Home | About IPJ | Editorial board | Ahead of print | Current Issue | Archives | Instructions | Contact us |   Login 
Industrial Psychiatry Journal
Search Articles   
    
Advanced search   
 


 
ORIGINAL ARTICLE
Year : 2020  |  Volume : 29  |  Issue : 1  |  Page : 33-37  Table of Contents     

Measuring addiction to internet gaming among Indian medical students: Development and preliminary psychometric properties of a new scale


Department of Community Medicine, Maulana Azad Medical College, New Delhi, India

Date of Submission10-Jun-2020
Date of Acceptance12-Jul-2020
Date of Web Publication07-Nov-2020

Correspondence Address:
Dr. Saurav Basu
Department of Community Medicine, Maulana Azad Medical College, New Delhi
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ipj.ipj_114_20

Rights and Permissions
   Abstract 


Background: Internet gaming disorder (IGD) is emerging as an important source of behavioral addiction in young people globally. Objective: The aim of this study was to assess addiction to internet gaming in medical students using a self designed research instrument. Materials and Methods: We conducted a cross-sectional study and enrolled 264 male and 160 female MBBS students (n = 424) in Delhi, India. The study instruments included: a 2-item Internet gaming screening questionnaire (IGSQ), a 14-item self-designed Internet gaming addiction scale (IGAS) to measure addiction-like behavior associated with multiplayer Internet gaming, and the Pittsburgh Sleep Quality Index to measure sleep quality. Results: After preliminary screening, 91 male and 6 female participants were found to be multiplayer Internet gamers, and were further assessed using the 14-item IGAS. The Cronbach's alpha of the IGAS was 0.879. Principal component analysis revealed a three-component IGAS structure based on eigenvalue cutoff (>1), loading score >0.4, and inspection of the scree-plot that explained 66.71% of the total variance. The IGAS score and the average weekly gaming time of the participants showed a moderate positive correlation (r = 0.45, P < 0.001). Only 17 (4.0%) participants reported agreement/strong agreement across ≥5 domains of addiction. The mean (standard deviation) IGAS score was significantly higher in the participants reporting poor sleep quality (PQSI > 6) (P = 0.047). Conclusion: IGD has low prevalence among medical students, and the problem is negligible among female students. The 14-item IGAS, in conjunction with the 2-item IGSQ, are reliable and valid tools for the assessment of IGD.

Keywords: Behavioral addiction, internet gaming addiction, technological addiction


How to cite this article:
Basu S, Sharma P, Rustagi R, Sharma R, Sharma N. Measuring addiction to internet gaming among Indian medical students: Development and preliminary psychometric properties of a new scale. Ind Psychiatry J 2020;29:33-7

How to cite this URL:
Basu S, Sharma P, Rustagi R, Sharma R, Sharma N. Measuring addiction to internet gaming among Indian medical students: Development and preliminary psychometric properties of a new scale. Ind Psychiatry J [serial online] 2020 [cited 2020 Dec 2];29:33-7. Available from: https://www.industrialpsychiatry.org/text.asp?2020/29/1/33/299921



Addiction like symptoms among adolescents and youth due to the excessive use of the Internet and associated technologies such as smartphones and social media is well-established.[1] Internet-based multiplayer gaming has become an increasingly popular technology-derived leisure time activity globally. Gaming technology has tremendously evolved over the decade in its ubiquitous availability on a variety of devices, especially smartphones, whereas high-speed Internet availability and affordability has rapidly enhanced, fueling the growth of the Internet gaming platform.[2],[3] There is a growing recognition of internet gaming disorder (IGD) as a potential psychiatric condition associated with significant impairment or distress. The American Psychiatric Association (APA) has included Internet gaming in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) as a preliminary disorder, and an emerging behavioral addiction that warrants further research.[4] The World Health Organization has identified “gaming disorder” characterized by the impaired control and escalating of gaming-related activities as a mental health disorder in the current 11th revision of the International classification of disease.[5]

The global prevalence of IGD in a systematic review ranged from 0.7% to 27.5%, with males and younger people experiencing significantly higher risk.[6] The Indian population is at considerable risk of all forms of technological addiction due to an exponentially increasing smartphone market, expanding Internet connectivity, and the largest population pool of adolescents and young people globally.[7] Compared to conventional gaming, Internet gaming carries a higher risk of addiction as it combines the addictive potential of the Internet through multiplayer gaming, which fosters virtual relationships on gaming-based social networks and an outlet to escape from the real world.[8],[9] Furthermore, like gambling, which is the only APA certified technological and behavioral disorder, IGD can also induce impaired control, craving, and compulsive engagement despite adverse consequences on health and well-being.[10] However, there exists a paucity of studies that have focused on the problem of internet gaming disorder in India. A recent study by Singh et al. reported a 3.6% prevalence of IGD among undergraduate and postgraduate medical students in Delhi.[11] However, to the best of our knowledge, no IGD assessment scale has been validated in Indian settings.

