmakers TM, van de Mheen D. Clinical validation of the C-VAT 2.0 assessment tool for gaming disorder: A sensitivity analysis of the proposed DSM-5 criteria and the clinical characteristics of young patients with 'video game addiction'. Addict Behav 2017;64:269-74.  Back to cited text no. 15
    
16.
Király O, Sleczka P, Pontes HM, Urbán R, Griffiths MD, Demetrovics Z. Validation of the ten-item Internet Gaming Disorder Test (IGDT-10) and evaluation of the nine DSM-5 internet gaming disorder criteria. Addict Behav 2017;64:253-60.  Back to cited text no. 16
    
17.
Przybylski AK, Weinstein N, Murayama K. Internet gaming disorder: Investigating the clinical relevance of a new phenomenon. Am J Psychiatry 2017;174:230-6.  Back to cited text no. 17
    
18.
Zhang L, Amos C, McDowell WC. A comparative study of internet addiction between the United States and China. Cyberpsychol Behav 2008;11:727-9.  Back to cited text no. 18
    
19.
Yu H, Cho J. Prevalence of internet gaming disorder among Korean adolescents and associations with non-psychotic psychological symptoms, and physical aggression. Am J Health Behav 2016;40:705-16.  Back to cited text no. 19
    
20.
van den Eijnden RJ, Spijkerman R, Vermulst AA, van Rooij TJ, Engels RC. Compulsive internet use among adolescents: Bidirectional parent-child relationships. J Abnorm Child Psychol 2010;38:77-89.  Back to cited text no. 20
    
21.
Kuss DJ. Internet gaming addiction: Current perspectives. Psychol Res Behav Manag 2013;6:125-37.  Back to cited text no. 21
    
22.
Stavropoulos V, Kuss DJ, Griffiths MD, Wilson P, Motti-Stefanidi F. MMORPG gaming and hostility predict internet addiction symptoms in adolescents: An empirical multilevel longitudinal study. Addict Behav 2017;64:294-300.  Back to cited text no. 22
    
23.
Stetina BU, Kothgassner OD, Lehenbauer M, Kryspin-Exner I. Beyond the fascination of online-games: Probing addictive behavior and depression in the world of online-gaming. Comput Human Behav 2011;27:473-9.  Back to cited text no. 23
    
24.
Wei HT, Chen MH, Huang PC, Bai YM. The association between online gaming, social phobia, and depression: An internet survey. BMC Psychiatry 2012;12:92.  Back to cited text no. 24
    
25.
Kuss DJ, Griffiths MD. Online gaming addiction in children and adolescents: A review of empirical research. J Behav Addict 2012;1:3-22.  Back to cited text no. 25
    
26.
Meng J, Williams D, Shen C. Channels matter: Multimodal connectedness, types of co-players and social capital for multiplayer online battle arena gamers. Comput Human Behav 2015;52:190-9.  Back to cited text no. 26
    
27.
King DL, Delfabbro PH. The cognitive psychopathology of internet gaming disorder in adolescence. J Abnorm Child Psychol 2016;44:1635-45.  Back to cited text no. 27
    
28.
Tyrer S, Heyman B. Sampling in epidemiological research: Issues, hazards and pitfalls. BJPsych Bull 2016;40:57-60.  Back to cited text no. 28
    
29.
Jeong H, Yim HW, Lee SY, Lee HK, Potenza MN, Kwon JH, et al. Discordance between self-report and clinical diagnosis of internet gaming disorder in adolescents. Sci Rep 2018;8:10084.  Back to cited text no. 29
    
30.
Subramaniam M, Chua BY, Abdin E, Pang S, Satghare P, Vaingankar JA. Prevalence and correlates of internet gaming problem among internet users: Results from an internet survey. Ann Acad Med Singapore 2016;45:174-83.  Back to cited text no. 30
    
31.
Király O, Griffiths MD, Demetrovics Z. Internet gaming disorder and the DSM-5: Conceptualization, debates, and controversies. Curr Addict Rep 2015;2:254-62.  Back to cited text no. 31
    
32.
Balhara YP, Bhargava R, Chadda RK. Service development for behavioural addictions: AIIMS experience. Ann Natl Acad Med Sci (India) 2017;53:130-8.  Back to cited text no. 32
    



 
 
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  [Table 1], [Table 2], [Table 3], [Table 4]



 

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