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 Table of Contents  
Year : 2022  |  Volume : 24  |  Issue : 3  |  Page : 59-63

Study of Prevalence of Internet Addiction among Adolescents in a Cantonment School

1 Department of Community Medicine, Armed Forces Medical College, Pune, Maharashtra, India
2 Department of Psychiatry, Command Hospital, Southern Command, Pune, Maharashtra, India
3 Department of Psychiatry, Armed Forces Medical College, Pune, Maharashtra, India

Date of Submission06-Aug-2020
Date of Decision16-Aug-2020
Date of Acceptance25-Oct-2020
Date of Web Publication01-Apr-2021

Correspondence Address:
(Dr) Surg Cdr Amit Chail
Department of Psychiatry, Armed Forces Medical College, Pune - 411 040, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmms.jmms_109_20

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Background: More Indians have access to mobile phones than toilets. Concerns have been raised for addictive potential of Internet. There are few Indian studies on the prevalence of Internet addiction (IA) among adolescents. The present study aimed to assess the prevalence of IA among adolescents and its association with sociodemographic variables in a cantonment school. Methodology: It was a cross-sectional study among students of the age group of 10–19 years in Western Maharashtra. Sociodemographic data were collected using a self-administered questionnaire. Young's IA test was used to diagnose IA. Statistical analysis was done using Chi-square test and odds ratio. Results: A total of 1325 students participated in the study. Among these, 2.5% had scores above 69 (addicted). Among those with scores > 69, 85% were boys, average daily Internet use was 5.3 h in last 1 year. Use for academics, online gaming, and social media were the most common reasons for current use among the study sample. Risk factors included watching pornography, online gaming, use of Internet due to loneliness or boredom, use in Internet café, permanent login status, accessing Internet > 4 h daily, and consumption of tobacco or alcohol by any of the parents. The possible protective factors included using Internet only for academics, restricting Internet use to < 2 h, and having a playground nearby and playing there. Conclusion: In our study, prevalence of IA is around 2.5%. The possible risk and protective factors can be targets of intervention and further study.

Keywords: Adolescents, Internet addiction, risk factors of Internet addiction

How to cite this article:
Kumar S, Singh H, Shankar P, Chail A. Study of Prevalence of Internet Addiction among Adolescents in a Cantonment School. J Mar Med Soc 2022;24, Suppl S1:59-63

How to cite this URL:
Kumar S, Singh H, Shankar P, Chail A. Study of Prevalence of Internet Addiction among Adolescents in a Cantonment School. J Mar Med Soc [serial online] 2022 [cited 2022 Aug 9];24, Suppl S1:59-63. Available from: https://www.marinemedicalsociety.in/text.asp?2022/24/3/59/312883

  Introduction Top

Internet is an important tool for gaining and sharing information, education, entertainment, connecting with people, business, and many other purposes. As per a recent UN report, more Indians have access to mobile phones than toilets.[1] According to the Internet World Statistics, there has been 1052% increase in the Internet use from 2000 to 2018.[2] India has over 560 million Internet users with around 41% penetration.[3] With people spending increasing time on the Internet, concerns have been raised about the addictive potential of Internet.[4],[5]

The term “internet addiction (IA)” was proposed by Ivan Goldberg in 1995 for pathological compulsive Internet use.[4] Diagnostic and statistical manual-5 has recognized Internet gaming disorder as an area for further study,[6] while international classification of diseases-11 (Mortality and Morbidity Statistics, Version 4/2019) has included “gaming disorder” – either online/offline as a clinical entity.[3]

Various models have been proposed for IA. Griffith used behavioral addiction model.[7] Kimberley Young conceptualized IA as being akin to pathological gambling. She gave a 20-item, self-report scale to measure the severity of compulsive use of the Internet.[7] The etiological models include cognitive behavioral model, neurobiological models, and behavioral models.[8] Proposed subtypes of IA include cyber-sexual addiction, Cyber-relationship addiction, net compulsions, information overload, and computer addiction.[9]

The prevalence of IA ranges from 1% to 12.5% across different countries.[4] Another review describes the prevalence among younger population from 0.9% to 38%.[8] Some groups like medical students have a higher prevalence of IA than the general population.[10]

