| Article Access Statistics|
| Viewed||150 |
| Printed||3 |
| Emailed||0 |
| PDF Downloaded||40 |
| Comments ||[Add] |
Click on image for details.
|Year : 2019
: 61 | Issue : 6 | Page
|Prevalence and pattern of problematic internet use among engineering students from different colleges in India
Saurabh Kumar1, Swarndeep Singh1, Krishnamurti Singh2, Srinivas Rajkumar1, Yatan Pal Singh Balhara3
1 Department of Psychiatry, National Drug Dependence Treatment Centre, All India Institute of Medical Sciences, New Delhi, India
2 Centre for Social Responsibility and Leadership, New Delhi, India
3 Department of Psychiatry, National Drug Dependence Treatment Centre, Behavioral Addictions Clinic, All India Institute of Medical Sciences, New Delhi, India
Click here for correspondence address and
|Date of Web Publication||5-Nov-2019|
| Abstract|| |
Introduction: The college students are prone to use internet in a manner that could negatively affect several aspects of their life. The present study is one of the largest studies to be undertaken in India, aimed at understanding the existing pattern of internet use and estimating the prevalence of problematic internet use (PIU) among college students.
Materials and Methods: The Generalized Problematic Internet Use Scale 2 (GPIUS-2) was used to assess the PIU. Multiple linear regression analysis was conducted to ascertain the relationship between GPIUS-2 total score and demographic and internet use-related variables.
Results: Of 3973 respondents from 23 engineering colleges located in the different parts of the country, about one-fourth (25.4%) had GPIUS-2 scores suggestive of PIU. Among the variables studied, older age, greater time spent online per day, and use of internet mainly for social networking were associated with greater GPIUS-2 scores, indicating higher risk for PIU. Students who used internet mainly for academic activities and during evening hours of the day were less likely to have PIU.
Conclusion: This study suggests PIU among engineering college students in India is an important public health concern. There is a need to create awareness among students, emerging adults, parents, and concerned authorities about the harms associated with PIU. Furthermore, there is a need to implement preventive strategies for inculcating pattern of safe and healthy internet usage among them. In addition, there is a need to develop public health policies for prevention and treatment of PIU and conduct further research to enhance our understanding about the same.
Keywords: Adolescents, behavioral addiction, college students, emerging adults, internet addiction, problematic internet use
|How to cite this article:|
Kumar S, Singh S, Singh K, Rajkumar S, Balhara YP. Prevalence and pattern of problematic internet use among engineering students from different colleges in India. Indian J Psychiatry 2019;61:578-83
|How to cite this URL:|
Kumar S, Singh S, Singh K, Rajkumar S, Balhara YP. Prevalence and pattern of problematic internet use among engineering students from different colleges in India. Indian J Psychiatry [serial online] 2019 [cited 2019 Nov 22];61:578-83. Available from: http://www.indianjpsychiatry.org/text.asp?2019/61/6/578/270356
| Introduction|| |
There has been an exponential rise in the number of internet users in India. The report “Internet in India 2017” had estimated that of about 500 million internet users in India in the year 2018, 60% shall be students and youth. The total number of internet users in the country is projected to grow to 666.4 million by the year 2023. While internet provides a world of opportunities, there has been a substantial amount of evidence highlighting the several negative consequences associated with misuse and/or excessive use of internet.
A recent meta-analysis comprising of nine Indian studies assessing the prevalence of internet addiction among adolescents reported a pooled prevalence of 22%, with a wide range of prevalence between 0.2% and 66% across the studies. This review further pointed out that most of the available studies were conducted among small study samples with several important methodological limitations such as use of inconsistent criteria to classify problematic internet use (PIU) and recruitment of study participants following methods prone to serious sampling bias. This highlights an urgent need to carry out a large-scale epidemiological study addressing the limitations of existing studies, assessing the extent and pattern of PIU among college students in India.
Another recent review of the published research from South East Asia has reported that the prevalence of severe PIU/internet addiction among students from South East Asia region has ranged from 0% to 47.4% whereas the prevalence of internet overuse/possible internet addiction ranged from 7.4% to 46.4%. Physical impairments in the form of insomnia (26.8%), day time sleepiness (20%), and eye strain (19%) were also reported among problem users.
