|ORIGINAL RESEARCH ARTICLE
|Year : 2020 | Volume
| Issue : 1 | Page : 12-15
Internet addiction and its psychosocial impact on urban adolescents of Kanpur, Uttar Pradesh
Rahul Srivastava1, Devina Pradhan2, Lokesh Sharma3, Bhuvan Jyoti4, Omveer Singh5
1 Department of Oral Medicine and Radiology, Rama Dental College Hospital and Research Centre, Kanpur, India
2 Department of Public Health Dentistry, Rama Dental College Hospital and Research Centre, Kanpur, India
3 Department of Public Health Dentistry, Sardar Patel Post Graduate Institute of Dental and Medical Sciences, Ranchi, Jharkhand, India
4 Department of Dental Surgery, Ranchi Institute of Neuro-Psychiatry and Allied Sciences, Ranchi, Jharkhand, India
5 Department of Public Health Dentistry, Career Dental College, Lucknow, Uttar Pradesh, India
|Date of Submission||05-Dec-2020|
|Date of Acceptance||17-Dec-2020|
|Date of Web Publication||31-Dec-2020|
Department of Oral Medicine and Radiology, Rama Dental College Hospital and Research Centre, A-1/8, Lakhanpur, Kanpur - 208 024, Uttar Pradesh
Source of Support: None, Conflict of Interest: None
Background: Internet addiction is defined as any online-related, compulsive behavior which interferes with normal living and causes severe stress on family, friends, loved ones, and one's work environment. The adolescents are most vulnerable to the numerous addictive temptations offered by the Internet during the transition phase. The present study aims to evaluate the prevalence of Internet addiction in adolescents of urban areas of Kanpur district and assess the association of Internet addiction with stress, anxiety, and depression. Materials and Methods: A cross-sectional study was conducted among students of higher secondary schools/colleges in the urban areas of Kanpur district in Uttar Pradesh. Out of 105 colleges, 10%, i.e., 10 colleges, were selected randomly and a total of 900 students were enrolled in the study. A pretested, predesigned questionnaire, Young's Internet Addiction Scale, and Depression Anxiety Stress Scales 21 were used in the study. Data distribution was assessed for normality using the Shapiro–Wilk test. Categorical data were compared using the Chi-square test. All values were considered statistically significant for a value of P ≤ 0.05. Results: Majority (61.1%) of the respondents were females, and the mean age was 17.20 years. The prevalence of Internet addiction was 89.78%. The main purpose of using Internet was social networking (54.89%), followed by online gaming/gambling (19.67%) and study (12.89%). About 60.44% of the respondents used Internet for 3–6 h/day and 28.67% of the respondents used Internet for <3 h/day. There was a significant association between Internet addiction and stress (odds ratio = 33.55), depression (odds ratio = 0.99), and anxiety (odds ratio = 5.25). Conclusion: Internet addiction is a much quieter problem, and as such, it may be more readily disregarded or not even recognized as a problem. As parents and caregivers, understanding how to differentiate between normal Internet use and compulsive use is critically important.
Keywords: Adolescents, anxiety, Internet addiction, stress
|How to cite this article:|
Srivastava R, Pradhan D, Sharma L, Jyoti B, Singh O. Internet addiction and its psychosocial impact on urban adolescents of Kanpur, Uttar Pradesh. J Prim Care Dent Oral Health 2020;1:12-5
|How to cite this URL:|
Srivastava R, Pradhan D, Sharma L, Jyoti B, Singh O. Internet addiction and its psychosocial impact on urban adolescents of Kanpur, Uttar Pradesh. J Prim Care Dent Oral Health [serial online] 2020 [cited 2021 Jan 20];1:12-5. Available from: http://www.jpcdoh.org/text.asp?2020/1/1/12/305894
| Introduction|| |
Internet addiction is defined as any online-related, compulsive behavior which interferes with normal living and causes severe stress on family, friends, loved ones, and one's work environment. Internet addiction was referred to as Internet dependency and Internet compulsiveness. Internet addiction is characterized by excessive or poorly controlled preoccupations, urges, or behaviors regarding computer use and Internet access that lead to impairment or distress.
