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| ORIGINAL ARTICLE |
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| Year : 2015 | Volume
: 59
| Issue : 2 | Page : 115-121 |
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Internet addiction: Prevalence and risk factors: A cross-sectional study among college students in Bengaluru, the Silicon Valley of India
Sharmitha Krishnamurthy1, Satish Kumar Chetlapalli2
1 Student Pursuing Masters in Public Health, SRM University, Chennai, Tamil Nadu, India
2 Professor Dean, School of Public Health, SRM University, Chennai, Tamil Nadu, India
| Date of Web Publication | 25-May-2015 |
Correspondence Address:
Sharmitha Krishnamurthy
Medical College Building, Third Floor, SRM University, Kattankulathur - 603 203, District - Kancheepuram, Chennai, Tamil Nadu
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/0019-557X.157531
 Abstract | | |
Background: The Internet is a widely used tool known to foster addictive behavior, and Internet addiction threatens to develop into a major public health issue in the near future in a rapidly developing country like India. Objective: This cross-sectional study intends to estimate prevalence, understand patterns, and evaluate risk factors for Internet addiction among college students in the city of Bengaluru, India. Materials and Methods: Out of a total of 554 data samples from eight colleges selected through multistage cluster sampling, 515 samples were analyzed. Young's 20-item Internet Addiction Test (IAT), an inventory including demographic factors and patterns of internet use, was administered. Results: This study of college students aged 16-26 years (mean ± SD 19.2 ± 2.4 years), with marginally high female representation (56%), identified 34% [95% confidence interval (CI) 29.91-38.09%] and 8% (95%, CI 5.97-10.63%) as students with mild and moderate Internet addiction respectively. Binary logistic regression found Internet addiction to be associated with male gender [adjusted odds ratio (AOR) 1.69, 95% CI, 1.081- 2.65, P = 0.021], continuous availability online (AOR 1.724, 95% CI, 1.018-2.923, P = 0.042), using the Internet less for coursework/assignments (AOR 0.415, 95% CI, 0.263-0.655, P < 0.001), making new friendships online (AOR 1.721, 95% CI, 1.785-2.849, P = 0.034), getting into relationships online (AOR 2.283, 95% CI, 1.424-3.663, P = 0.001). Conclusion: The results highlight the vulnerability of college students to Internet addiction. The findings provide explanations on the addictive behavior of the internet users, support the inclusion of "Internet Addiction" in the DSM-VI, and open up new paths for further research. Keywords: College students, Internet addiction, Internet use patterns, prevalence, risk factors, training center
How to cite this article:
Krishnamurthy S, Chetlapalli SK. Internet addiction: Prevalence and risk factors: A cross-sectional study among college students in Bengaluru, the Silicon Valley of India. Indian J Public Health 2015;59:115-21 |
How to cite this URL:
Krishnamurthy S, Chetlapalli SK. Internet addiction: Prevalence and risk factors: A cross-sectional study among college students in Bengaluru, the Silicon Valley of India. Indian J Public Health [serial online] 2015 [cited 2023 Mar 27];59:115-21. Available from: https://www.ijph.in/text.asp?2015/59/2/115/157531 |
| Background | |  |
In the new generation, the Internet has become an important tool for education, entertainment, communication, and information-sharing. Easy access and social networking are two of the several aspects of the Internet fostering addictive behavior. [1] In tandem with the splurge in access to the Internet globally, with the rise of new-generation gadgets, the risk of "internet addiction" is emerging as a significant behavioral addiction pandemic to be tackled worldwide. [2] The developing countries are not spared either due to extreme infiltration of technology even into the remotest corners. The population of India is around 1.2 billion as of 2012, of which the number of Internet users (both urban and rural) is around 205 million. It is estimated to increase to 243 million by June 2014, and India will be the second-leading country after China which currently has the highest Internet user base of 300 million. [3]
Internet addiction commonly refers to an individual's inability to control his or her use of the Internet (including any online-related, compulsive behavior), which eventually causes one's marked distress and functional impairment in daily life. Research studies in the Western and Asian contexts suggest that the risk of Internet addiction among young people is increasing. [4]
Internet addiction in adolescence can have a negative impact on identity formation and may negatively affect cognitive functioning, lead to poor academic performance and engagement in risky activities, and inculcate poor dietary habits. [5]
College students are especially vulnerable to developing dependence on the Internet, more than most other segments of the society. This can be attributed to several factors including the following: Availability of time; ease of use; unlimited access to the Internet; the psychological and developmental characteristics of young adulthood; limited or no parental supervision; an expectation of Internet/computer use implicitly if not explicitly, as some courses are Internet-dependent, from assignments and projects to communication with peers and mentors; the Internet offering a route of escape from exam stress, [6] all of which make Internet overuse a significant cause of concern for parents and faculty.