In this study, we assessed internet gaming disorder in Indian medical students using a self-designed scale in conjunction with a screening questionnaire.


   Materials and Methods Top


Study setting and participants

We conducted a cross-sectional study among adult medical undergraduate (MBBS) students at a government medical college in Delhi during September–November 2019.

Study instruments

  1. A 2-item Internet gaming screening questionnaire (IGSQ): After describing an Internet multiplayer game, a few examples of such gaming titles, based on total downloads and ratings on the Google (Android) Play Store and the Steam PC gaming platform, were mentioned to trigger easy understanding and recognition among the participants. Students who answered “Yes” to both the statements, “Playing an internet multiplayer game in the previous seven days” and “playing on average for ≥1 h any Internet multiplayer game on their mobile or laptop on a typical day” were further assessed for the presence of IGD. Those students who answered “No” to any of the statements were considered not having any potential for addiction-like behavior associated with Internet gaming
  2. A self-designed 14-item Internet gaming addiction scale (IGAS) based on the symptoms of IGD described by the APA [4] and application of the International Classification of Diseases-10 criteria for substance dependence syndrome.[12] The IGAS measures addiction across the following domains: (i) Intense desire (Q. 1, 5, 7) (ii). Withdrawal symptoms (Q. 3, 9, 10) (iii). Tolerance (Q. 4) (iv). Harmful use (Q. 6, 11, 12, 13) (v). Impaired control (Q. 2, 3, 4, 6, 14) and (vi). Reduced alternate pleasure (Q 7. 8). The response to the each of the IGAS items were self-rated on a 6-point Likert scale with options being 1 (strongly disagree), 2 (disagree), 3 (weakly disagree), 4 (weakly agree), 5 (agree), and 6 (strongly agree). The total IGAS score was the sum of individual scores for all the 14 items of the questionnaire, with higher scores indicating a greater risk of addiction
  3. Pittsburgh Sleep Quality Index: was used to ascertain the overall sleep quality of the participants, and a PQSI score >6, was applied as a cutoff to distinguish the poor from good sleepers.[13],[14]


Pretesting of the instruments was conducted among 20-students inclusive of 10 male and 10 female students who were not part of the final study. It was observed that among the students who reported no to any of the items on the IGSQ, all had a total IGAS score ≤16, due to an absence of internet gaming addiction. Content validity of the items included in the instruments was assessed at this stage by the investigators for accuracy, grammar, lack of bias, and avoiding leading questions.

Sample size

A respondent ratio of 5–10 per scale item is considered an adequate sample size in factor analysis.[15] We wanted to include a minimum of 100 and preferably 140 participants for conducting the factor analysis of the IGAS. However, despite screening 424 students with the IGSQ, only 97 were found at risk of internet gaming addiction, and they were further screened using the IGAS.

Sampling and data collection

We selected MBBS students from five class batches. The final year students and the internship batch were considered as a single unit. Each class consisted of approximately 250 students. A total of 95 students were selected from each batch using the simple random sampling method with the sampling frame obtained from the batch's attendance registers containing the names and roll numbers of the students. The student roll numbers were selected as a set of random numbers without replacement using the online research randomizer tool.[16] In case the selected student was absent on the day of the assessment, an additional student was selected using the same method.

Procedure

The study instruments were self-administered to the participants. Attempts were made by the investigators to normalize the risk behaviors by informing the participants before the survey administration that there were no correct or incorrect answers and reassuring them regarding the complete confidentiality and anonymization of their responses. An investigator was also present in the room when the participants submitted their instruments to assist them in case of any query and minimize interaction between the participants during this period to avoid any external influence and maintain survey integrity.

Handling of missing data

The dataset was cleaned by inspection of the cases for missing values. We excluded a total of 9 questionnaires having >2 incomplete entries on the IGAS. Missing items were imputed, taking the item median score as per the participant gender and age-group.

Statistical analysis

Data were entered in MS-Excel, cleaned, and imported into the Statistical Package for the Social Sciences (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY) for analysis. The independent samples t-test was applied to test for the significance of the mean difference between groups. A P < 0.05 was considered statistically significant. We also conducted a principal component analysis (PCA) on the responses to the IGAS for investigating its construct validity.