Adolescence can be defined as the period between puberty and adulthood, usually between the ages of 11 and 18 years. Events during this time greatly influence the development of a person and can determine his attitudes and behaviors in later life.
Internet and mobile technology are becoming more and more relevant for adolescent's educational and social lives and becoming part of their identity.
With the introduction of broadband and mobile Internet connectivity giving young people access to the Internet everywhere and at any time, and thus entertainment, engagement, and communication 24/7, there is a real risk that teenagers may become so busy in their online environment that it seems to take over their lives.
Previous studies revealed that heavy use of the Internet contributes to social isolation, addiction, and development of psychological signs and symptoms.
The adolescents are most vulnerable to the numerous addictive temptations offered by the Internet during the transition phase. Adolescents appear to be more vulnerable to risky behavior and may engage in addictive behaviors to cope with anxiety, disappointment, and failure, or because of the need for excitement, false hope about the feeling of invulnerability, or even the desire to achieve their goals as part of their transition into adulthood.
The present study was done to evaluate the prevalence of Internet addiction in adolescents of urban areas of Kanpur district and assess the association of Internet addiction with stress, anxiety, and depression.
| Materials and Methods|| |
A cross-sectional study was conducted among students of higher secondary colleges/school in the urban areas of Kanpur district of Uttar Pradesh from July to November 2019. Approval from the Ethical Committee of the Institute was obtained. Students who consented to participate in the study and those who had been using the Internet for the past 6 months were included in the study. The purpose of the study was explained and informed consent was obtained from the participants. Students who did not completely fill the questionnaire and students who did not give written informed consent to participate were excluded from the study.
The sample size was calculated using G Power 188.8.131.52 software (Denmark, Europe). Taking effect size as 0.2, at α = 0.05, and power of 0.85, the total sample size for two groups was estimated to be 854. To accommodate dropouts, 10% were added to the estimated sample size, so the sample size was 940. There were 40 dropouts because the participants were absent on the days of the study. Therefore, the final sample size was 900.
Of the 105 colleges, 10%, i.e., 10 colleges, were selected randomly. Permission to undertake the study in these schools was obtained from the school authorities. From each selected college, a class was selected randomly. If there was more than one stream in that institution, one was selected randomly, and if there was more than one section, one section was also selected randomly. The lottery method was used for selecting 22 students from the attendance register from each selected class. If the randomly selected student did not meet the requirements for inclusion, then the next number on the roll was chosen.
For data collection, a pretested, predesigned, self-administered questionnaire was used. Young's Internet Addiction Scale has been used to measure Internet addiction, as this scale has been found to be more accurate for college students and possibly Asia. Depression Anxiety Stress Scales 21 was used as a reliable scale in assessment in various settings and in different population groups.,,,
The questionnaire was distributed by the investigator. The students were told to approach the investigator immediately in case of any doubts regarding any of the questions in the questionnaire. Data were analyzed using IBM SPSS Statistics version 21 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, version 21.0. Armonk, NY, USA: IBM Corp.) Descriptive statistics included calculation of percentages. Data distribution was assessed for normality using the Shapiro–Wilk test. Categorical data were compared using the Chi-square test. All values were considered statistically significant for a value of P ≤ 0.05.
| Results|| |
Nine hundred participants were enrolled in the study. [Table 1] shows that of 900 participants, 550 were female respondents (61.10%) and 350 (38.90%) respondents were male. The mean age of the respondents with Internet addiction was 17.2 years. [Table 2] shows that the overall prevalence of Internet addiction was 89.78%. Evaluation of the various grades of mild, moderate, and severe addiction showed that 18.33% of the respondents had mild, 32.55% had moderate, and 38.89% had severe addiction. Most of the respondents (92.55%) lived with their parents at home.
|Table 2: The characteristics of the study participants and prevalence of Internet addiction depending upon these characteristics|
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The main purpose of using the Internet was social networking (54.89%), followed by online gaming/gambling (19.67%) and study (12.89%).