As mentioned earlier, internet use has become widespread in India particularly among the youth, and it is pertinent to study the extent and pattern of internet usage in adolescent students pursuing professional courses with an easy access to internet. There is also a need to focus on emerging adults who are students to promote safe and healthy internet use behaviors across the life span. Further, college students form a particularly vulnerable group to develop PIU due to several psychological and environmental factors associated with college life.
The present study was aimed at understanding the existing pattern of internet use and estimating the prevalence of PIU among the engineering students from different colleges located in the northern, eastern, and central parts of India. Further, the relationship between internet usage patterns and PIU was also explored.
| Materials and Methods|| |
Study settings and participants
The study participants were 1st year engineering students from different engineering colleges located in eastern, northern, and central part of India. The colleges were identified in consultation with a not for profit, civil society organization (Centre for Social Responsibility and Leadership). The organization has been actively involved in providing education to the underprivileged section of the society through its various SUPER 30 projects being executed across India. The colleges, once identified, were approached formally using a predrafted letter for participation in the survey. Those who provided permission to participate were included in the survey. All the eligible students were approached, and those who were willing to give informed consent were included for participation in the study. The students were approached in lecture theaters, immediately after the completion of a lecture. Anonymous and self-completed questionnaires were distributed to all of the study participants in classroom setting and collected onsite after a duration of about 30 min. The teachers left the classrooms during this period to avoid any bias, influence, or hesitancy. Thirty colleges were invited to participate in the initial phase of this study. A total of 4121 1st year engineering graduate students from 23 colleges participated in the present study. Of these, 148 students (3.59%) did not provide answers to the questions assessing either their demographic profile, internet use pattern or items of Generalised Problematic Internet Use Scale-2 (GPIUS2) and were excluded from the analysis. Thus, the final study sample comprised of 3973 students.
The study questionnaire consisted of three parts: a semi-structured questionnaire to assess demographic information (age and gender); a semi-structured questionnaire to assess internet use pattern (number of hours spent online in a typical day, the devices used for accessing internet, usual purpose of using internet, and the predominant time of day for internet use over the past 1 month); and Generalised Problematic Internet Use Scale-2 (GPISU2). The Generalized Problematic Internet Use Scale 2 (GPIUS2) is a 15-item questionnaire with excellent internal consistency (α =0.91) and was used to assess the PIU. Moreover, the 15 items of this scale could be combined to form five different subscales comprising of three items each: preference for online social interaction (POSI) (α = 0.830), mood regulation (MR) (α = 0.854), cognitive preoccupation (CP) (α = 0.726), compulsive internet use (CIU) (α = 0.877), and negative outcomes (NO) (α = 0.872). The participants were asked to rate all the 15 items on a Likert scale ranging from a score of one (strongly disagree) to seven (strongly agree). Thus, the possible total and subscale score ranges between 15–105 and 3–21, respectively, with higher scores associated with greater severity of PIU. Since answering neutrally to each item would yield a total score of 60, a total score of higher than 60 was considered as suggestive of PIU. Similarly, a subscale of higher than 12 was indicative of significant problem in the area represented by that particular subscale.
Statistical analysis was done using SPSS version 23.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics using mean, standard deviation (SD), frequency, and percentage were used to describe the sample characteristics, internet use patterns, and GPIUS scores. Pearson's correlation was used to determine the level of agreement between the five subscale and total GPIUS score. Bivariate analysis using Pearson's correlation and independent t-test was conducted to examine the associations between continuous and categorical variables, respectively, with the total GPIUS score. Multiple linear regression analysis was performed to determine if variables with significant bivariate relationship could be used to independently predict the GPIUS score. The level of statistical significance was set at P < 0.05 for all the tests. Missing value imputation was not conducted.
| Results|| |
The final study sample included for the statistical analysis comprised of 3973 students. Three-fourth of respondents were male (n = 2998, 75.5%), and the average age was 18.18 years (Range: 18–21; SD: 0.41). The detailed description of study sample is given in [Table 1].
