The global network offered by the internet is widely used as a medium for interpersonal communication and entertainment by people across the world. Over a few decades, it has become an indispensable part of life. Adolescence is a transition period from childhood to adulthood, comprising of a wide range of 10–24 years and covering a broader portion of the life course. Children and adolescents are extremely fragile and sensitive and may be fascinated by the internet as a means of escape where they feel a sense of acceptance. They are mainly attracted to various social networking sites such as Facebook, WhatsApp, YouTube, and online games. Despite the numerous advantages bestowed by the internet, it poses perils for adolescents who have unlimited and unrestricted access to it. Eventually, this can lead to problematic internet use (PIU) or internet addiction (IA) necessitating treatment. A high concordance is seen in the manifestations expressed in PIU and traditional substance addiction such as salience, mood modification, tolerance, withdrawal, conflict, and relapse. Social network dependence among adolescents is often related to low self-esteem and a sense of social inadequacy among them. Furthermore, PIU results in significant impairment and distress, and is also associated with psychiatric disorders in adolescents. On the other hand, PIU or IA has not been included in neither the International Classification of Diseases, Eleventh Revision (ICD-11) nor the Diagnostic and Statistical Manual, Fifth Revision (DSM-5) due to poor consensus on conceptualizing it as a disorder. PIU is characterized by excessive or poorly controlled preoccupations, urges, or behaviors regarding computer use and Internet access that lead to impairment or distress. Multiple scales, questionnaires, and instruments are developed over time to measure PIU or IA. But the most commonly used reliable scale is the Young Internet Addiction Test (Y IAT) developed by Young. The Y-IAT was developed based on the criteria for pathological gambling diagnosis in DSM-IV. The scale consists of 20 items rated on a 5-point Likert scale yielding a total score categorizing addictive behavior into four categories: no addiction (0–30), mild signs of addiction (31–49), moderate signs of addiction (50–79), and severe addictive behavior (80–100).
Studies conducted in South Asian countries have reported alarming prevalence rates of internet usage among adolescents. Multiple studies conducted across various parts of the India have highlighted the magnitude of PIU at large, and several other studies have reported modest values for the same. A wide discrepancy, as reported in several studies, may be due to the inconsistent cutoff score used, use of diverse scales, cultural and social fabric existing across various regions, variable sample sizes, and survey approaches. Henceforth, the current systematic review and meta analysis were undertaken to identify the lacunae in the paucity of data on the national prevalence of PIU among adolescents justifying for increased investment in their health; or else the predominant programs for maternal and child health in our country becomes futile when we are not able to save the future generation of our country. Similarly, the psychological effects of PIU can hamper the productivity and scholastic performance of active minds, so a meticulous exploration of the issue can help avert the ill effects. Accordingly, this meta-analysis aims to estimate the pooled prevalence of PIU among school-going adolescents in India.
This systematic review is reported following the PRISMA checklist. We searched the following electronic bibliographic databases: PubMed, Web of Science, Scopus, EMBASE, and Google Scholar for articles published during the years 2010 to 2020. Besides, archives of relevant Indian journals were reviewed for maximum inclusion of available studies. The cross-references of the identified studies were explored for additional studies. We have used a combination of Medical Subject Headings (MeSH) and keywords to identify the various studies. The following MeSH terms were used: prevalence; epidemiology; internet addiction disorders; internet use; internet; adolescent; schools; students; India. The keywords used were the following: problematic internet use; internet addiction; smartphone addiction; internet gaming disorder; social media addictions; India; South-East Asia; low- and middle-income countries. For details on the search strategy and example in PubMed.
Inclusion and exclusion criteria
School-based prevalence studies conducted in the Indian setting that estimated PIU using the Y-IAT and published in the English language during the years 2010 to 2020 were included. In this study, the definition of PIU should be understood as a measured outcome variable based on the Y-IAT cutoff point of 50 and above. Studies with varying Y-IAT cutoff scores (Y-IAT 40 and above or cutoff points not mentioned) or different screening tools of PIU used were excluded.