As per DSM-V, Internet addiction is not yet recognized as a disorder, but is being considered as an area in need of further research. [7] Though there are innumerable studies globally depicting a worldwide scenario of the behavioral addiction phenomena, a lot of these studies [8] have utilized inconsistent criteria to rate the levels of addiction, applied recruiting methods that may have caused serious sampling bias, and examined data using primarily exploratory rather than confirmatory data analysis techniques to investigate the degree of association rather than a causal relationship among variables. The absence of large-scale epidemiological studies and huge disparities in the use of diagnostic criteria have resulted in difficulties in establishing the prevalence of Internet addiction. [9] This paper attempts to understand the patterns, prevalence, and risk factors for Internet addiction among college students in Bengaluru, the Silicon Valley of India. Unfortunately, there is no similar published study from the Information Technology (IT) Capital of India.
| Materials and Methods | | |
This cross-sectional study was carried out in eight different colleges across different streams (Arts, Science, and Computer Science) in the city of Bengaluru during the period June-July 2013. It covered about 600 college students aged 16-26 years. Study participants were from graduate and postgraduate colleges. Of the total 600 students, 554 returned filled questionnaires, around 10 could not be included in the study as they were not Internet users, and 29 submitted forms that were incomplete. Thus, a total of 515 students were finally included in the study. Of the studied sample, 56% were females. College approval and written informed consent were obtained for all students who participated. The study was approved by the SRM University School of Public Health Ethics Committee. A pilot study was done on 20 students; subsequent suggestions were incorporated before the start of the study.
The sample size was calculated for an assumed prevalence of Internet addiction being 50% (as the exact measure from studies using a similar rating scale was unavailable), and for a 95% confidence level and 5% absolute precision of the estimate. A 30% oversampling was incorporated to account for nonresponse.
Sample selection
A multistage cluster random sampling design was applied to target recruitment. Bengaluru has eight zones under Bangalore Bruhat Mahanagara Palike. All wards under each zone were listed and one ward from each zone was randomly selected. Senior secondary, undergraduate, masters, graduate, and postgraduate colleges in the selected wards were randomly selected and then contacted for permission to conduct the survey. The survey was made with students of all the colleges where permission was granted. Of the 15 colleges that were contacted, eight colleges gave immediate permission to conduct the survey. The remaining seven colleges requested to be contacted later.
Data collection and measures of Internet addiction
All questionnaires were distributed to the participants in classroom settings at a predetermined time and were collected onsite after 30 min. The questionnaires were anonymous and self-administered. Teachers left the classrooms during the 30-min period to avoid any bias, influence, or hesitancy.
The questionnaire contained three parts:
- Sociodemographic information,
- Details regarding patterns of internet use, and
- Young's Internet Addiction Test (IAT). Results and discussion with respect to the prevalence of Internet addiction, patterns of Internet use, and risk factors for Internet addiction have been discussed below.
Young's 20-item scale for Internet addiction (YIAT 20) was applied to qualify for the prevalence of Internet addiction. It is a 20-item questionnaire measured on the five-point Likert Scale. After all the questions have been answered, numbers for each response are added to obtain a final score. The higher the score range, the greater the level of addiction; normal range: 0-30 points, mild: 31-49 points, moderate: 50-79 points, and severe: 80-100 points. [10] The excellent psychometric properties of the questionnaire are well-documented in the literature. [11] Young's IAT, developed for screening and measuring levels of Internet addiction, has been the most widely used and well-tested for its psychometric properties. [12] The items of the IAT, each rated from 1 (rarely) to 5 (always), include compulsive behavior related to use of the Internet, the occupational or academic difficulties, lack of competence at home, problems in interpersonal relations, and emotional problems. [13] The rationale for choosing Young's diagnostic questionnaire for the study was that it is the first global psychometric measure and hence has been extensively and frequently used across many studies globally, is self-completed, has been validated on adult and adolescent populations, and has good internal consistency reliability as well as concurrent validity. In a recent meta-analysis study drawing from a large sample of studies conducted to determine the overall value for the reliability YIAT20, the mean differences showed that it is more reliable in college students and probably in Asia. The overall Cronbach's computed from the studies was 0.889 [95% confidence interval (CI) 0.884-0.895]. The standard deviation of the alpha was low, at 0.049. [14]
Statistical analysis
0The SPSS version 17.0 (IBM SPSS Statistics) was used for statistical analysis of the data collected. Sociodemographic variables and patterns of Internet use have been denoted by frequency tables. The prevalence of Internet addiction was described in terms of percentage. Descriptive statistics has been used to examine the association of factors of the questionnaire with Internet addiction. The frequency and odds ratio with CI has been reported for all variables where the P values (<0.05) was significant. Binary logistic regression was performed with Internet addiction as the dependent variable and independent variables including several demographic and other variables. In all calculations, P values under 0.05 were considered significant.
