Introduction
In January 2021, there were 4.66 billion web clients in the entire world, of which 4.32 billion got to the web through cell phones.1 A world without the Internet is unthinkable. In 2020, Asia was the region with the most significant number of online clients - over 2.5 billion. China, India, and the United States rank most raised with regard to internet users. India has around 560 million online users.1 There has been an outrageous improvement in the usage of the Internet in India also worldwide in the last decade.1
The Internet is another innovation that is a significant piece of regular day-to-day existence all around the world, and it provides effective and quick data.2 Its utilization is, for the most part, in youngsters.3 The Internet offers entertainment, shopping, and social sharing applications that make getting information easier, faster, and more convenient.4 Although this tool is advantageous, psychologists and educators have been made aware of its adverse effects, particularly the abuse that leads to physical and mental problems.5
Symptoms of Internet addiction include overactive or poorly controlled preoccupations and distracting behaviors related to PC use or access to the Internet that impede or hinder productivity.6 The term addiction has, for the most part, been related to substance use. DSM IV codes contain the expression “very strong need or compulsion towards taking a substance” for addiction.7 The concept of internet addiction was first coined by Goldberg (1996), and by following DSM IV addiction criteria, it was defined as a “very strong desire or urge for using the internet”.5
The problem of internet access has become widespread, and there are noticeable differences between users who use the Internet regularly and those who are addicted.8 In daily life, people use the Internet according to their requirements. Most normal side effects of abuse of the Internet is internet addiction with web habit like sleepiness,9 hostility, depression10 loneliness11 and some educational harms like wasting of time,12 decrease in academic performance,13 loss of career opportunities,14 poor dietary ways of behaving,15 communication issues with family and friends.16 Internet access has become a widespread problem, and it was noticeable.
Globally, the coronavirus disease 2019 (COVID-19) pandemic has altogether upset typical exercises of everyday life.17 Since individuals overall remained at home, proceeding to keep physical distance, and restrictions to limit movement in order to prevent the spread of COVID-19. May escalate the utilization of digital entertainment. Practically 90% of understudies are actually cut off from their schools because of the COVID-19 pandemic, and technology has become fundamental for students to access educational materials, to interact with each other.18 Habit-forming ways of behaving could emerge as expected issues during lockdowns, and consequently, other behavioral addictions emerge, influencing the adolescent population.19 This age group is more vulnerable to using the Internet and thus effectively develops Internet addiction behavior.20 Hence, it is critical to know the COVID-19 pandemic impact on internet use of the internet among this age group. We did this study with the objective to determine the prevalence of internet addiction and to measure its association with various factors among the school-going adolescents of South Delhi.
Materials and Methods
Study design and population
This was a school-based cross-sectional study among school-going adolescents of class 8th to 12th belonging to school from South Delhi done during the month of May - August 2022. We included both gender, public and private schools, adolescents with a history of using the internet from past one month or more. Those who were absent on the day of data collection were excluded.
Sampling: Sample size of the study was calculated by using the Schwartz formula, anticipating the prevalence as 35.6% from the neighboring district21 and taking relative error as 15%. Considering a design effect of 1.5 and 10% non-response, our final sample size was calculated as 509. The list of all the schools of South Delhi was prepared and randomly ten schools were selected and approached for permission. Data collection was done among those schools who gave permission. Number of students to be selected from each school was determined by probability-proportional-to-size. The selection of students from a particular school was done by systematic random sampling in each class who had been using the internet for at least 30 days.
Study instruments
The study instrument was a questionnaire with two parts: (1) Socio-demographic profile and pattern of internet use; and (2) Young’s Internet Addiction Test (IAT). The study classified students' families into socioeconomic classes according to the BG Prasad.22 Young’s IAT, was a 20-item 5-point Likert scale, with scores ranging from 0-100. The psychometric property of the IAT was established by a six-factor model consisting of Salience, Excess use, neglecting work, Anticipation, Lack of self-control and neglecting social life. We used 50% cut-off criteria of score for classifying internet addiction as used by the majority of the studies.21, 23 The IAT showed a very good internal consistency in a study conducted in India with Cronbach’s alpha = 0.93(23). The reliability for the six subscales was found to be adequate, Cronbach’s alpha = 0.54 to 0.93 and validity of all six factors significantly correlated with each other.