Two researchers independently carried out the internet searches and selected potentially relevant articles from the search outputs by reading the study titles and abstracts. Additional two investigators independently appraised the full texts of appropriated records to reach a common consensus regarding the inclusion and exclusion of individual studies. No attempts were made to acquire grey/unpublished literature considering the inherent conflict of interest which might increase the risk of bias. All the eligible studies were further screened and data extraction was carried out based on the following study characteristics: author (year of publication), study setting (state/population), and sample size and sampling method, age, and prevalence as per the severity of PIU and as per gender. Any disagreement about inclusion criteria and scoring of the methodological quality of the included studies were resolved through mutual discussion and by reaching a consensus.
The methodological quality of included studies was assessed by two independent reviewers employing the JBI Critical Appraisal Checklist for Studies Reporting Prevalence Data. This checklist contains nine criteria with a total quality score ranging from 1 to 9. We classified scores as having high (0–3), moderate (4–6), and low (7–9) risk of bias. The score of the included studies was not considered for the study selection criterion. Discrepancies in the quality scoring of two reviewers were addressed by a third reviewer.
Meta-Analyst software was used to perform the meta-analysis. The statistical heterogeneity was addressed using Cochran’s Q and I2 statistics. The value was interpreted in the following ways in our study: 0%, 25%, 50%, and 75% as zero, low, medium, and high heterogeneity, respectively. The fixed-effect model was used to estimate the pooled prevalence in our meta-analysis due to medium heterogeneity among studies (I2 = 49.1% and Cochran’s Q = 99.7, P < 0.001). The funnel plot was used to assess potential publication bias.
Description of study characteristics
Of the 544 screened, 14 eligible articles were identified and included in the meta-analysis [Figure 1. The characteristics of the included studies are summarized in Table 1. The geographical heterogeneity across the diverse country was addressed by incorporating studies from different areas: two studies from South India, six from North India, one from the northeast part of India, two from the central part of India;  and four from the western part of India. The study population comprised of school-going adolescents from classes 8 to 12 within an age group ranging from 13 to 19 years. Besides, the majority of the studies focused on adolescents from class 11 to 12 (11 studies) and a small number of studies assessed students from class 8 to 10 with an overall sample size of 7,946, with the sample size in each study ranging from 150 to 1,312. Most of the studies utilized non-probability sampling methods (convenience-09), while a few of the studies (n = 06) employed probability sampling methods. A wide variation in the prevalence of moderate addiction has been reported in studies with a modest value of 6% to the highest value of 32.9%. Furthermore, a study from Gujarat reported a prevalence of 13.7% had the largest sample size among the included studies (n = 1312). Meager values were reported for severe addiction in almost all of the studies. Gender difference in prevalence of IA has also been brought out in the review. The majority of the studies indicated a higher prevalence of IA among males, but a study from central India reported a higher prevalence among females than males.
Prevalence of PIU among school-going adolescents in India
The estimated effect of interest for this study was the pooled prevalence of PIU (Y-IAT ≥50). We used the DerSimonian and Laird method of fixed-effects models to calculate the pooled prevalence of IA, as the magnitude of observed heterogeneity was statistically significant (I2 = 49.1% and Cochran’s Q = 99.7, P < 0.001). The overall pooled estimate of the prevalence of PIU was 21.5% (95% CI: 17.0%–26.8%) and 2.6% (95% CI: 1.6%–4.2%) based on the Y-IAT cutoff point of 50 and 80, respectively [Figure 2].
Nine studies were classified as having a low risk of bias and the remaining six as having a moderate risk of bias. The quality score ranged from 4 to 9 with a median value of 7 and a mean of 6.4. The overall quality of evidence was rated as “moderate” based on the following criteria under GRADE assessment:
- Risk of bias was evaluated based on Joanna Briggs Institute (JBI) critical appraisal checklist for studies reporting prevalence data. The median and mean score was 7 and 6.4, respectively, in which the total score ranged from 1 to 9. Therefore, no serious risk of bias was identified.