| Results | | |
Socio demographic Characteristic of the study population and pattern of Internet use are depicted in [Table 1] and [Table 2]. Time spent per day using the Internet and amount spent per month on the Internet that could be the consequence of excessive Internet use were proportionately high among those with moderate and mild addiction, and these significant outcomes of Internet addiction are depicted in [Table 3]. | Table 1: Sociodemographic characteristics of study participants (n = 515)
Click here to view |
With absolutely no prevalence of severe internet addiction, moderate levels of addiction seem to be at par with what has been reported elsewhere in the literature in the same population. Mild Internet addiction though is marginally on the higher end. The exact findings have been reported in [Table 4].
Then, factors associated with Internet addiction were investigated, for which the chi-square test was performed. Male gender; initial years of coursework; permanently logged-in status; peer influence; preference for virtual interaction with friends; and using the Internet for chatting, pornography, making new friendships, getting into relationships online, and shopping were potential influential factors (P value <0.05), whereas age, medium of instruction during schooling, place of stay, father's/mother's occupation, years of computer and Internet use, gadgets (e.g., desktop, laptop, mobile phone) and mode of Internet access (e.g., Wi-Fi, data card), supervision during Internet use, time spent by parents using the Internet outside work hours; time spent by any one sibling on the Internet, preference for virtual interaction with relatives, and using the Internet for news, updates, checking mails, entertainment, social networking, and playing games were not found to be significantly influential factors. A summary of the significant risk factors has been shown in [Table 5].
Binary logistic regression analysis was used to find the most influential predictors affecting outcome (Internet addiction) by using the backward stepwise (conditional) technique. These were male gender, using the Internet for making new friendships and getting into relationships online, and having permanently logged-in status increased risk for Internet addiction, whereas using the Internet more for coursework/assignments was favored as a protective factor for addiction.
| Discussion | | |
The literature has termed Internet addiction frequently in synonymous with pathological Internet use, [15] compulsive Internet use, [16] and problematic Internet use. All of these share some common elements, such as excessive use of the Internet, withdrawal, tolerance, and negative consequences for interpersonal or personal well-being with respect to diagnostic criteria. [17],[18] In the current study, a Cronbach's alpha of 0.887, P = 0.000 suggested good internal consistency of the overall questionnaire. The prevalence of mild Internet addiction [34% (95% C.I 29.91%-38.09%] was higher than global past estimates, whereas moderate levels of addiction [8% (95% C.I 5.97%-10.63%] was almost on the similar lines. For a study conducted among Japanese college students, a sensitivity of 0.87 and a specificity of 0.98 were reported for the same screening tool. [19] Using this, the true prevalence of the addiction rates as reported in [Table 4] was calculated using a formula (Test Positivity Rate + Specificity)-1/(Sensitivity + Specificity)-1. The patterns of Internet use are extremely varied, with the majority of students having been using computers for more than 5 years, using Internet for less than 5 h a day, the mobile phone being the most preferred gadget for Internet use, spending less than Rs. 300 per month for Internet usage, logging in occasionally during the day, and not being under any supervision.
Students with Internet addiction had twice the odds of spending more than 35 h per week on the Internet and spending more than Rs. 300 per month as compared to their nonaddict counterparts. "Although time is not a direct function in diagnosing internet addiction, early studies suggested that those classified as dependent online users were generally excessive about their online usage, spending anywhere from 40 to 80 hours per week, with sessions that could last up to 20 hours". found that respondents with five or more symptoms of internet addiction averaged 35 hours a week online, compared to 27 hours for those with fewer symptoms". [10]
Based on the self-reported responses to specific questions on usage of the Internet, multiple risk factors such as the following were identified in this study: Male gender; initial years of coursework; permanently logged-in status; peer influence; preference for virtual interaction with friends; and using the Internet for chatting, pornography, making new friendships, and getting into relationships online.
The variables that contributed strongly were identified by binary logistic regression and represented by a model. They included not using the Internet for coursework assignments but for making new friendships, permanent login status, male gender, and getting into new online relationships or breaking existing relationships.
