A Cross-Sectional Analytical Study on Internet Addiction and Associated Risk Factors Among Medical Students

Introduction

Internet addiction (IA) is defined as excessive use of the Internet that impairs an individual’s psychological state (emotional and mental), occupational, scholastic, and social interactions.¹ Kimberly Young was the first to propose recognizing problematic computer use as a form of addiction and advocated for its inclusion in the Diagnostic and Statistical Manual of Mental Disorders – IV (DSM-IV).²

The Internet has become an integral part of daily life. With increasing global usage, both the number of users and dependence on it continue to rise. India is the second largest user of the Internet worldwide, with approximately 665.31 million users in 2019. The concept of IA as a behavioural disorder, meeting the six core components of addiction, was first proposed by Goldberg.

Based on various studies, the types of IA identified include net compulsions, cyber-relationship addiction, computer addiction, cyber-sexual addiction, and information overload.³ The prevalence of IA varies between countries and within a country, depending on the context of assessment and the sub-types. A study conducted in six Asian countries reported the prevalence rates ranging from 5% to 21%. In India, the prevalence of IA among college students has been found to range from 5% to 46.7%.⁴ Although IA affects all age groups, its impact on the younger generation can lead to poor academic performance, physical, mental, and behavioural disturbances, reduced productivity, and cognitive dysfunction.⁵ Previous studies have reported varying prevalence rates among different student populations. Therefore, this study was undertaken to determine whether the prevalence of IA among medical students falls within a similar range, or differs, compared to other groups. It also aimed to examine the patterns of internet usage, and its impact on their overall physical and mental health.

Materials and Methods

A cross-sectional descriptive study was conducted among medical students of Vijayanagara Institute of Medical Sciences, Bellary, during November 2023. Considering the prevalence of 38%, a 99% confidence interval (CI), 10% absolute error, and a 10% nonresponse rate, the required sample size was calculated to be 173. A convenient sampling technique was employed, and to achieve the required sample size, all 2^nd and 3^rd year MBBS students were included. The total number of participants in the study was 230. All students who were present during the study and provided consent were included. Students who were not present during data collection were excluded. Data were collected using a pretested semi-structured questionnaire and Young’s Internet Addiction (IA) test scale.

The questionnaire consisted of three sections: the first included questions on sociodemographic information; the second addressed information related to Internet dependency and usage patterns; and the third included the IA impairment index. The questionnaire was developed based on ‘Dr. Kimberly S. Young’s Therapist’s Guide to Assess and Treat IA’ to evaluate dependency and the addiction impairment index.

Young’s IA test scale was used to assess the degree, and the IA impairment index was applied to categorize the severity of addiction. The scale consists of 20 items, with a maximum score of 100, where higher scores indicate greater severity of addiction. Participants with scores ranging from 0 to 30 were classified as having normal internet use, scores of 31-49 indicated mild Internet addiction, 50-79 reflected moderate IA, and 80-100 indicated severe dependence on the Internet. Scoring on the IA test was based on a six-point Likert scale.

The data were entered in MS Excel sheet and analysed using SPSS version 20.0 and Jamovi software version 2.2.5. Categorical data were analysed using descriptive statistics and numerical data were presented as mean, and standard deviation. Univariate analysis was performed for those associated with IA disorder and significant variables were tested with logistic regression model.

Results

A total of 230 participants were approached to achieve the predetermined sample size of 173, yielding a response rate of 100%. Of the 230 respondents, 103 (44.8%) were classified as addicted to the Internet. Based on the IA impairment index developed by Dr. Kimberly Young, 70 (30.4%) had mild addiction, 32 (13.9%) had moderate addiction, and 1 (0.4%) had severe addiction (Figure 1). The reliability and internal consistency of the Young’s IA test scale was very high, with a Cronbach’s alpha of 0.926 and McDonald's omega of 0.928.

The correlation between various variables using a heatmap based on Pearson correlation coefficients. The values range from -1.0 to 1.0, where positive values (shown in green) indicate a direct relationship between variables, and negative values (shown in red) suggest an inverse relationship. The intensity of the color reflects the strength of the correlation darker shades represent stronger associations. This visual representation helps quickly identify which variables are closely linked and which are not, aiding in data interpretation and decision-making (Figure 2).

The response rates of participants to the IA index are presented in Table 1.

Since the study was conducted among MBBS students, all participants were between 19 and 23 years of age, with a mean age of 20.58±0.8 years. The sample consisted of equal distribution of males (50%) and females (50%). A significant association was observed between gender and IA (P= 0.00).

Table 2 shows that students aged >20 years (59.2%) were more likely to be addicted to the Internet compared to those aged <20 years (40.8%), although the difference was not statistically significant. Students whose fathers were skilled workers (54.4%) demonstrated higher levels of Internet addiction compared to those whose fathers were unskilled workers (32%). A significant association was found between higher socioeconomic status and greater Internet addiction (P= 0.02).

Internet addiction often involves dependence on specific applications that trigger excessive Internet use. In this study, students showed higher levels of addiction to online gaming (P=0.015), social networking sites (P=0.003), virtual casinos (P=0.022), and adult sites (P=0.000) (Table 3).

