A cross-sectional study was conducted by the Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry, among the medical, engineering and arts and science college going adolescents in Puducherry district of Union territory of Puducherry, South India from July 2018 to August 2018. This study protocol was reviewed and approved by the Institutional Ethics Committee (Ref No: IEC:RC/18/16). The Puducherry district is spread over 11 non-contiguous enclaves lying on the Bay of Bengal within a compact area in the state of Tamil Nadu with a population of 950,289 [8].

Sample size calculation: Based on the prevalence of digital gaming (p) of 56%, absolute error (d) 5.6%, p-value 0.05 and design effect of 1.3 using the formula of 4pq/d², calculated sample size was 410 [9]. Stratified multi-stage sampling technique was used to recruit the study participants [Table/Fig-1].

[Table/Fig-1]:

Flowchart showing the multistage stratified sampling method used for selection of study participants.

Colleges in Puducherry district were stratified was based on the type of college, i.e., Arts, Engineering and Medical colleges. From each of these strata, one Medical, one Engineering and one Arts and Science college was selected based on convenient sampling. Permission was obtained from the college authorities to conduct the study among their students. Participant information sheet with all necessary details was provided to all the study participants and written informed consent was obtained before the interview.

Inclusion criteria: From the selected college, one department was randomly selected and all the first and/or second year students in the selected department (age ≤19 years) were invited to participate in the study.

Exclusion criteria: Students of age >19 years, students who are not willing to participate.

After obtaining the written informed consent, a total of 415 eligible study participants were interviewed by a third year MBBS student. He was adequately trained in administering the study questionnaire by a Professor of Community Medicine. The information related to socio-demographic factors and digital gaming was collected using a structured questionnaire consisting of the following variables: age, gender, type of college, digital gaming use, mode, place and frequency of playing digital games, game engagement and addiction. Gaming Addiction Scale (Cronbach’s alpha of 0.86) was used to assess the level of addiction to digital gaming [10]. The criteria of engagement were: 1) Salience (Did you think about playing a game all day long?); 2) Tolerance (Did you spend increasing amounts of time on games?); and 3) Mood modification (Did you play games to forget about real life?). The addiction criteria included the following parameters: 1) Relapse (Have others unsuccessfully tried to reduce your game use?); 2) Withdrawal (Have you felt bad when unable to play?); 3) Conflict (Did you have fights with family/friends over your time spent on games?); 4) Problems (Have you neglected other activities (e.g., school, work, sports) in order to play games?). The digital gamers were classified as addictive gamers (endorsed all four addiction criteria), problematic gamers (endorsed two or more of the addiction criteria) and engaged gamers (endorsed all three engagement criteria and no more than one addiction criterion) [10]. Ryff’s scale (Cronbach’s alpha 0.77) was used to assess the psychological well-being of all the participants [11,12]. Permission was obtained from the concerned authors to use Ryff’s scale. It consisted of 42 items with six broad dimensions such as autonomy, environmental mastery, personal growth, positive relations with others, purpose in life and self-acceptance. Study participants are asked to rate how strongly they agree or disagree with 42 statements using a 6 point scale (ranging from 1=strongly disagree to 6=strongly agree) and total scores were calculated by summation. The range of scores of each dimension was 7 to 42. For overall scale, 42 and 252 were the minimum and the maximum scores respectively [13]. As per the operational definition used in the present study, the study participants who scored below 25^th, 25^th to 75^th and above 75^th percentile were classified as participants with low, moderate and high psychological well-being respectively.

Data were entered in MS Excel and analysed in IBM SPSS Statistics for windows, Version 21.0 (IBM Corp, Armonk, New York). Means and proportions were calculated. Chi-square test was used to measure p-value for comparison of proportions between the groups. For comparing the mean scores among three groups, one-way ANOVA was used to calculate the p-value. The p-value <0.05 was considered as statistically significant.

Relation between socio-demographic characteristics and digital gaming among the study participants are shown in [Table/Fig-2]. Among the study participants of 17 years of age, the proportion of ever played digital gaming was 49.1%. Proportion of study participants who ever played digital gaming among females (93%) was higher than males (74%) and the difference was statistically significant (p<0.001). All the students (100%) of arts and engineering colleges ever played digital gaming whereas only 48.9% of medical students ever played digital gaming i.e., both current and past users (p<0.001).

Pattern of digital gaming with respect to gender among those who ever played digital games are presented in [Table/Fig-3]. The proportion of digital gamers using online mode only was slightly higher in males (11.9%) than females (8.1%) but statistically not significant (p=0.380). A 79.2% of males were playing digital games at their homes which was higher than that for females (73.7%) and the difference was statistically significant (p<0.001). The proportion of daily playing digital gamers was higher in females (28.6%) than males (23.4%) but not statistically significant (p=0.215).

