|Year : 2021 | Volume
| Issue : 3 | Page : 192-196
Internet addiction and its impact on mental health among dental students, Belagavi
Shubhechchha Bhattarai1, Mubashir Angolkar1, SS Chate2, Pooja S Dhagavkar1
1 Department of Public Health, J. N. Medical College, KLE Academy of Higher Education and Research, Belagavi, Karnataka, India
2 Department of Pshychiatry, J. N. Medical College, KLE Academy of Higher Education and Research, Belagavi, Karnataka, India
|Date of Submission||29-May-2021|
|Date of Acceptance||26-Sep-2021|
|Date of Web Publication||28-Dec-2021|
Dr. Pooja S Dhagavkar
Department of Public Health, J. N. Medical College, KLE Academy of Higher Education and Research, Nehru Nagar, Belagavi - 590 010, Karnataka
Source of Support: None, Conflict of Interest: None
Background: Internet dependence ordinarily alludes to a person's powerlessness to control their utilization of the Internet (counting any online-related, impulsive, or compulsive conduct), which finally leads to one's stamped trouble and practical debilitation in everyday life. The signs and indications of Internet Addiction Disorder can be seen in both physical and psychological (emotional) appearances. Few psychological symptoms include: Sadness, nervousness, disconnection, sensation of blame, mindset swings, depression, dread, and so forth. Objective: To assess the prevalence of Internet addiction and its impact on mental health among dental students of Belagavi city. Materials and Methods: A cross-sectional study was conducted, involving 168 dental students of age 17-25 years of Belagavi city. Students were selected using convenient sampling method and data were collected using pretested structured questionnaire. The ethical clearance was obtained from the institutional ethics committee JNMC Belagavi. Written consent was obtained from participants before collecting data which was analyzed in SPSS version 22. Results: The percentage of the normal use of Internet and potential addiction of Internet was 83.3% and 16.7%, respectively. Internet addiction was not associated with depression. But was significantly associated with anxiety, stress, and insomnia. Conclusion: The prevalence of potential Internet addiction among dental students is 16.7%. Hence, it becomes necessary to not only address the Internet addiction among dental students but also the mental issues related to it.
Keywords: Belagavi, dental students, internet addiction, mental health
|How to cite this article:|
Bhattarai S, Angolkar M, Chate S S, Dhagavkar PS. Internet addiction and its impact on mental health among dental students, Belagavi. J Sci Soc 2021;48:192-6
|How to cite this URL:|
Bhattarai S, Angolkar M, Chate S S, Dhagavkar PS. Internet addiction and its impact on mental health among dental students, Belagavi. J Sci Soc [serial online] 2021 [cited 2022 May 25];48:192-6. Available from: https://www.jscisociety.com/text.asp?2021/48/3/192/333852
| Introduction|| |
In the 21st century, Internet has become an integral part of an individual's life. Since the past 15 years, a rapid growth has been seen in the field of Internet use. At present time, roughly 40% of the total population is found to be on the web. Internet is a significant apparatus for schooling, amusement, correspondence, and data sharing.
A total of 3.9 billion Internet users were found worldwide in 2017. A total of more than 600 million Internet users are estimated to be recorded in the year 2021.
Internet dependence ordinarily alludes to a person's powerlessness to control their utilization of the Internet (counting any online related, impulsive, or compulsive conduct), which finally leads to one's stamped trouble and practical debilitation in everyday life.
Signs and indications of Internet Addiction Disorder can be seen in both physical and psychological (emotional) appearances. It has been discovered that depression, anxiety, stress, insomnia, and low self-esteem among students are straightforwardly affected by Internet addiction.
Dental students develop into health professionals; Internet addiction can hamper their career goals and can have wide impact on society as a whole. Very few studies have been carried out in India on Internet addiction and its impact on mental health among dental students. Hence, this study has been planned to assess the impact of Internet addiction on mental health among dental students of Belagavi city.
| Materials and Methods|| |
The ethical approval was obtained from an Institutional Ethics Committee. After obtaining permission from the respective college authority, participants were briefed about the study. Written consent was obtained from the concerned teachers and students too.