Ethical consideration
The study was approved by the Institution's ethics committee, HIMSR, New Delhi. Appropriate permission and written consent/assent were taken from school authorities/parents of the students. To emphasize the importance of the research, the investigator explained the purpose of the study before enrollment. They were informed that the confidentiality of the survey would be maintained. Health education was given to all the participants. In case of doubt, they were clarified and made to complete the questionnaire. Participants diagnosed with internet addiction after the screening were motivated to visit concern centers/physicians for psychosocial therapy.
Statistical analysis
Data entry and analysis was done using SPSS software version 26. Descriptive statistics were used to describe the data using frequencies and percentages for categorical variables and mean values with standard deviations for continuous variables. Chi-square or fissure test was used for analyzing categorical variables, while unpaired t test for continuous variable. Association between internet addiction and various factors of the study participants was calculated at a significance level 0.05 and at a confidence interval of 95%.
Result
A total of 509 students were enrolled who had used/been using the internet for at least 30 days. The mean age of the students was 15.8±1.4 years. As shown in Table 1, the students were distributed similarly across gender and class. Most of the parents of the students were educated till high school, while about a quarter were graduates. Majority of the students were belonging to Upper socio-economic class.
Table 1
Distribution of different socio demographic variable
As shown in Figure 1, 51.3% were found to be internet addicted, with a mean Internet addiction (IA) score came out to be 48.4+13.2. As shown in Table 2, IA is present more in the 11-14-year-old age group (58.8%) and lowest in 17-19 years of age. IA is present more in males (56.7%) as compared to females (47.5%). Students in the 10th class (59.4%) have the highest IA, whereas students in the 11th class (33.3%) have the least IA. Among these sociodemographic variables, Class groups were seen to be significantly associated with IA (p-value <0.001). Among socioeconomic status, IA is present more in the upper middle class (60.8%), whereas lowest in the lower class. Students were found to be more internet addicted whose mother’s education had primary education (66.7%) compared to students whose mothers were illiterate as well as; those students whose fathers had primary education (67.7%) tended to be more internet addicted, while those whose father was illiterate were less addicted (37.5%). Among these variables, socioeconomic status, mother’s education, and father’s education were seen to be significantly associated with internet addiction (p-value= .036, .015, .018), respectively, IA was present in students having computers at home (55.6%), internet access at home (52.9%), and students having personal devices (56.8%) in comparison to sharing devices (41.7%). However, IA was seen to be significantly associated with computers at home, internet access at home, and personal devices or shared devices (p-value =0.004,0.049,0.001), respectively.
Table 2
Association of internet addiction among sociodemographic variables and access
Table 3
Association of internet addiction with purpose of using the internet
As shown in Table 3, IA was present more in students who were using the internet once a month (70%), whereas lowest in students using the internet every day for education (49.6%). IA was present more in students who were using the internet every day (59.8%), whereas lowest in students using the internet once a month for movies (47.4%). IA was present more in students who were using the internet once a week (63.9%), whereas lowest in students using the internet once a month (48.7%) for shopping. IA was present more in students who were using the internet every day (58.4%), whereas lowest in students using the internet once a month (34%) for downloading media. IA was present more in students who were using the internet more than once a day/week (80.9%), whereas lowest in students using the internet once a month (53.3%) for an online game. IA was present more in students who were using the internet more than once a day/week (55.2%), whereas the lowest in students using the internet once a month (47.1%) for social networking. IA was present more in students who were using the internet more than once a day/week (55.6%), whereas the lowest in students using the internet every day (45.7%) for online news. IA was present more in students who were using the internet once a month (60%), whereas lowest in students using the internet once a week (40.8%) for online songs. IA was present more in students who were using the internet more than once a day/week (68.6%), whereas lowest in students using the internet once a week (42.9%) for chatting. IA was present more in students who were using the internet once a week (66.7%), whereas the lowest in students using the internet every day (62.5%) for Cybersex/pornography. However, IA was seen to be significantly associated with internet use by students for movies, online shopping, downloading media, online game, chatting, and Cyber-sex/pornography.