- Inconsistency: No serious inconsistency in results was noted as the I2 value was less than 50%.
- Indirectness: Approximately 60% of the studies had an adequate sample frame to address the target population. Therefore, no serious indirectness in the outcome measure was identified.
- Imprecision: There was a wide CI around the pooled prevalence estimates.
- Publication bias: The visual examination of the funnel plot was performed to assess the publication bias and it was found to be symmetrical.
In subgroup analysis, the year of publication, gender, sampling method, and severity of addiction had a significant effect on the prevalence estimates [Table 2]. The pooled prevalence of PIU among studies published during 2010–2015 was 23.1% (95% CI: 21.5%–24.7%) and 2016–2020 was 17.0% (95% CI: 13%–23.0%). The aggregate estimate of the prevalence of PIU was higher among males than females (19.4%; 95% CI: 18.0%–20.8% vs 12.9%; 95% CI: 11.5%–14.4%). The estimates for studies conducted through convenience sampling was 16.8% (95% CI: 15.6%–18.1%) and for random sampling was 26.9% (95% CI: 24.9%–29.0%). The prevalence of moderate addiction was significantly higher when compared to severe addiction (20.1%; 95% CI: 16%–24.8% vs 2.6%; 95% CI: 1.6%–4.2%). However, no statistically significant difference in prevalence was observed based on year of schooling (P = 0.07).
A leave one-out sensitivity analysis was carried out to address the potential influence of any single study on the overall pooled effect. There was no significant impact of any particular study on the overall pooled effect for PIU among school-going adolescents (21.5%) and the values ranged between 20.6% (16.2%–25.9%) and 22.4% (17.8%–27.8%).
The major findings of the review are the estimation of a pooled prevalence of PIU in Indian school-going adolescents. The overall prevalence of moderate PIU was 21.5% (95% CI: 17.0%–26.8%) based on the Y-IAT cutoff score of 50. This pooled estimate of PIU in Indian adolescents is comparable to the magnitude reported in studies from Hong Kong (17%–26.8%), China (26.5%), Taiwan (24%), Iran (21%) but slightly higher than those reported from Turkey (15%). The overall estimates of 2.6% of severe PIU in this study are found to be similar to the figure in Croatia (3.4%; Y-IAT ≥70), Egypt (2.6%; Y-IAT ≥80), and Iran (3.7%; Y-IAT ≥80). However, these comparisons should be interpreted with caution due to the discrepancies in screening instruments, criteria, and study context. It is important to note that our study reflects the exact magnitude of PIU among Indian school students based on the measurement of a single screening instrument: the Y-IAT. Many previous studies investigating the severity of PIU used different Y-IAT cutoff points such as 40, 50, 70, and 80. We found aggregate estimates of moderate and severe PIU as 21.5% and 2.6% based on the Y-IAT cut-off points of 50 and 80 respectively. Referring to some previous studies, there are conflicting results regarding the Y-IAT detection rate of PIU/IA across the world. The magnitude of PIU based on studies conducted in the Indian college studies using a Y-IAT cutoff point of 40 ranged from 20% to 33%. Furthermore, two school-based cohort studies using the Y-IAT (cutoff score of 50) of a large number of school-going adolescents yielded a wide variation in the prevalence rate of PIU/IA in China (12.2% vs 23.7%). All of these results suggest the need for further research in the reconsideration of cutoff points of the Y-IAT among Indian adolescents.