A cross-sectional study was conducted from January 2021 to April 2021 among dental students of Belagavi city.
Sampling and data collection
A convenient sampling method was used to select the participants from the dental college of Belagavi city. Undergraduate dental students who were using Internet for the past 1 year were included in the study. Participants who did not give informed consent and those who were under any psychological treatment were excluded from the study. The sample size for the study was calculated based on the 95% confidence interval which was 168. Pretested self-administered questionnaires were provided which included information on sociodemographic variables and Internet-related characteristics.
Tool 1: General information questionnaire representing baseline information of respondents.
Tool 2: The Young Internet Addiction Test (YIAT).
Internet Addiction Test (IAT) was developed by Dr. Kimberly Young. This 20-item tool is scored using five-point Likert's scale.
Tool 3: Insomnia Severity Index.
ISI, a seven-item tool is a self-report instrument that measures patient's perception of insomnia.
Tool 4: Depression Anxiety Stress Scale (DASS 21.)
DASS 21 is a 21-item self-report scale to measure depression, anxiety, and stress. The scores were calculated by summing scores for each item.
Data were entered in MS Excel. Analysis was performed using the SPSS version 22 (IBM, US). Rates and proportions were calculated for descriptive statistics. Chi-square test was used to assess the association between different variables. A P < 0.05 was considered statistically significant.
| Results|| |
The mean age of the total participants (n = 168) was 20 ± 1.567.
Around half of the participants (53%) fall in the age group of 20–22 years. Female participants (75.6%) covered most of the sample size. The majority of the participants (81.5%) were Hindu and 71.4% were from the nuclear family. More than half of the participants (54.8%) had monthly family income of Rs. 100,000–199,000. Most of the participants (70.2%) were lived in hostel/PG [Table 1].
|Table 1: Distribution of participants according to sociodemographic characteristics|
Click here to view
Around half of the participants spent 4–6 h/day on Internet. Less than half (45.8%) of them spent less than Rs. 300 monthly on Internet. The most common location of using Internet was the hostel which shares 61.9%. Around one-third of their siblings spent 4–8 h time daily on Internet [Table 2].
|Table 2: Distribution of participants according to Internet-related variables|
Click here to view
The most common purpose of using Internet was social networking (81.1%) followed by YouTube (41.5%). Only 13% of the participants use Internet for coursework whereas the rest of them use it for entertainment purposes only [Figure 1].
The univariate analysis showed that potential Internet addiction was significantly associated with age groups (P = 0.003), with higher prevalence in 23–25 years of age group (94.5%). Monthly family income was significantly associated with potential Internet addiction with P = 0.000 [Table 3].
|Table 3: Association between Internet addiction and sociodemographic variables|
Click here to view
Potential Internet addiction was found to be significantly associated with the common location of Internet access (P = 0.011), with high prevalence in libraries and classroom. Similarly, time spent by siblings on Internet outside work hours was significantly associated with potential Internet addiction with P = 0.036 [Table 4].
|Table 4: Association between Internet addiction and Internet-related characteristics|
Click here to view
The average YIAT score was 39.7 ± 13.069. The prevalence of potential Internet addiction was 16.7% and the percentage of normal Internet user was 83.3% [Table 5].
|Table 5: Distribution of study participants on the basis of young Internet addiction test categorization|
Click here to view
Internet addiction was significantly associated with anxiety (P = 0.001), stress (P = 0.017), and insomnia (P = 0.000) [Table 6].
|Table 6: Association between Internet addiction and depression, anxiety, stress, and insomnia|
Click here to view
| Discussion|| |
In the present study, the mean age of the participants was 20 ± 1.567 years ranging from 17 to 25 years. It might be because the sample populations were undergraduates. A similar study conducted in Nepal also revealed that the mean age of the participants was 21.01 ± 2.18 years. In this study, the numbers of female participants (75.6%) were more than the number of male participants (24.4%). A similar cross-sectional study also showed similar results with higher female participants (69.7%) than male participants (30.3%). In this study, the majority of the participants were Hindu (81.5%). Similar result was found in the study conducted in Nepal with higher percentage of Hindu, i.e., 92.2%.
