Abstract
Background:
Internet has become an integral part of our daily lives but as the use of internet is increasing, it is important to be aware of the prevalence, context, and impact of its addiction on sleep and the presence of anxiety, depression, and stress in our lives.
Aim:
To assess the prevalence of internet addiction and its association with insomnia, depression, anxiety, and stress among medical students in a tertiary care medical institute in Eastern India.
Materials and Methods:
A descriptive cross-sectional questionnaire-based study with a purposive sampling method was conducted among 420 undergraduate medical students of different professional years. Out of 420 medical students, 413 students who gave consent and returned complete performa were taken in the study using a semi-structured performa for sociodemographic details, Young’s Internet Addiction Test, Insomnia Severity Index, and Depression Anxiety Stress Scale.
Results:
We found 31.2% of students had internet addiction, 24.2% had clinical insomnia, 58.1% had stress, 68.8% had anxiety, and 64.6% had depression. Potential internet addiction was significantly related to average screen time, insomnia, stress, anxiety, and depression.
Conclusions:
Internet addiction is prevalent among medical students affecting sleep, anxiety, depression, and stress, which needs urgent preventive strategies.
Keywords: Anxiety, depression, Eastern India, insomnia, internet addiction, medical student, stress
Among online markets for internet, India ranks second in the globe with around 700 million internet users and has shown an exponential increasing trend from about 4 percent in 2007 to now around 47 per cent.[1,2] For everyday routine work, social, and personal life, it has become an integral part of our lives, but studies have reported its abuse and addiction as evident from a recent meta-analysis,[3] which has become an invisible threat to our mental health and sleep.[4] The accurate classification of internet addiction is still disputed. Currently, it is included in the appendix of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) under the section encouraging and persuading further research.[5] There are various risk factors among medical students to get addicted to internet like no supervision, free access to the internet in the campus, online courses, study materials, interest for socialization and relaxations, and experimentation characteristics of adolescent and young adulthood. Internet addiction causes apprehension and is a matter of concern for parents and faculty, which cannot be overlooked in today’s times as overuse of internet has an association with mood disorders and sleep, which affects academic as well as personal life.[4,6,7] Excessive internet use brings about emotional instability, stress, anxiety, and depression among already vulnerable medical students with academic and performance pressure.[8,9,10] The medical students who would be the precious future doctors of our society, are vulnerable to internet addiction which is affecting their psychological well-being and it urgently needs our attention. Hence, this study was done to assess internet addiction, the severity of insomnia, depression, anxiety, and stress among medical students of different professional years and see the correlation with other sociodemographic and clinical variables.
MATERIAL AND METHODS
Study design and sample
A descriptive cross-sectional questionnaire-based study was conducted from the second week of April 2021 to the second week of June 2021 among 420 undergraduate medical students of different professional years of a tertiary care medical institute in Eastern India with a purposing sampling method after ethical approval (letter no. 1816/IEC/2020 dated 30/09/2020) obtained from the Institutional Ethics Committee. The participants were explained the details of the study and those who volunteered to participate and fulfilled inclusion and exclusion criteria, and informed consent was obtained. Out of 420 students, 415 gave consent and 2 performas were not complete, so 413 students were enrolled for the final assessment. They filled the questionnaire forms anonymously in around 30-45 minutes to avoid any influence, hesitancy, or bias.
Inclusion criteria
All undergraduate medical students who were willing to participate voluntarily
Students who were using internet at least for last 6 months.
Exclusion criteria
Students having substance dependence except for nicotine
Students with current syndromal or past psychiatric illness
Students with chronic debilitating medical illnesses.
Tools used
Semi-structured questionnaire for sociodemographic variables
Details about age, gender, year of study, place of current stay, parent’s occupation, urban/rural background, age at first mobile use, and average screen time per day.
Young internet addiction test (YIAT)
It is a 20-item self-report questionnaire evaluating participant’s patterns of internet use. The total score ranges between 0 to 100. We assessed normal internet use as scores between 0 and 49 and potential internet addiction as scores above 50.[11]
Insomnia severity index (ISI)
It is a 7-item self-report scale evaluating the nature, severity, and effect of insomnia. The total score on the scale ranges from 0 to 28 with clinically significant insomnia when the total score is >14.[12]
Depression anxiety stress scales (DASS 21)
It is self-report questionnaire to evaluate the emotional situation of depression, anxiety, and stress.[13]
Statistical analysis
For statistical analysis, Microsoft excel 2007 worksheet in the form of a master chart was used to enter all data collected. SPSS 23.0 (IBM. Amrock, USA) was used for the statistical analysis with 0.05 as the significance level. Categorical variables were reported in terms of numbers and percentages and continuous variables using the mean and standard deviation. To assess the relationship between categorical variables, the Chi-square test of association was used and the Pearson correlation coefficient was used to study the correlation.