Currently, the use of the internet is rampant in India, and the findings of this meta-analysis reflect national-level data on the magnitude of PIU among school-going adolescents. The level of heterogeneity was moderate (I2 = 49.1%) and most of the included studies had a low risk of bias in terms of methodological quality. We detected a higher prevalence in male students (19.4%) than in female students (12.9%) in this setting, which is similar to studies conducted abroad. It may be explained by the fact that males are more attracted to the wider utility of the internet such as online games than females. After subgroup analyses based on the year of publication and sampling, we found that the prevalence rate ranged from 17.0% to 26.9%. Taken together, we identified that nearly one-fifth of the Indian school students were at risk of PIU, which is similar to other studies conducted among college students in India but different from those conducted in China. Our findings also reflect that compared with other screening instruments, the Y-IAT detection rate of PIU among adolescents in India was higher than in Turkey (15.1%), China (8.8%), Korea (4.3%), and Japan (7.9%).
Strength and limitations
To the best of our knowledge, this is the first meta-analysis that evaluated the pooled prevalence rate of PIU among Indian school-going adolescents. Most of the included studies were rated as moderate quality and the studies covered in this meta-analysis were conducted in different geographic areas of India, which makes the sample representative of Indian school-going adolescents. However, there are some limitations. Although the assessment of PIU was based on the Y-IAT tool, the diagnosis was not confirmed in any of the studies. Factors that may influence the prevalence of PIU were not examined due to the paucity of such data. Furthermore, the purpose and context of internet use has changed due to the COVID-19 pandemic. Hence, comparing studies from 2010 to 2020 might not reflect the exact magnitude of the problem. Considering the moderate heterogeneity among studies, the overall prevalence estimate needs to be treated with caution.
Currently, the use of the internet is rampant in India and our findings reflect nationally representative data on the magnitude of PIU among school going adolescents. About one-fifth of school-going adolescents are at risk for PIU in this setting. There is a need for further research in the reconsideration of cutoff points of the Y-IAT among Indian adolescents.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
1. Haythornthwaite C Introduction:The internet in everyday life Am Behav Sci 2001 45 363–82
2. Sawyer SM, Azzopardi PS, Wickremarathne D, Patton GC The age of adolescence Lancet Child Adolesc Heal 2018 2 223–8
3. Griffiths MD, Kuss DJ, Demetrovics Z Social networking addiction:An overview of preliminary findings Behavioral addict 2014 119–41
4. Van Rooij AJ, Prause N A critical review of “internet addiction”criteria with suggestions for the future J Behav Addict 2014 3 203–13
5. Thadani DR, Cheung CM Online social network dependency:Theoretical development and testing of competing models In 2011 44th Hawaii International Conference on System Sciences 2011 1–9 IEEE USA DOI:10.1109/HICSS.2011.331
6. Tsai MH, Huang YS Attention-deficit/Hyperactivity disorder and sleep disorders in children Med Clin North Am 2010 94 615–32
7. Block JJ Issues for DSM-V:Internet addiction Am J Psychiatry 2008 165 306–7
8. Weinstein A, Lejoyeux M Internet addiction or excessive internet use Am J Drug Alcohol Abuse 2010 36 277–83
9. Young KS Internet addiction:The emergence of a new clinical disorder Cyberpsychology Behav 1998 1 237–44
10. Mak K-K, Lai C-M, Watanabe H, Kim D-I, Bahar N, Ramos M, et al Epidemiology of internet behaviors and addiction among adolescents in six Asian countries Cyberpsychol Behav Soc Netw 2014 17 720–8