Cravings and withdrawal

When assessed for cravings and withdrawal, the majority of students classified as Internet-addicted responded positively to several items listed in Table 3, with statistically significant differences compared to non-addicted students. They reported being always preoccupied with the Internet (P <0.001), frequently thinking and talking about going online (P <0.001), and experiencing irritation or anxiety when unable to access the internet due to technical or other issues (P <0.001). Many indicated using the Internet as a means of escaping situational difficulties (P <0.001). Many participants had created online personas, often multiple, to remain engaged online (P <0.001). Additionally, most reported disrupted relationships with their friends or family (P <0.001), and many complained of difficulty concentrating on their studies (P <0.001) (Table 3).  

Insomnia (48.3%) was the most commonly reported symptom among students when unable to access the internet, while 4.3% reported experiencing panic attacks (Table 4).

Discussion

The present study examined the prevalence of IA among young adults at a medical college in Ballari, Karnataka. It also explored associations between IA and various sociodemographic factors as well as internet usage patterns. Our study revealed an overall prevalence of IA of 44.8%, with 30.4% classified as mild, 13.9% as moderate, and only 0.4% as severe. These findings are in accordance with a study conducted in the Silicon Valley city of Bangalore.⁶ A study by Anku M et al. on adolescents reported an IA prevalence of 80%, with higher rates among males compared to females.⁷ Although the overall prevalence in that study was higher than in ours, the gender distribution was comparable, showing similar prevalence rates among males and females. The study also reported that 65% of participants had mild IA, 13.4% had moderate IA, and 1.9% had severe IA. While the prevalence of moderate and severe IA was similar to our findings, the proportion of mild addiction was higher compared to our study (30%). In contrast, few studies have reported much lower prevalence. For instance, a study done by Raju Srijampana VG in Andhra Pradesh found an overall addiction prevalence of 10.8%, with comparable rates among males and females.⁸ Such varied prevalence across different regions of India highlight the growing concern of IA as an emerging mental health issue among medical students.

Gupta Aman et al. conducted a study in North India to examine the association between sociodemographic characteristics and IA among college students.⁹ Their results showed a significant association between socioeconomic status and IA, but no association with age, gender, year of course, or medium of instruction. In contrast, our study demonstrated significant associations of IA with gender, father’s occupation, and socioeconomic class. These differences may be attributed to the diverse economic and social backgrounds of medical students, who come from various regions. The present study supports the prevailing perception that individuals from higher income groups, owing to greater access to resources, exhibit increased Internet usage. Notably, our findings reveal that this enhanced access is significantly associated with higher levels of Internet addiction. This outcome stands in contrast to the findings of Hassan et al. in Bangladesh, who reported an inverse relationship between economic status and Internet usage.¹⁰

Studies conducted in various parts of the country, including those by Anusha PMC11 in Vadodara, Venkat Babu in Andhra Pradesh, and Krishnamurthy in Bangalore, have reported that the most common purposes of Internet use among students are social networking, chatting, online gaming, shopping, and accessing media files, with academic use being the least common. The findings of our study are congruent with these reports; however, our participants also reported using the internet to access adult sites, virtual casinos, and online games. Adolescents are particularly vulnerable to developing IA due to their intellectual characteristics, lack of supervision, and susceptibility to forming online friendships.

We found a positive correlation between cravings for internet use and withdrawal symptoms such as insomnia, anxiety, when participants were unable to access the internet; for example, due to lack of recharge or financial constraints in requesting money from parents. Such difficulties may have a significant impact on the overall mental health of affected individuals.

Aswathy Dasin his study on technology addiction found that subjects with technology addiction demonstrated premorbid personalities like anxiousness, impulsive behaviour, eccentric dramatic traits, and difficult childhood temperament.¹² In our study, half of the study subjects with IA reported insomnia, and about 1/3^rd experienced anxiety, restlessness, and irritability. A smaller proportion reported panic attacks. A significant association was observed between IA and most factors related to cravings and withdrawal. A study conducted in Mumbai similarly found a significant association between IA and depression among adolescents. Goel et al. and other authors who conducted studies outside India across multifarious populations from different countries have also reported associations of IA with depression and insomnia.^10,12-14.

Students suffering from IA may experience increased levels of irritability, anxiety, as withdrawal symptoms, often accompanied by co-existing depression. Those with insomnia or irritability may resort to excessive internet use as an evasion strategy to cope with dysphoric mood.

A strong positive association was noted between concealment and the risk of IA. Many students reported creating multiple personas and concealing their identity from family and friends. Similar findings were reported by other authors, who identified online friendships as being positively associated with IA, thereby supporting our study results.^10-12,14 Individuals with dismissive personality traits may also resort to excessive Internet use as a means of escaping real-world problems, which can further reinforce addictive behaviour.

Conclusion

Internet use was found to be particularly high for accessing social networking platforms. Accessibility of high-speed internet on smartphones has contributed to increased time spent on such activities. There is a pressing need to develop guidelines and interventions at the primary health care level to reduce IA among adolescents. Clear recommendations should be formulated regarding the appropriate age for exposure to the internet, the age at which personal devices may be provided, and the permissible duration of daily Internet use.

Conflict of Interest

No conflict of interest