Discussion
We did the study to find the prevalence of internet addiction among school-going adolescents in South Delhi and found a prevalence of 51.3%. This is higher than most of the studies done in similar settings. In the Indian setting, it was found to be 0.3% in Jabalpur, 3% in Bhavnagar,24 8.7% in Vadodara25 to 35.6% (Arvind Sharma et al.; 35.6% in Aligarh, UP.21 Internationally, varied prevalence has been reported from China (0.2%), Nepal (13.3%), and Italy (36.7%).26, 27, 28 The higher prevalence of IA in the present study may be a result of the covid-19, which has provided students with increased access to the Internet and led to addictive behaviors, as highlighted in previous study.29 Furthermore, a multicentric study document 67.6% of COVID-19 diagnosed patients had internet addiction.28 The discrepancy in prevalence rates across these studies could be attributed to different criteria for classifying internet addiction, apart from different settings and COVID-19 lockdowns. Although a higher prevalence of IA has been reported in Maharashtra,30 this study was done among (Medical students) who have limited restrictions and easy internet access as compared to school-going adolescents.
Our study revealed that younger age and males tend to be addicted more to the Internet, although this relationship was not statistically significant. These findings align with previous studies,31, 32 while few studies conducted in Asian and European countries reported significant associations of internet addiction with age and male.3, 23, 33, 34, 35, 36, 37 Students belonging to lower academic classes (high school) in comparison to a higher academic class (senior secondary) were more addicted. This was similar to a study from Nepal,38 but inverse to findings from Taiwan.39 A significant association was seen between the participant’s parent’s education and internet addiction prevalence, with those whose parents were illiterate significantly less addicted. However, it is contrary to other studies.37 With regard to socio-economic status, the students from upper and upper-middle socioeconomic classes were addicted to the internet more, which is similar to other studies.38
This may be due to better access of Internet and internet-enabled devices to adolescents from the upper socioeconomic class. Additionally, computers at home, internet-enabled personal devices and internet connection at home were also found to be statistically significant, consistent with previous studies.33 These findings highlight the complex interplay between socio-demographic factors and internet addiction, suggesting the need for tailored interventions and further investigations into the underlying mechanisms that contribute to these associations.
Regarding the purpose of internet usage, the internet was primarily used by students for movies, online shopping, downloading media, online game, chatting, and Cyber-sex/pornography, which was found to be significantly associated with internet addiction. However, students who used the internet regularly for academic purposes were less prone to addiction. These findings of the purpose of using the internet are similar to previous studies.4, 8, 39, 40, 41, 42, 43, 44 The findings of our study should be interpreted considering some limitations. The finding may not be generalized to all adolescents as it was collected only from selected schools going students that granted permissions. Although we tried to reduce the selection bias by employing systematic random sampling and ensuring the population proportionate to size. Additionally, our assessment of addiction was limited to those who had used the internet in the last month, and may have yielded higher prevalence, since those who have never used were excluded, although this proportion was very small in our study. Young’s IAT is a self-reported tool, assessing IA in the past month may be susceptible to social desirability and recall bias. However, Young’s IAT has been widely used and reported as valid and accurate. We further collected the data anonymously, which may reduce the social desirability bias.
Conclusion
In conclusion, we observed a high prevalence of internet addiction among school going adolescents which was seen to increase in recent years, may be attributed COVID-19 pandemic. While more students are having improved access to the internet at homes, they are primarily using it for nonacademic purposes. To address this issue, targeted intervention including proper awareness regarding the harmful effect of regular use of smart devices and the internet may be introduced in the school curriculum. Given that a significant number of the students were using the internet at home, it is crucial for teachers and parents to collaborate in promoting safe internet practices for the benefit of students. Encouraging students to be involved in recreational activities, including painting, sports, dancing, and outdoor activities, rather than spending time on the internet or smart devices could be beneficial. While our study establishes certain associations, it is essential to conduct large-scale studies to gain a deeper understanding of the underlying risk factors and mechanisms that contribute to internet addiction. This will help in generating evidence-based intervention, and would help in mitigating the potential escalation of this bigger public health problem.
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