RESULTS
Sociodemographic profile of the participants is given in Table 1. The prevalence of internet addiction, insomnia, stress, anxiety, and depression is shown in Table 2. Pattern of internet use [Table 3] shows the highest prevalence of internet use for the purpose of studying to be 20.3%, 14.8%, 17.9%, and 18.9% in first, second, third, and final professional students, respectively. The second highest prevalence of internet use for online shopping with 16.9%, 11.9%, 14.8%, and 13.6% in first, second, third, and final professional students, respectively. Potential internet addiction was significantly different between different professional year students with the highest prevalence in first year, i.e., 11.9%. Neither age, age of first use, gender, place of current stay nor background were significantly related to internet addiction [Table 4]. Relationship between internet addiction and clinical variables [Table 5] shows that average screen time was significantly related to potential internet addiction, which was 6.32 ± 2.59 hours. Potential internet addiction was significantly related to insomnia, stress, anxiety, and depression (P value = 0.000) and the highest prevalence of potential internet addiction (13.1%) was found in students who had clinical insomnia with moderate severity, 10.4% students who had severe stress, 14.8% students who had extremely severe anxiety, and 9.7% students who had severe depression. Analysis of the relationship between the score of the questionnaire, age at first use, and average screen time is shown in Table 6.
Table 1.
Sociodemographic characteristics of study participants (n=413)
| Sociodemographic variables | Percent |
|---|---|
| Age (Mean±SD) | 21.75±1.49 |
| Gender, Percent | |
| Female | 34.4 |
| Male | 65.6 |
| Year of Study, Percent | |
| First Professional | 28.1 |
| Second Professional | 24.0 |
| Third Professional | 23.7 |
| Final Professional | 24.2 |
| Place of Current Study, Percent | |
| Hostel | 69.2 |
| Paying Guest | 1.9 |
| Residence | 28.8 |
| Father’s Occupation, Percent | |
| Government | 38 |
| Private | 36.8 |
| Business | 25.2 |
| Mother’s Occupation, Percent | |
| Homemaker | 81.8 |
| Government | 13.8 |
| Private | 2.7 |
| Business | 1.7 |
| Background, Percent | |
| Rural | 34.1 |
| Urban | 65.9 |
SD- Standard deviation
Table 2.
Prevalence of internet addiction, insomnia, depression, anxiety, and stress according to different professional year
| Total, Percent | Professional Year of Study, Percent |
Mean±SD | ||||
|---|---|---|---|---|---|---|
| First | Second | Third | Final | |||
| Internet Addiction | 41.38±17.82 | |||||
| Normal | 68.8 | 16.2 | 17.2 | 17.2 | 18.2 | |
| Potential | 31.2 | 11.9 | 6.8 | 6.5 | 6.1 | |
| Insomnia | 10.53±6.29 | |||||
| Not Clinically Significant | 35.6 | 10.2 | 6.8 | 10.4 | 8.2 | |
| Subthreshold | 40.2 | 9.2 | 11.4 | 9.0 | 10.7 | |
| Clinical (Moderate Severity) | 19.1 | 6.1 | 4.6 | 3.4 | 5.1 | |
| Clinical (Severe) | 5.1 | 2.7 | 1.2 | 1.0 | 0.2 | |
| Stress | 17.38±9.55 | |||||
| Normal | 41.9 | 13.1 | 8.7 | 12.8 | 7.3 | |
| Mild | 17.7 | 4.8 | 5.1 | 3.6 | 4.1 | |
| Moderate | 17.7 | 2.4 | 5.1 | 3.9 | 6.3 | |
| Severe | 16.9 | 5.3 | 3.9 | 2.4 | 5.3 | |
| Extremely Severe | 5.8 | 2.4 | 1.2 | 1.0 | 1.2 | |
| Anxiety | 13.77±9.64 | |||||
| Normal | 31.2 | 9.7 | 6.3 | 10.7 | 4.6 | |
| Mild | 3.9 | 1.7 | 0.5 | 0.5 | 1.2 | |
| Moderate | 22.0 | 6.5 | 6.1 | 4.1 | 5.3 | |
| Severe | 15.5 | 2.4 | 3.9 | 3.9 | 5.3 | |
| Extremely Severe | 27.4 | 7.7 | 7.3 | 4.6 | 7.7 | |
| Depression | 14.06±10.01 | |||||
| Normal | 35.4 | 10.2 | 7 | 10.9 | 7.3 | |
| Mild | 9.9 | 3.4 | 2.7 | 1.5 | 2.4 | |
| Moderate | 28.8 | 6.3 | 7.7 | 6.8 | 8 | |
| Severe | 17.2 | 4.1 | 4.8 | 3.4 | 4.8 | |
| Extremely Severe | 8.7 | 4.1 | 1.7 | 1.2 | 1.7 | |
SD- Standard deviation
Table 3.