Ethical considerations

The study was approved with an exemption from full review by the Institutional Ethics Committee (F.1/IEC/MAMC/68/03/2019/158). Written and informed consent was obtained from all the study participants.


   Results Top


Participant characteristics

We evaluate responses from 424 participants with mean (standard deviation [SD]) age of 19.83 (1.6) years and comprising 264 (62.3%) males and 160 (37%) female students.

After screening with the preliminary gaming questionnaire, we retained 97 participants including 91 (93.8%) boys and 6 (6.2%) girls who reported playing an online, social or multiplayer player in the previous 7 days, with the total time spent playing multiplayer games on an average day reported as ≥1 h.

Among multiplayer Internet gaming participants, the average weekly frequency of gaming was 4.58 (±1.6) days, and the mean (±SD) gaming time on a typical day was 2 (±0.8) hours.

Factor analysis of Internet gaming addiction scale

The dataset was found suitable for the application of the PCA having an adequate sample size. The correlation matrix showed that all variables had at least one correlation coefficient >0.3. The overall Kaiser–Meyer-Olkin (KMO) measure was 0.942, classifications of “marvellous” according to Kaiser, individual KMO measures were all >0.9, and Bartlett's Test of Sphericity was statistically significant (χ2 = 774.4) (P < 0.0001).

The PCA revealed a three-component structure based on eigenvalue cutoff ≥1, loading score ≥0.4, and inspection of the Scree Plot [Figure 1]. The total variance explained by the model was 66.71%, with component 1 (51.02%), component 2 (8.0%), and component 3 (7.6%). We observed strong loading of items relating to intense desire, withdrawal symptoms, tolerance, and reduced alternate pleasure on component 1, impaired control items on component 2, and the harmful use items on component 3 [Table 1].
Figure 1: Scree plot of a 3-component internet gaming addiction scale

Click here to view
Table 1: Rotated structure matrix for principal component analysis with varimax rotation of a 3-component Internet gaming addiction scale showing major loadings (≥0.4)

Click here to view


Reliability of Internet gaming addiction scale

The distribution of participant responses to the IGAS is reported in [Table 2]. The Cronbach's alpha of the IGAS was 0.924, indicating excellent reliability. The reliability analysis suggested that all the 14 items in the IGAS were to be retained.
Table 2: Distribution of responses to the Internet gaming addiction scale in multiplayer Internet game playing participants (n=97)*

Click here to view


Prevalence of IGD

The mean (SD) IGAS score in the participants was 44.2 (±17.3), whereas the maximum permissible score was 84. Positive agreements (item mean score >3) were reported for items 1, 2, 4, 6, 8, 9 and 10. Based on item loadings on the PCA, we observed only 17 (4%) participants who reported agreement/strong agreement across ≥5 domains of addiction (Q. 2, 4, 7, 8, 9, 12).

Construct validity of Internet gaming addiction scale

Scores on the IGAS were not significantly associated with participant age. However, the participants with poor sleep quality registered significantly higher IGAS scores compared to those with good sleep quality (P = 0.047) [Table 3]. The IGAS score and the average weekly gaming time of the participants also showed a moderate positive correlation that was statistically significant (r = 0.45, P < 0.001). The application of both the IGSQ and the IGAS showed that female students rarely participated in Internet gaming sessions that were of significant duration and were not at risk of IGD, unlike, one in four male students who pursued Internet gaming as a source for recreation.
Table 3: Construct validity of the Internet gaming addiction scale in multiplayer Internet game playing participants (n=97)

Click here to view



   Discussion Top


The present study indicates that nearly 23% of medical students that were predominantly male participated routinely in internet gaming activities, but only 17 (4%) students reported addiction-like symptoms suggestive of IGD. Our results are partially in agreement with another study conducted at a medical institute in Delhi that reported 3.6% of students meeting criteria for the diagnosis of IGD, although unlike our study, females were also reported to be at risk.[11] Large-population surveys in the Western world have reported IGD prevalence of ≤1% in the school-going adolescent population,[17] but a similar study in South Korea reported a higher IGD prevalence (5.6%).[18] The study also had a substantial proportion of adolescents, although their IGAS scores did not differ significantly from comparatively older participants. This was probably because college or university, unlike school, entails a lack of supervision and control of internet gaming activities of students by their parents and teachers.[11],[19] Higher IGD scores were also associated with poor sleep quality, which corroborates the evidence from previous studies.[20]