11. Balhara YPS, Mahapatra A, Sharma P, Bhargava R Problematic internet use among students in South-East Asia:Current state of evidence Indian J Public Health 2018 62 197–210
12. Mathew P, Krishnan R Impact of problematic internet use on the self-esteem of adolescents in the selected school
, Kerala, India Arch Psychiatr Nurs 2020 34 122–8
13. Moher D, Liberati A, Tetzlaff J, Altman DGfor the PRISMA Group Preferred reporting items for systematic reviews and meta-analyses:The PRISMA statement BMJ 2009 339 332–6
14. Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data Int J Evid Based Healthc 2015 13 147–53
15. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR A basic introduction to fixed-effect and random-effects models for meta-analysis Res Synth Methods 2010 1 97–111
16. Grover D, Joseph J The prevalence of internet addiction among school
-going adolescents:A comparative assessment as per two screening criteria J Mental Health Hum Behav 2020 25 133–7
17. Vadher SB, Panchal BN, Vala AU, Ratnani IJ, Vasava KJ, Desai RS, et al Predictors of problematic internet use in school
going adolescents of Bhavnagar, India Int J Soc Psychiatry 2019 65 151–7
18. Kumar N, Kumar A, Mahto SK, Kandpal M, Deshpande SN, Tanwar P Prevalence of excessive internet use and its correlation with associated psychopathology in 11th
grade students Gen Psychiatr 2019 32 e100001
19. Saikia AM, Das J, Barman P, Bharali MD Internet addiction and its relationships with depression, anxiety, and stress in urban adolescents of Kamrup district, Assam J Family Community Med 2019 26 108–12
20. Goswami A, Singh J, Kumar P Factors responsible for internet addiction among adolescents of Central India J Med Sci Clin Res 2018 6 249–53
21. Kayastha B, Gurung A, Chawal R A descriptive study to assess the level of internet addiction among adolescents:A case study of high schools in Mangalore J Child Adolesc Behav 2018 6 378
22. Arthanari S, Khalique N, Ansari MA, Faizi N Prevalence &determinants of internet addiction among Indian adolescents Indian J Comm Health 2017 29 89–95
23. Sharma KD, Gupta ID, Gunjan SV, Sharma R, Sharma D Internet addiction pattern among high school
students of Jaipur city:A descriptive study Int Multispecialty J Health. 2016 2 25–31
24. Bhatia M, Rajpoot M, Dwivedi V Pattern of internet addiction among adolescent school
students of a North Indian city Int J Community Med Public Heal 2016 3 2459–63
25. Prabhakaran AM, Patel VR, Ganjiwale DJ, Somashekhar Nimbalkar M Factors associated with internet addiction among school
-going adolescents in Vadodara J Fam Med Prim Care 2016 5 765–9
26. P B SK, Prasad N, Raj Z, Abraham A, M V, Bal A, et al Internet addiction and substance use disorders in adolescent students-A cross sectional study J Int Med Dent 2015 2 172–9
27. Mali KH, Sawardekar P, Anjenaya S, Mahadik VJ A study to assess internet addiction among adolescent junior college students of Navi Mumbai, India Med Int Med J 2015 2 894–96 Available from https://www.medpulse.in/Article/Volume2Issue12/MedPulse_2_12_16.pdf
Last accessed on 2022 May 09
28. Anwar E Internet addiction among secondary school
children and its relation with their academic achievements education Indian J Res 2014 3 71–4
29. Yadav P, Banwari G, Parmar C, Maniar R Internet addiction and its correlates among high school
students:A preliminary study from Ahmedabad, India Asian J Psychiatr 2013 6 500–5
30. Meena PS, Soni R, Jain M, Paliwal S Social networking sites addiction and associated psychological problems among young adults:A study from North India Sri Lanka J Psychiatry 2015 6 14
31. Sedgwick P, Marston L How to read a funnel plot in a meta-analysis BMJ 2015 351 h4718
32. Xin M, Xing J, Pengfei W, Houru L, Wang M, Hong Z, et al Online activities, prevalence of Internet addiction and risk factors related to family and school