In this study, social networking (49.8%) was the most common purpose of using Internet by participants. This explains that students are using Facebook, Instagram, and WhatsApp more often than other sites. A study conducted in Iran showed that chatting and playing games as the most common purpose of using the Internet. In this study, more than half (62.5%) of the participants used Internet for <2 h/day. A study conducted in Saudi Arabia found that more than 50% of them used Internet for an average of more than 4 h/day which is in contrast to our findings.
In this study, Internet addiction was significantly associated with the age of the participants with P = 0.003. This explains that Internet addiction increases with increase in age. In contrast to this study, a study conducted among Japanese students showed no association between Internet addiction and age (P = 0.82). In the present study, the result revealed no significant association between gender and Internet addiction (P = 0.574). Similar result was found in the meta-analysis study with P = 0.85. This indicates that both males and females are equally addicted to Internet.
In the present study, the prevalence of potential Internet addiction was found to be 16.7% out of 168 participants. The results revealed that there was no significant association of Internet addiction with depression (P = 0.289). However, Internet addiction was significantly associated with anxiety (P = 0.001), stress (P = 0.017), and insomnia (P = 0.000). Similar result was found in a cross-sectional study with the prevalence of potential Internet addiction 16.8%. Significant association was found between potential Internet addiction and depression, anxiety, stress, and insomnia (P ≤ 0.001). Thus, these results indicate that more addictive to Internet a student is, the more anxiety, stress, and insomnia he/she has.
Strength and limitations
To the best of our knowledge, very few studies have been carried out to assess the impact of Internet addiction on depression, anxiety, stress, and insomnia in India. However, self-reported questionnaires cannot rule out response bias.
| Conclusion|| |
The study concludes that the prevalence of normal Internet use and potential Internet addiction was 83.3% and 16.7%, respectively. Depression was not found to be associated with Internet addiction. However, it was significantly associated with anxiety, stress, and insomnia. Overuse of Internet has a direct or indirect impact on the mental health of dental students.
The authors are thankful to all the respondents who participated in this study.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Soundariya K, Deepika V. Prevalence of internet addiction among medical students. Biomed 2015;35:363-7.
Poli R. Internet addiction update: Diagnostic criteria, assessment and prevalence. Neuropsychiatry (London) 2017;07:4-8.
Taylor GM, Dalili MN, Semwal M, Civljak M, Sheikh A, Car J. Internet-based interventions for smoking cessation. Cochrane Database of Systematic Reviews. 2017(9).
Kovalchuk O, Masonkova M, Banakh S. The Dark Web Worldwide 2020: Anonymous vs Safety. In 2021 11th International Conference on Advanced Computer Information Technologies (ACIT) 2021. pp. 526-30. IEEE.
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]
Younes F, Halawi G, Jabbour H, El Osta N, Karam L, Hajj A, et al.
Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in university students: A cross-sectional designed study. PLoS One 2016;11:1-13.
Bhandari PM, Neupane D, Rijal S, Thapa K, Mishra SR, Poudyal AK. Sleep quality, internet addiction and depressive symptoms among undergraduate students in Nepal. BMC Psychiatry 2017;17:106.
Salehi M, Norozi Khalili M, Hojjat SK, Salehi M, Danesh A. Prevalence of internet addiction and associated factors among medical students from Mashhad, Iran in 2013. Iran Red Crescent Med J 2014;16:e17256.
Abdel-Salam DM, Alrowaili HI, Albedaiwi HK, Alessa AI, Alfayyadh HA. Prevalence of internet addiction and its associated factors among female students at Jouf University, Saudi Arabia. J Egypt Public Health Assoc 2019;94:1-8.
Seki T, Hamazaki K, Natori T, Inadera H. Relationship between internet addiction and depression among Japanese university students. J Affect Disord 2019;256:668-72.
Zhang MW, Lim RB, Lee C, Ho RC. Prevalence of internet addiction in medical students: A meta-analysis. Acad Psychiatry 2018;42:88-93.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]