Strengths, limitations, and recommendations

This is one of the first studies in India that assesses internet gaming disorder using a self-designed instrument, which demonstrated good psychometric properties. We collected data applying the principles of random sampling. Nevertheless, the study was conducted in a single site, among medical students who are likely to have a greater awareness of addiction, which limits the external validity of the study findings. In this study, the correlation of poor sleep quality with internet gaming addiction was considered as a measure of construct validity. However, other psychosocial factors such as stress and depression may also influence sleep quality, but these were not evaluated in the present study.[21]

Furthermore, in this study, we restricted the assessment of addiction to Internet gaming or multiplayer online gaming, which is known to have a significantly higher addictive potential compared to conventional offline gaming.[9] However, the study lacked qualitative perspectives to distinguish the gaming time of the participants that was spent exclusively on multiplayer games, and instead, the gaming time reported is likely to include a combination of both offline and online gaming. Moreover, multiplayer games may also have a single-player (offline) mode of play. Consequently, the study findings may not be generalizable in those gamers who predominantly play offline games. Another significant limitation of our study was the cross-sectional design, due to which temporal and causal relationships could not be established, like, the effect of long-standing IGD on academic performance or health outcomes, which require prospective evaluation.

Future studies using the IGAS should assess its validity in other student cohorts belonging to the general university, information technology, and school-going students. Furthermore, IGAS scores should be validated against other measures such as depression and anxiety.[22]


   Conclusion Top


IGD has low prevalence among medical students in India, and the problem is negligible among female students. The 14-item IGAS, in conjunction with the 2-item IGSQ, are reliable and valid tools for the assessment of IGD.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Kuss DJ, Lopez-Fernandez O. Internet addiction and problematic Internet use: A systematic review of clinical research. World J Psychiatry 2016;6:143-76.  Back to cited text no. 1
    
2.
Kuss DJ. Internet gaming addiction: Current perspectives. Psychol Res Behav Manag 2013;6:125-37.  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.
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. 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.
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. 6
    
7.
Pew Research Center. Mobile Connectivity in Emerging Economies; March, 2019. Available from: https://www.pewresearch.org/internet/wp-content/uploads/sites/9/2019/03/PI_2019.03.07_Mobile-Connectivity_FINAL.pdf. [Last accessed on 2020 Jul 03].  Back to cited text no. 7
    
8.
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. 8
    
9.
Achab S, Nicolier M, Mauny F, Monnin J, Trojak B, Vandel P, et al. Massively multiplayer online role-playing games: Comparing characteristics of addict vs. non-addict online recruited gamers in a French adult population. BMC Psychiatry 2011;11:144.  Back to cited text no. 9
    
10.
Fauth-Bühler M, Mann K. Neurobiological correlates of internet gaming disorder: Similarities to pathological gambling. Addict Behav 2017;64:349-56.  Back to cited text no. 10
    
11.
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.  Back to cited text no. 11
[PUBMED]  [Full text]  
12.
WHO. The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. Geneva: World Health Organization; 1992.  Back to cited text no. 12
    
13.
Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res 1989;28:193-213.  Back to cited text no. 13
    
14.
Manzar MD, Moiz JA, Zannat W, Spence DW, Pandi-Perumal SR, BaHammam AS, et al. Validity of the Pittsburgh sleep quality index in Indian University students. Oman Med J 2015;30:193-202.  Back to cited text no. 14
    
15.
Tsang S, Royse CF, Terkawi AS. Guidelines for developing, translating, and validating a questionnaire in perioperative and pain medicine. Saudi J Anaesth 2017;11:S80-9.  Back to cited text no. 15
    
16.
Urbaniak GC, Plous S. Research Randomizer (Version 4.0); 2013. Available from: http://www.randomizer.org/. [Last accessed on 2020 Jul 03].  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.
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. 18
    
19.
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. 19
    
20.
Lam LT. Internet gaming addiction, problematic use of the internet, and sleep problems: A systematic review. Curr Psychiatry Rep 2014;16:444.  Back to cited text no. 20
    
21.
Oh CM, Kim HY, Na HK, Cho KH, Chu MK. The effect of anxiety and depression on sleep quality of individuals with high risk for insomnia: A population-based study. Front Neurol 2019;10:849.  Back to cited text no. 21
    
22.
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. 22
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

Top
  
 
  Search
 
  
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
    Abstract
    Materials and Me...
   Results
   Discussion
   Conclusion
    References
    Article Figures
    Article Tables

 Article Access Statistics
    Viewed166    
    Printed4    
    Emailed0    
    PDF Downloaded13    
    Comments [Add]    

Recommend this journal