There are few Indian studies on assessing the prevalence of IA among the adolescents. A 2013 study reported the prevalence of IA as 0.7% among adolescents in the age group 16%–18%.[5] They reported higher use among males and it was associated with increased rates of depression and anxiety. Prabhakaran et al. reported the prevalence of IA at IA–8.7%.[11] The risk factors associated with IA were male gender, owning a personal device, use of smartphones, permanent login status, use of Internet for chatting, online friendships, shopping, watching movies, and online gaming. In a study among school-going adolescents in the age group of 13–19 years in Vadodara district, 0.5% of the students were significantly above average users (SAAU = IA).[12] While 14.6% were above average users (AAU) and 44.8% were average users, males were more likely to be AAU and SAAU than females.

IA in adolescents is believed to be associated with dysfunctional development of certain brain circuits and areas like orbitofrontal cortex, dorsolateral prefrontal cortex, cingulate cortex, and the reward circuit.[13] It is associated with physical morbidity which may be primary or secondary to a sedentary lifestyle. These include fatigue, backache, headache, weight gain, and other metabolic complications and vision disturbances (digital eye strain).[14],[15],[16],[17]

Psychological problems associated with IA include depression, anxiety (especially social anxiety), poor self-esteem, attention deficit hyperactivity disorder, fear of missing out, sleep disturbances, other substance use disorders, aggression, poor impulse control, cyber-bullying, dysfunctional coping, and eating disorders.[5],[13],[18],[19],[20],[21],[22] The social correlates include social withdrawal, poor academic, and work performance and disturbed inter-personal relationships.[13]

Most of the Indian research on IA has been on college students. There have been few prevalence studies among younger children. The current study was carried out with the aim of assessing the prevalence of IA in young children and adolescents and its association with sociodemographic variables. In addition, we sought to find possible risk and protective factors associated with IA so that early and appropriate interventional strategies can be planned.

  Methodology Top

It was a cross-sectional study wherein students of the age group of 10–19 years in a cantonment school in Western Maharashtra were included. The study was approved by the institutional ethics committee. Permission of school authorities and parents' consent was obtained. Verbal assent from the participants was obtained. Confidentiality was assured and a record of identity of students was not maintained. Statistical analysis was done using SPSS Version 23.0 (IBM, India) (Chi-square test and odds ratio).

The data were conducted from February to March 2018 and data analysis was done from April to July 2018. Data were collected using two tools. The first, a self-administered sociodemographic questionnaire was used for data collection. In addition, certain questions based on possible associated factors from previous studies were included in the questionnaire.[11],[23],[24] No data having personal identifiers were asked. The second was Young's IA test (IAT). It was used as a diagnostic tool for IA.[25] It is a self-administered 20-item, 5-point Likert scale that measures the severity and compulsive use of the Internet. The total score ranges from 20 to 100. The IAT is a valid and reliable scale, with satisfactory internal consistency (Cronbach's alpha of 0.84). Scores between 20 and 39 were classified as normal use; between 40 and 69 as “possibly addicted;” and scores higher than 69 as “addicted”.[4] Those who scored more than 69 (addicted) were given option of appropriate treatment (through their parents) of either pharmacotherapy or psychotherapy or both.

  Results Top

A total of 1325 adolescents participated in the study. All participants were students of the same school. The mean age of the participants was 13.99 years, with 57.4% of the participants being males. [Table 1] shows the age distribution of students. All students (100%) had access to Internet either as smartphone or laptop/PC with WiFi connection at home.

Common reasons for the initiation of Internet use and current use of Internet are shown in [Figure 1] and [Figure 2], respectively. Use for academics was the most common reason for initiation and current use.
Table 1: Age distribution

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Figure 1: Reasons for initiation of Internet use

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Figure 2: Reasons for present use of internet

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Out of 1325 participants, 938 (70.8%) had normal use of Internet (IAT score < 40), while 354 (26.7%) were classified as having possible IA (IAT score 40–69). The prevalence of IA (IAT score > 69) was found to be 2.5% [Table 2]. Among the students with IA (n = 33), 14 had cybersexual, 25 had cyber-relational, 21 had net compulsions, 10 had information overload, and 12 had computer addictions. The total was more than 33 since many had more than one type of addiction.
Table 2: Prevalence of Internet addiction