About 60.44% of the respondents used Internet for 3–6 h/day and 28.67% of the respondents used Internet for <3 h/day [Table 4].
|Table 3: The association between Internet addiction and depression, stress and anxiety|
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|Table 4: The purpose of Internet use and time spent on Internet by gender|
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Respondents those having Internet addiction are found to be more depressed (odds ratio = 14, 95% confidence interval [CI] = 7.9–24.6), stressed (odds ratio = 12, 95% CI = 5.5–25.7), and anxious (odds ratio = 3.3, 95% CI = 1.9–5.6), as compared to those who are not having Internet addiction. There was a significant association between Internet addiction and depression, anxiety, and stress (P < 0.0001).
There was a high prevalence of stress (85.22%) and anxiety (69.55%) in the participants addicted to the Internet [Table 3].
| Discussion|| |
Throughout this research, we tried to find the prevalence of Internet addiction and its various correlates throughout adolescents in high schools/schools in the urban areas of Kanpur city, Uttar Pradesh. We also tried to find out the association of this addiction and depression, anxiety, and stress.
Upadhyay et al. conducted a study to assess the prevalence of Internet addiction and related behavioral problems in students of adolescent age group in an Indian A Grade city. They found that 74.5% of participants were potential addicts. Bhatia et al. conducted a study to assess the prevalence of Internet overuse among schoolgoing adolescent students, 90.66% Internet addiction was found among schoolgoing adolescent students. The results obtained from the present study showed that the overall prevalence of Internet addiction was 89.78%.
The results obtained from the present study showed that the average age of Internet addiction was 17.21 years which correlates with the average age of addiction revealed in the study conducted by Bernardi and Pallanti at 16.67 ± 1.85 of 16.67 ± 1.85 years and study conducted by Saikia et al. who reported the average age of Internet addiction was 17.20 years but somewhat different than the study conducted by Karacic and Oreskovic (14.9 years).
Shek and Yu conducted a study in high school adolescents of Hong Kong and found that Internet addictive behavior was consistently high for male gender. Ballarotto et al. conducted a study on adolescent internet abuse in a large community sample. On the basis of sex, they reported that girls had a higher score than boys. Saikia et al. also found higher levels of Internet addiction in females as compared to males. In the present study, there was female predominance to Internet addiction that was in contrast with other studies conducted by Mazalin and Moore. Various factors such as cultural norms, Internet connectivity, institutional policies, and personal preferences can be attributed to the varying findings about gender disparities and Internet addiction. Comparatively higher proportion of female respondents and possibly the relatively higher number of females attending classes may also be a contributor factor.
According to KFF, a full 21% of youth are defined as heavy media users who spend more than 16 h with media a day. Another 63% are defined as moderate users who use media 3–16 h a day. Youth who fall into the light user category are those who consume <3 h of media a day. In the current study, most of the respondents used the Internet 3–6 h/day (60.44%). A relatively fewer number of respondents used the Internet for >9 h/day (4.33%) which is somewhat different from the study conducted Bhatia et al. 2016, Mutalik et al. 2018, and by Sharma et al. 2014.,, This difference may be due to that the present study was conducted in a general college on students in the humanities, commerce, and science.
Digital networking has a strong impact on adolescent developmental stages. Adolescents express their experiences through new ways of communication; they pursue their own place within a community and refer to their peers as a great source of social support, far greater than their parents. According to the results of the present study, there was a statistically significant influence of the purpose of Internet use on the level of Internet addiction and influence of the age of adolescents on the level of Internet addiction. Most of the participants use the Internet for social networking than study, chatting, and online games/gambling.
Association of Internet addiction and psychological morbidities such as depression, stress, intention to commit suicide, violence, and antisocial behaviors were supported by many studies. McMillan and Morrison stated that excessive engagement in virtual social functions is linked to psychological disorders such as depression and addiction. These studies support our findings of a substantial association between depression, stress, anxiety, and addiction to the Internet.
Chang and Hung reported that addicts use the Internet as a way to escape and address underlying psychological problems. Even in the present study, it was not possible to determine if this behavior was the cause or the product of specific psychological morbidities. The drawbacks of the study include limited sample size due to methodological restrictions not using qualitative research techniques. Hence, there is a need for further studies with a larger sample size utilizing multiple institutional participants for the generalizability of the results.
| Conclusion|| |
Internet addiction is a much quieter problem, and as such, it may be more readily disregarded or not even recognized as a problem. As parents and caregivers, understanding how to differentiate between normal Internet use and compulsive use is critically important for knowing when to seek help for concerning behavior. Usage may spike because child has a big homework project to finish, they are setting up a social network, just started playing a new game, has a new boy/girlfriend to chat with, is missing a friend, or for some other short-term interest. While potentially time consuming and engrossing, this is very different behavior than that of youth who spend virtually all of their waking hours, week in and week out, behind an Internet connected screen, ignoring relationships, homework, and the world. The literature on Internet addiction prevention is sparse. There is an immediate need to develop and incorporate new strategies for different at-risk populations, perform well-designed research, and publish data on these interventions' effectiveness.