The mean total and subscale scores obtained on GPIUS2 and correlation between them is described in [Table 2]. There was significant positive correlation between various subscale and total scores obtained on GPIUS2 scale (r = 0.69–0.79; P < 0.01**). Of a total of 3973 students, 1008 students (25.4%) scored higher than 60 on GPIUS2 indicating PIU. Further, the proportion of students scoring above the cutoff threshold score of 12 on POSI, internet use for MR, CP by internet use, CIU behavior, and NO due to internet use subscales of GPIIUS2 are described in [Figure 1].
|Table 2: Correlations between total and different subscale scores obtained on global problematic internet use scale-2 total score|
Click here to view
|Figure 1: The proportion of participants scoring above the cutoff threshold scores on GPIUS2; GPIUS2 – Generalized Problematic Internet Use Scale 2; GPIUS2t – Generalised Problematic Internet Use Scale-2 total score; POSI – Preference for online social interaction; MR – Mood regulation; CP – Cognitive preoccupation; CIU – Compulsive internet use; NO – Negative outcomes|
Click here to view
The bivariate analysis assessed the relationship of different demographic and internet use variables with the GPIUS2 total score. The age of participants (r = 0.34, P < 0.01**), average time spent online per day (r = 0.58, P < 0.01**), and cost of internet use per month (r = 0.09, P < 0.01**) were positively correlated with the GPIUS2 total score. The male gender (male: 52.21 ± 14.69 and female: 50.83 ± 15.13; t = 2.53, P = 0.01*), use of internet mainly for gaming (yes: 54.98 ± 16.41 and no: 51.32 ± 14.44; t = 5.08, P < 0.01**), social networking (yes: 54.28 ± 14.88 and no: 47.55 ± 13.66; t = 14.41, P < 0.01**) purposes; use of mobile (yes: 52.15 ± 14.42 and no: 50.98 ± 15.94; t = 2.03, P = 0.04*) for internet use, and internet use predominantly during morning (yes: 55.71 ± 18.23 and no: 51.64 ± 14.55; t = 3.25, P < 0.01**) and night (yes: 54.19 ± 14.88 and no: 48.68 ± 14.12; t = 11.77, P < 0.01**) hours were associated with significantly greater GPISU2 total scores. Whereas, the use of internet mainly for academic (yes: 49.62 ± 13.80 and no: 53.72 ± 15.35; t = −8.86, P < 0.01**) purpose and internet use predominantly during afternoon (yes: 50.30 ± 14.97 and no: 52.01 ± 14.79; t = -2.01, P = 0.04*) and evening (yes: 50.26 ± 14.88 and no: 52.89 ± 14.68; t = −5.45, P < 0.01**) hours were associated with significantly lesser GPISU2 total scores.
Multiple linear regression analysis was conducted with all the variables showing significant relationship with GPIUS2 total score in bivariate analysis, entered as independent variables (predictors) and the GPIUS2 total score as the dependent variable (outcome variable). The model [Table 3] was statistically significant (F = 236.14; P < 0.01**) and explained 41.7% of the variance in GPIUS2 total scores. It was seen that age of participant (β = 0.234, P < 0.01**), use of internet mainly for social networking (β = 0.072, P < 0.01**), and average time spent online per day (β = 0.517, P < 0.01**) were significant positive predictors of PIU (GPIUS2 total score). Further, use of internet mainly for academic purposes (β = -0.047, P < 0.01**) and internet use predominantly during evening hours (β = 0.052, P = 0.006**) were significant negative predictors of PIU (GPIUS2 total score).