College students, probably due to the psychological and developmental characteristics of young adulthood and limited or no parental supervision, are more susceptible to getting into online friendships, which eventually most often turns into online relationships. The findings of this study suggest that male students tend to use the Internet more frequently than females. These findings are supported by the findings of various studies in the literature. [20],[21],[22],[23]
In a recently published Indian study carried out on professional course students in the 15-25-years age group in Jabalpur city, Madhya Pradesh, in which the same Young's 20-item IAT scale and scoring pattern was used, [24] out of the 391 students who participated in the study, 55% were male. The mean age of the students was 19.02 (±1.450) years. Male students were more addicted to the Internet than female students. The IAT scoring revealed 57.3% as normal users, 35.0% as mildly addicted to the Internet, 7.4% as moderately addicted, and 0.3% as severely addicted.
In a study carried out by Choi et al. in 2008, it was reported that the case of Internet addiction was more common in male students compared to female students, and in 2001, Hahn and Jerusalem reported that males used the Internet more than females; however, the Internet usage levels of females have increased in recent years. [25] The risk factors for Internet addiction, identified through this study range from personal, social, and behavior-specific factors, highlight the multifactorial model of development of Internet addiction. Further research is warranted to study each risk factor in detail.
Strengths and limitations
The study has been designed scientifically at various levels to significantly avoid bias. The main strength of the study includes scientific selection of samples. Multistage cluster sampling ensured that the study could be completed in limited time. Another aspect was the heterogeneous representation of college students. Sampling bias has always been a major drawback of previously conducted studies. This study has tried to significantly fill this void, as the study did not recruit participants through email, group networks, and postings on websites designed for Internet or other addicts, thereby limiting itself to a self-selected sample of participants who have some interest or psychological investment in the topic and would have been more likely to participate, thus leading to a biased sample. Participants were recruited from both public and private colleges with academically diverse backgrounds, including courses that did not necessarily require heavy Internet usage, which limited generalizability of the results due to the lack of a representative sample. Questionnaires were answered anonymously, and teachers were kept away from the classrooms where information was being collected. Anonymous answering of the questionnaires and data analysis after pooling ensured that the participants could provide more factual and credible answers without the fear of later consequences.
Along with the nonconsideration of design effect in the calculation of sample size, the two relatively major drawbacks were recall bias and social desirability bias. First, this being a retrospective study and participants being asked to report details of past exposure to/use of the Internet, recall bias cannot be ruled out. Second, there was self-reporting of data, and hence social desirability bias could be present, as the study participants may have responded in such a way as to portray themselves in a good light.
| Conclusions | | |
India is a developing country that is embracing technological growth at a pace faster than ever. Bengaluru is considered its IT and education hub. The understanding that Internet use can be a disorder is still in its initial stages in India, and excessive Internet use is an emerging public health issue as research findings have highlighted that excessive use of the Internet adversely affects one's physical and mental health and social well-being. There are very limited studies establishing the prevalence of Internet addiction in Bengaluru. Globally, a number of studies have tried to analyze similar risk factors associated with Internet addiction, and the results of this study provide evidence to support the findings of prior research from an Indian context. This study's results imply that Internet addiction is a prevalent public health issue, having multiple risk factors and varied patterns of Internet use, in a place where the Internet is becoming an inclusive component of an individual's personal and social life. The need of the hour is to create awareness among the public, plan public health policies with regard to this behavioral addiction, and conduct further research to support the same.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]
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Associations of personality traits with internet addiction in Chinese medical students: the mediating role of attention-deficit/hyperactivity disorder symptoms |