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To determine factors associated with IA, we compared the students with IA (IAT score > 69) with students with normal Internet use (IAT < 40) [Table 3] and [Table 4]. The prevalence of IA was found to be significantly higher among males. The mean age of students with IA was 16.2 years as compared to 13.99 years for the study population. The average time spent online (daily) was 5.3 h for IA group versus 1.4 h for those with normal Internet use.
Table 3: Comparison of two groups in terms of sex distribution

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Table 4: Association of Internet addiction with possible risk/protective factors

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Factors with a positive and statistically significant association with IA were using Internet for watching pornography, online gaming, due to loneliness/boredom, use in Internet cafes, having a permanent login status and a personal device like PC, laptop or mobile, and consumption of tobacco or alcohol by any of the parents.

Factors with a negative and statistically significant association with IA were use of Internet for academics and having a playground near one's house and actually playing in playground. Certain factors like use for accessing social media, use of Internet in private study room or bedroom, and employment status of either or both parents had no significant association with IA.

  Discussion Top

The study was conducted among students of an urban school who did not differ much in their socioeconomic background. The access to Internet was universal. The prevalence of IA (2.5%) was comparable to earlier Indian studies which have reported a range from 0.5% to 8.7%.[5],[11],[12] The most common subtype of IA was cyber-relational, but most students with IA had more than one subtype of IA. Like previous studies among adolescents, this study also showed a significantly higher male preponderance for IA. The skewed male-to-female ratio (similar to other studies[5],[11],[12]) can be due to sociocultural factors like higher acceptance of mobile/Internet use for boys than girls.

Most participants of this study started accessing Internet for academics, out of curiosity or under peer or family influence. They continued using it for academics, social media, online gaming, or watching pornography etc., these findings were also similar to previous studies.

Factors which were positively associated with IA like watching pornography, online gaming, permanent login status, and having a personal device to access Internet were similar to other studies.[11],[23],[24] Our study found additional associated factors like access to Internet in Internet cafes and use of alcohol or tobacco by either of parents. These factors, being modifiable, provide an opportunity for prevention or early intervention. The use of inside cafes may provide seclusion and anonymity which becomes lucrative for these adolescents. The use of psychoactive substances by parents of these students indicated a possible biological and social vulnerability to addictions.

In addition, average daily use more than 4 h was significantly associated with IA, while daily use <2 h appeared to be protective. These findings confirm the recommendations in available literature to limit Internet use to <2 h in a day.[26] Use of Internet for social media was not significantly associated with IA. This was in variance to other studies.[11] Certain factors which may have had a protective effect were using Internet for academics and having a playground nearby and playing there. Protective association with these factors can be used in planning early intervention programs for IA. We included all students from age 10–19, thereby reducing the chances of selection bias. We ensured confidentiality by not obtaining any identifying data from the participants. This would have encouraged the participants to give accurate responses to the questionnaire, thereby reducing information bias. In addition, we considered a large number of variables based on previous studies to reduce confounding.

Permanent login status implies being logged on the particular app or social media platform even when your device is locked. This enables the user to get notifications and badges on the smartphone or wearable devices (smartwatch). This results in the user being “psychologically connected,” even though not currently using the device.

This was one of the largest cross-sectional studies of the prevalence of IA among school students, with 1325 participants. The study used a validated scale and considered a large number of sociodemographic variables and patterns of Internet use. Those who scored more than 69 (addicted) were given option of appropriate treatment (through their parents) of either pharmacotherapy or psychotherapy or both. The possible risk and protective factors can be a basis of further studies. Selection of participants from a single urban school may have restricted the sample in terms of variation in socioeconomic status as well as cultural and ethnic influences. This may limit the generalizability of results. The variables studied have an “association” with IA and do not indicate causality. To establish causality, analytical cross-sectional and longitudinal studies will be required.

  Conclusion Top

Our study suggests that IA is common among urban adolescents and is more common among males and among those who use Internet for watching pornography and online gaming. A history of substance abuse among parents increases vulnerability to IA, while having facilities for outdoor sports and playing them may be possibly protective.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

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  [Figure 1], [Figure 2]

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


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