|Table 3: Multiple linear regression equation for predictors of global problematic internet use scale-2 total score (n=3973)|
Click here to view
| Discussion|| |
This study describes the extent and pattern of PIU among the engineering students from colleges located in the northern, eastern, and central parts of India. The students on average reported spending 3.47 h/day online, with about two-third of them (64%) using internet mainly for social networking, and 45.2% students also using internet for academic activities. This supports the findings of internet use among students as a double-edged sword, with positive effects when used as a medium for acquiring knowledge, and possible negative effects when used excessively for other purposes such as social networking, gaming, or pornography. The main device used for accessing internet was mobile for more than three-fourth of students (76%), followed by laptops in about 26.6% of them. This is in line with findings of other studies, which suggest the recent increase in internet penetration among youth in India is due to the rapid expansion of mobile broadband network coverage, smartphone applications, and cheaper internet prices.,
A key finding of the present study is that about one-fourth (25.4%) of the study participants were found to have GPIUS-2 scores suggestive of generalized PIU. This is line with the findings of a recent systematic review of epidemiological research on PIU among adolescents worldwide, which reported the prevalence rates ranging between 0.8% in Italian high school students to 26.7% among adolescent students in Hong Kong. The review also highlighted that PIU or internet addiction is a poorly defined construct, with no consensus or gold standard instrument available for its measurement. This was reflected by the use of 21 assessment instruments in 68 studies included in the review. Further, the dissimilarity in assessment across studies in terms of the instrument, cutoff scores, and methodology used for sample selection and data collection precludes any meaningful cross-comparisons between them. A study assessed the prevalence of internet addiction among the same study sample using two different assessment instruments based on different constructs of internet addiction and reported a marked difference in the two prevalence estimates (3.8% vs. 51.9%). The researchers have viewed internet addiction from different theoretical perspectives such as impulse control disorder, obsessive compulsive disorder, and substance use disorder.
The conceptualization of PIU solely on an addiction or dependence paradigm might lead to narrowing of its scope and result in premature conclusions about individuals suffering with this new entity. The commonly used assessment instruments such as the Internet Addiction Test (IAT) and the Internet Addiction Diagnostic Questionnaire (IADQ) focus mainly on the addiction perspective and overlook other important aspects associated with the PIU. Whereas, the cognitive-behavioral perspective describes PIU as a multidimensional construct consisting of cognitive, emotional, and behavioral group of symptoms, which provides a more detailed information regarding the key causal processes involved in the etiology, development, and NO associated with the PIU. Thus, the GPIUS-2 based on the cognitive-behavioral perspective was used for the assessment of PIU in the present study instead of the IAT or IADQ. It offers unique insights about the distinct constructs associated with PIU such as POSI vis-à-vis real social interaction, internet use for MR, CP with internet, CIU, and NO associated with excessive internet use.
The prevalence of PIU found in this study was higher than the 19% reported in another recent study conducted among 4,600 schoolgoing students using the GPIUS2 scale and similar assessment methodology as in the present study. This might be because of the differences between the characteristics of the study population in these two studies. The present study sample had a higher mean age of participants (18.18 years vs. 14.12 years) and a greater proportion of male participants (75.7% vs. 66.7%), both of which have been associated with a higher prevalence of PIU. Further, the collegegoing students assessed in the present study are less likely to receive parental guidance and supervision with regard to internet use than the schoolgoing students, which has been reported to be associated with an increased risk of PIU. Interestingly, the prevalence of PIU was higher than that observed among a sample of 6,291 school students from India (25% vs. 19%). Furthermore, a lesser proportion of the school students reported internet use for MR (37% vs. 54%).
Another important finding of the present study was that a large number of study participants reported a significant (GPIUS2 subscale score of above 12) POSI (29.1%) and use of internet for MR (54.4%). This is particularly worrisome as both internet use for MR and POSI have been shown to be associated with internet addiction among adolescents.
The present study also showed that older age, greater time spent online per day, and use of internet mainly for social networking were associated with greater scores on GPIUS-2. Whereas, use of internet mainly for academic activities and during evening hours of the day were associated with lesser scores on GPIUS-2. This is also supported by the findings of another study done among Indian college students as well as school students, which reported using of internet less for academic activities such as coursework/assignments and more for social networking such as making new friends online or getting into relationships online were risk factors associated with internet addiction.,
Strengths and limitations
The main strengths of this study are its large sample size and offline survey approach used for collection of data. To the best of our knowledge, this is the largest epidemiological study published till date estimating the prevalence of PIU among adolescents in India. The students were recruited via offline classroom surveys form 23 different engineering colleges located in the northern, eastern, and central parts of India to avoid any sampling bias, which had been a major limitation of several previous studies. This study did not recruit participants through E-mail or other online platforms designed for internet or other addicts, thereby limiting itself to a sample of participants who would be having some interest or psychological investment in the research topic and would more likely to participate in the study leading to a biased sample. Further, the study questionnaires were filled anonymously by the participants, and teachers were kept away from the classrooms during the period of data collection. This was done to encourage the participants to provide more factual and truthful response without the fear of later consequences.