among adolescents in China Addict Behav Rep 2018 7 14–8
33. Shek DTL, Yu L Adolescent internet addiction in Hong Kong:Prevalence, change, and correlates J Pediatr Adolesc Gynecol 2016 29 1 Suppl S22–30
34. Yu L, Shek DTL Internet addiction in Hong Kong adolescents:A three-year longitudinal study J Pediatr Adolesc Gynecol 2013 26 3 Suppl S10–7
35. Lin MP Prevalence of internet addiction during the Covid-19 outbreak and its risk factors among junior high school
students in Taiwan Int J Environ Res Public Health 2020 17 8547
36. Modara F, Rezaee-Nour J, Sayehmiri N, Maleki F, Aghakhani N, Sayehmiri K, et al Prevalence of internet addiction in Iran:A systematic review and meta-analysis Addict Heal 2017 9 243–52
37. Şaşmaz T, Öner S, Kurt AÖ, Yapici G, Yazici AE, Bugdayci R, et al Prevalence and risk factors of internet addiction in high school
students Eur J Public Health 2013 24 15–20
38. Černja I, Vejmelka L, Rajter M Internet addiction test:Croatian preliminary study BMC Psychiatry 2019 19 388
39. Kamal NN, Mosallem FAEH Determinants of problematic internet use among El-Minia high school
students, Egypt Int J Prev Med 2013 4 1429–37
40. Ahmadi K, Ahmadi K Internet addiction among Iranian adolescents:A nationwide study Acta Med Iran 2014 52 467–72
41. Ali A, Horo A, Swain MR, Gujar NM, Deuri SP The prevalence of internet addiction and its relationship with depression, anxiety and stress among higher secondary school
students:North-East perspective J. Indian Assoc Child Adolesc Ment Health 2019 15 13–26
42. Sowndarya TA, Pattar M Pattern of internet addiction among urban and rural school
students, Mangaluru, India:A comparative cross-sectional study Int J Contemp Pediatr 2018 5 1750
43. Upadhyay P, Jain R, Tripathi VN A study on the prevalence of internet addiction and its association with psychopathology in Indian adolescents Indian J Neurosci 2017 3 56–60
44. Goel D, Subramanyam A, Kamath R A study on the prevalence of internet addiction and its association with psychopathology in Indian adolescents Indian J Psychiatry 2013 55 140–3
45. Wang H, Zhou X, Lu C, Wu J, Deng X, Hong L Problematic internet use in high school
students in Guangdong Province, China PLoS One 2011 6 e19660
46. Xu D-D, Lok K-I, Liu H-Z, Cao X-L, An F-R, Hall BJ, et al Internet addiction among adolescents in Macau and mainland China:Prevalence, demographics and quality of life Sci Rep 2020 10 16222
47. Vigna-Taglianti F, Brambilla R, Priotto B, Angelino R, Cuomo GL, Diecidue R Problematic internet use among high school
students:Prevalence, associated factors and gender differences Psychiatry Res 2017 257 163–71
48. Joiner R, Gavin J, Brosnan M, Cromby J, Gregory H, Guiller J, et al Gender, internet experience, internet identification, and internet anxiety:A ten-year followup Cyberpsychol Behav Soc Netw 2012 15 370–2
49. Joseph J, Varghese A, Vr V, Dhandapani M, Grover S, Sharma S, et al Prevalence of internet addiction among college students in the Indian setting:A systematic review and meta-analysis Gen Psychiatr 2021 34 e100496
50. Shao Y-J, Zheng T, Wang Y-Q, Liu L, Chen Y, Yao Y-S Internet addiction detection rate among college students in the People's Republic of China:A meta-analysis Child Adolesc Psychiatry Ment Health 2018 12 25
51. Xu J, Shen L-x, Yan C-h, Hu H, Yang F, Wang L, et al Personal characteristics related to the risk of adolescent internet addiction:A survey in Shanghai, China BMC Public Health 2012 12 1106
52. Jang KS, Hwang SY, Choi JY Internet addiction and psychiatric symptoms among Korean adolescents J Sch Health 2008 78 165–71
53. Mihara S, Osaki Y, Nakayama H, Sakuma H, Ikeda M, Itani O, et al Internet use and problematic internet use among adolescents in Japan:A nationwide representative survey Addict Behav Rep 2016 4 58–64