One-third of the digital gamers played to forget about real life and about one-fourth neglected other activities (e.g., school, work, sports) in order to play games. Out of 345 ever users of digital gaming, the prevalence of addictive and problematic users was 4.3% and 33.6% respectively [Table/Fig-4].

Relationship between digital gaming and psychological well-being is shown in [Table/Fig-5]. The prevalence of low psychological well-being was higher among the never (31.4%) than current (22.5%) and past users (18.2%) of digital gaming and the difference was statistically not significant (p=0.092).

[Table/Fig-6] shows the comparison of mean scores of six dimensions of psychological well-being scale among the study participants with respect to digital gaming. The mean score of personal growth dimension was higher among the current (29.7) and never (29.5) users than past users (26.5) and it was statistically significant (p<0.001).

In the present study, more than 50% of the study participants were currently playing digital games which were similar to the prevalence of current digital gamers (55.6%) among the medical students of New Delhi, India [7]. The proportion of study participants who never played digital gaming was higher among the males (26%) than females (7%) and this difference was statistically significant. In contrast, the level of internet addiction was higher among males than females in a study which was conducted in the same settings [6]. Every student of Engineering and Arts colleges played digital games in the past or was currently playing however it was only 48.9% among the medical students. The preference to play digital games at homes was slightly higher among males than females and it was statistically significant. In a study conducted by Niranjjan R et al., in 2016 from Puducherry, only 6% of the study participants were using internet for digital gaming whereas in the present study, 64.9% were playing online games [6]. This rapid increase in online gaming in the same settings over a shorter period could be explained by increased availability of smart phones and cheaper internet services in the recent years [14].

The proportion of study participants playing digital games using offline mode only was much higher than online only which was in contrast to the findings of a study conducted by David B et al., in Chicago. In their study, the proportion of individuals who played for 21 to 40 hours per week was higher among online (34%) than offline (4%) gamers [15].

In a Norwegian study, the prevalence of addictive and problematic users was 0.6% and 4.1% respectively. In contrast to their findings, addictive and problematic users were higher in the present study which could be attributed to difference in the study participants’ age. The present study was conducted among adolescents from 17 to 19 years of age whereas the Norwegian study included participants in the age group of 15 to 40 years [9]. In another study by Mannikko N et al., the prevalence of problematic gaming behaviour was 1.3% based on Internet Gaming Disorder Test (IGDT-10), whereas it was 33.6% in the present study based on Lemmens JS et al., addiction scale [10,16]. However, the prevalence of problematic mobile gaming was 19% among Taiwan school going adolescents using the same IGDT-10 questionnaire which was relatively lesser than the present study (33%), the variation might be due to different tools and age groups [17]. In a large nation-wide survey conducted among the adolescents in Germany using a Video Game Dependency Scale (KFN-CSAS-II), the prevalence of video game dependency was 1.7%. Males (91%) were predominately affected with video game dependency compared to females [18]. Similarly, the researchers from Netherlands reported that 3% of the adolescents were addicted online gamers based on the duration of weekly online gaming and compulsive internet use [19].

The mean score for personal growth dimension was significantly higher among the current users than the past users (p<0.001) however the mean score of self-acceptance was lower among the current users than the past users (p=0.046*). Positive association with other dimension’s score was highest among current users but statistically not significant. Among Dutch children, Lobel A et al., found that frequent competitive gaming was associated with low prosocial behaviour. However, violent (r=0.041) and co-operative gaming (r=0.079) didn’t have correlation with psychosocial changes which was assessed using strengths and difficulties questionnaire [20]. In another study, among 2482 Canadian youth, video game playing and overall screen time were found to be associated with severe symptoms of depression and anxiety [21]. A multicentre collaborative study conducted among the adolescents of United Kingdom (1 site) and United States (2 sites) also supported the above findings that heavy users of digital media including electronic gaming was associated low psychological well-being which includes unhappiness, depression, suicidal ideation and attempts [22]. Similarly, addiction to mobile games was found to be associated with depression and loneliness among school going adolescents in Guizhou Province of China [23]. The findings of these studies indicated that gaming disorders affected the mental health of the young population. The strengths of the present study were utilisation of standard validated questionnaire for assessment of psychological well-being and inclusion of study participants from different types of higher education.

Due to significant non-response to variables such as frequency and duration, the dose response gradient was not calculated. The variation in psychological well-being score across different types of digital games users could not be assessed due to lesser sample size of the study. There is a need to conduct similar studies with large sample size using cohort design.

The present study findings suggested that most of the college going adolescents had played digital games either previously or were currently playing. There was no significant relationship between psychological well-being and digital gaming except for personal growth and self-acceptance dimensions. Adolescents should be educated about the harmful effects of digital gaming and its use should be restricted through collaborative efforts of educational institutions, regulatory bodies and government organisations.