However, this study also has certain limitations which should be kept in mind while interpreting its results. The cross-sectional study design precludes one from assessing temporal relationships and establishing causal associations between various variables. The information was collected through self-reported questionnaires, and PIU was not confirmed by use of any other means such as a clinical interview by a trained person. Thus, there is a possibility of misreporting of actual prevalence rates. Further, this study did not differentiate between essential and nonessential internet use during assessment of PIU.
The analysis of the extent and pattern of PIU among college student showed that about one-fourth of them have self-reported generalized PIU. This study strongly suggests PIU among collegegoing adolescents in India as an important public health concern, keeping in mind the substantial amount of existing evidence regarding negative consequences of excessive internet use on the physical, mental, and social well-being of individuals. Further, it suggests that PIU among college students might be associated with faulty cognitions such as 'internet being a safe way of presenting oneself to others' (preference for online social interaction), or a coping behavior for alleviating low or dysphoric mood (internet use for mood regulation). Thus, a carefully designed intervention targeting these maladaptive beliefs and behaviors might be an effective treatment strategy for these individuals with PIU. There is a need to scale up services for PIU in the country by establishing Behavioral Addictions Clinic. Moreover, this study adds to the limited existing literature on risk factors associated with PIU in Indian context. The use of internet more for social networking activities and less for academic purposes was associated with PIU. This information may be helpful in screening of individuals who are at greater risk for developing PIU and also in designing various preventive strategies for emerging adults. There is a need to create awareness among students, emerging adults parents, and concerned authorities about the harms associated with PIU. Furthermore, there is a need to implement preventive strategies for inculcating pattern of safe and healthy internet usage among them. In addition, there is a need to plan public health policies for prevention and treatment of PIU and conduct further research to enhance our understanding about the same.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Internet and Mobile Association of India. Internet Users Projected to Cross 500 Million by June 2018. New Delhi: Internet and Mobile Association of India; 2018. Available from: http://www.iamai.in/media/details/4990
. [Last accessed on 2018 Dec 24].
Balhara YP, Dahiya N, Varshney M, Garg S, Bhargava R. Awareness, self-assessment and help seeking behavior for behavioral addictions related to use of mobile technology among attendees of a health camp. J Assoc Physicians India 2018;66:45-8.
Sanjeev D, Davey A, Singh J. Emergence of problematic internet use among Indian adolescents: A multi method study. Child Adolesc Ment Health 2016;12:60-78.
Balhara YP, 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.
] [Full text]
Sharma A, Sahu R, Kasar PK, Sharma R. Internet addiction among professional courses students: A study from central India. Int J Med Sci Public Health 2014;3:1069-73.
Caplan S. Theory and measurement of generalized problematic Internet use: A two-step approach. Comput Hum Behav 2010;26:1089-97.
Guan SS, Subrahmanyam K. Youth internet use: Risks and opportunities. Curr Opin Psychiatry 2009;22:351-6.
Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: A systematic review of epidemiological research for the last decade. Curr Pharm Des 2014;20:4026-52.
Chakraborty K, Basu D, Vijaya Kumar KG. Internet addiction: Consensus, controversies, and the way ahead. East Asian Arch Psychiatry 2010;20:123-32.
Aboujaoude E. Problematic internet use: An overview. World Psychiatry 2010;9:85-90.
Davis RA. A cognitive-behavioral model of pathological internet use. Comput Hum Behav 2001;17:187-95.
Balhara YP, Harshwardhan M, Kumar R, Singh S. Extent and pattern of problematic internet use among school students from Delhi: Findings from the cyber awareness programme. Asian J Psychiatr 2018;34:38-42.
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.
Gámez-Guadix M, Villa-George FI, Calvete E. Measurement and analysis of the cognitive-behavioral model of generalized problematic internet use among Mexican adolescents. J Adolesc 2012;35:1581-91.
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.
] [Full text]
Balhara YP, Bhargava R, Chadda R. Service development for behavioural addictions: AIIMS experience. Ann Natl Acad Med Sci 2018;53:130-8.
Dr. Yatan Pal Singh Balhara
Behavioral Addictions Clinic (BAC), Department of Psychiatry, National Drug Dependence Treatment Centre, All India Institute of Medical Sciences, New Delhi
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2], [Table 3]