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| Meng Shi,Tian Jiao Du | | BMC Psychiatry. 2019; 19(1) | | [Pubmed] | [DOI] | | | 31 |
Psychological risk factors associated with internet and smartphone addiction among students of an Indian dental institute |
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| Sumeet Bhatt,Ambika Gaur | | Indian Journal of Public Health. 2019; 63(4): 313 | | [Pubmed] | [DOI] | | | 32 |
A Multifaceted Approach to Understand the Problem of Internet Addiction Among the Young Indian Students |
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| Sumanjeet Singh | | Development. 2019; 62(1-4): 86 | | [Pubmed] | [DOI] | | | 33 |
Problematic internet use and its correlates among resident doctors of a tertiary care hospital of North India: A cross-sectional study |
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| Sandeep Grover,Swapnajeet Sahoo,Ashish Bhalla,Ajit Avasthi | | Asian Journal of Psychiatry. 2019; 39: 42 | | [Pubmed] | [DOI] | | | 34 |
A Study on internet addiction among teenagers in selangor state of malaysia |
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| Murugesan Raja Kumar,Siti Khadijah Binti Mohd Hamzaht,Siti Khadijah Supriyanto,W. Martiningsih,R. Wiryadinata,S. Praptodiyono,M.I. Santoso,I. Saraswati | | MATEC Web of Conferences. 2018; 218: 03018 | | [Pubmed] | [DOI] | | | 35 |
Extent and pattern of problematic internet use among school students from Delhi: Findings from the cyber awareness programme |
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| Yatan Pal Singh Balhara,M. Harshwardhan,Rajeev Kumar,Shalini Singh | | Asian Journal of Psychiatry. 2018; 34: 38 | | [Pubmed] | [DOI] | | | 36 |
Internet Use Behaviors, Internet Addiction and Psychological Distress among Medical College Students: A Multi Centre Study from South India |
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| Nitin Anand,Anish V. Cherian,Christofer Thomas,Chriss Thomas,Prathyusha Vasuki,Kimberly Young | | Asian Journal of Psychiatry. 2018; | | [Pubmed] | [DOI] | | | 37 |
Prevalence of Internet addiction disorder in Chinese university students: A comprehensive meta-analysis of observational studies |
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| Lu Li,Dan-Dan Xu,Jing-Xin Chai,Di Wang,Lin Li,Ling Zhang,Li Lu,Chee H. Ng,Gabor S. Ungvari,Song-Li Mei,Yu-Tao Xiang | | Journal of Behavioral Addictions. 2018; 7(3): 610 | | [Pubmed] | [DOI] | | | 38 |
INTERNET USE BEHAVIOR: EMERGING STUDENT MENTAL HEALTH PROBLEM |
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| Samina Qadir,Ayesha Naeem,Muhammad Naeem Akhtar | | Gomal Journal of Medical Sciences. 2018; 16(2): 39 | | [Pubmed] | [DOI] | | | 39 |
Internet Gaming Addiction Presenting as Withdrawal Psychosis: An Unusual Case Report |
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| Prosenjit Ghosh,Sujit Sarkhel | | Indian Journal of Private Psychiatry. 2018; 12(1): 34 | | [Pubmed] | [DOI] | | | 40 |
Factors influencing Internet addiction |
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| Nikolina Banjanin, Nikola Banjanin | | Medicinska istrazivanja. 2018; 52(3): 29 | | [Pubmed] | [DOI] | | | 41 |
Study of internet addiction: Prevalence, pattern, and psychopathology among health professional undergraduates |
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| SachinR Gedam,Santanu Ghosh,Lipsy Modi,Arvind Goyal,Himanshu Mansharamani | | Indian Journal of Social Psychiatry. 2017; 33(4): 305 | | [Pubmed] | [DOI] | | | 42 |
Vietnamese validation of the short version of Internet Addiction Test |
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| Bach Xuan Tran,Hue Thi Mai,Long Hoang Nguyen,Cuong Tat Nguyen,Carl A. Latkin,Melvyn W.B. Zhang,Roger C.M. Ho | | Addictive Behaviors Reports. 2017; 6: 45 | | [Pubmed] | [DOI] | | | 43 |
Comparison of internet addiction, pattern and psychopathology between medical and dental students |
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| Sachin Ratan Gedam,Imran Ali Shivji,Arvind Goyal,Lipsy Modi,Santanu Ghosh | | Asian Journal of Psychiatry. 2016; 22: 105 | | [Pubmed] | [DOI] | | | 44 |
Fear of missing out (FOMO) is associated with activation of the right middle temporal gyrus during inclusion social cue |
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| Carlo Lai,Daniela Altavilla,Ambra Ronconi,Paola Aceto | | Computers in Human Behavior. 2016; 61: 516 | | [Pubmed] | [DOI] | | | 45 |
Internet use among anaesthesiologists: A cross-sectional survey |
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| Sriganesh Kamath, JasonW Busse, Sudhir Venkataramaiah, Chandrayan Rachana | | Indian Journal of Anaesthesia. 2016; 60(10): 771 | | [Pubmed] | [DOI] | | | 46 |
Functional magnetic resonance imaging of internet addiction in young adults |
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| Gianna Sepede,Margherita Tavino,Rita Santacroce,Federica Fiori,Rosa Maria Salerno,Massimo Di Giannantonio | | World Journal of Radiology. 2016; 8(2): 210 | | [Pubmed] | [DOI] | |
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