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Conflicts of interest
There are no conflicts of interest.
| References|| |
Net Addiction, the Center for internet addiction your Source Since 1995. Available from: file:///D:/Internet%20addiction/definition%20of%20internet.html. [Last accessed 2020 Nov 10].
Shaw M, Black DW. Internet addiction: Definition, assessment, epidemiology and clinical management Internet Addiction. CNS Drugs 2008;22:353-65.
Karacic S, Oreskovic S. Internet addiction through the phase of adolescence: A questionnaire study. JMIR Ment Health 2017;4:1-11.
Bhatia M, Rajpoot M, Dwivedi V. Pattern of internet addiction among adolescent school students of a North Indian city. Int J Community Med Public Health 2016;3:2459-63.
Passos JA, Pires AV, Scheidt L, de Almeida LA, Ferreira CF, Gubert C, et al
. Alcohol use in adolescence, impulsivity, and risk-taking behavior in Wistar rats. Psychol Neuroscience 2015;8:130-142.
Frangos CC, Frangos CC, Sotiropoulos I. A Meta-Analysis of the Reliability of Young's Internet Addiction Test. London, UK: World Congress on Engineering; 2012. p. 4-6.
Bener A, Alsulaiman R, Doodson LG, El Ayoubi HR. Comparison of reliability and validity of the breast cancer depression anxiety stress scales (DASS-21) with the beck depression inventory-(BDI-II) and Hospital Anxiety and Depression Scale (HADS). Int J Behav Res Psychol 2016;4:197-203.
Taylor R, Lovibond PF, Nicholas MK, Cayley C, Wilson PH. The utility of somatic items in the assessment of depression in patients with chronic pain: A comparison of the zung self-rating depression scale and the depression anxiety stress scales in chronic pain and clinical and community samples. Clin J Pain 2005;21:91-100.
Brown TA, Chorpita BF, Korotitsch W, Barlow DH. Psychometric properties of the depression anxiety stress scales (DASS) in clinical samples. Behav Res Ther 1997;35:79-89.
Edmed S, Sullivan K. Depression, anxiety, and stress as predictors of postconcussion-like symptoms in a non-clinical sample. Psychiatry Res 2012;200:41-5.
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.
Bernardi S, Pallanti S. Internet addiction: A descriptive clinical study focusing on comorbidities and dissociative symptoms. Compr Psychiatry 2009;50:510-6.
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.
Shek DT, Yu L. Adolescent internet addiction in Hong Kong: Prevalence, change, and correlates. J Pediatr Adolesc Gynecol 2016;29 (1 Suppl):S22-30.
Ballarotto G, Volpi B, Marzilli E, Tambelli R. Adolescent internet abuse: A study on the role of attachment to parents and peers in a large community sample. Bio Med Res Int 2018;2018: 5769250.
Mazalin D, Moore S. Internet use, identity development and social anxiety among young adults. Behav Chang 2004;21:90-102.
Mutalik N, Tejaswi T, Moni S, Choudhari S. A cross-sectional study on assessment of prevalence of internet addiction and its correlates among professional college students. Open J Psychiatry Allied Sci 2018;9:20.
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.
McMillan SJ, Morrison M. Coming of age with the internet: A qualitative exploration of how the internet has become an integral part of young people's lives. New Media and Society 2006;8:73-95.
Chang J, Hung C. Problematic internet use. In: Rey JM, editor. IACAPAP e-Textbook of Child and Adolescent Mental Health. Ch. 6. Geneva: International Association for Child and Adolescent Psychiatry and Allied Professions; 2012. p. 1-12.
[Table 1], [Table 2], [Table 3], [Table 4]