Pattern of internet use
| Internet Application Checklist | Professional Year of Study, Percent |
|||
|---|---|---|---|---|
| First | Second | Third | Final | |
| Instant Messaging | 14.5 | 8.2 | 12.6 | 14.0 |
| News Sites | 12.3 | 8.5 | 10.2 | 9.0 |
| Online Auction | 2.2 | 0.5 | 0.7 | 0.2 |
| Online Gambling | 1.9 | 0.7 | 0.2 | 1.0 |
| Online Gaming | 11.9 | 6.5 | 7.0 | 3.9 |
| Online Shopping | 16.9 | 11.9 | 14.8 | 13.6 |
| Personal Email | 12.1 | 5.8 | 6.8 | 8.2 |
| Recreational Surfing | 6.8 | 6.8 | 5.6 | 8.2 |
| Stock Trading | 2.9 | 1.0 | 1.5 | 1.5 |
| Studying | 20.3 | 14.8 | 17.9 | 18.9 |
| Others | 2.4 | 0.7 | 1.5 | 0.0 |
Table 4.
Relationship of internet addiction with sociodemographic variables
| Variables | Internet Addiction, Percent |
P≤0.05 | |
|---|---|---|---|
| Normal | Potential | ||
| Gender | 0.762 | ||
| Female | 24.0 | 10.4 | |
| Male | 44.8 | 20.8 | |
| Professional Year of Study | 0.025 | ||
| First | 16.2 | 11.9 | |
| Second | 17.2 | 6.8 | |
| Third | 17.2 | 6.5 | |
| Final | 18.2 | 6.1 | |
| Current Stay | 0.761 | ||
| Hostel | 48.2 | 21.1 | |
| Paying Guest | 1.5 | 0.5 | |
| Residence | 19.1 | 9.7 | |
| Father’s Occupation | 0.065 | ||
| Government | 27.1 | 10.9 | |
| Private | 26.6 | 10.2 | |
| Business | 15.0 | 10.2 | |
| Mother’s Occupation | 0.761 | ||
| Homemaker | 57.1 | 24.7 | |
| Government | 8.7 | 5.1 | |
| Private | 1.7 | 1.0 | |
| Business | 1.2 | 0.5 | |
| Background | 0.661 | ||
| Rural | 23.0 | 11.1 | |
| Urban | 45.8 | 20.1 | |
Table 5.
Relationship of internet addiction with clinical variables
| Variables | Normal Internet Use | Potential Internet Addiction | P≤0.05 |
|---|---|---|---|
| Age at First Use (years), Mean±SD | 14.42±3.17 | 14.88±2.96 | 0.163 |
| Average Screen Time (hours), Mean±SD | 5.20±2.32 | 6.32±2.59 | 0.000 |
| Age of Students (years), Mean±SD | 21.88±1.52 | 21.47±1.41 | 0.028 |
| Insomnia, Percent | 0.000 | ||
| Not Clinically Significant | 31.2 | 4.4 | |
| Subthreshold | 30.3 | 9.9 | |
| Clinical (Moderate Severity) | 6.1 | 13.1 | |
| Clinical (Severe) | 1.2 | 3.9 | |
| Stress, Percent | 0.000 | ||
| Normal | 36.3 | 5.6 | |
| Mild | 13.1 | 4.6 | |
| Moderate | 10.7 | 7.0 | |
| Severe | 6.5 | 10.4 | |
| Extremely Severe | 2.2 | 3.6 | |
| Anxiety, Percent | 0.000 | ||
| Normal | 25.9 | 5.3 | |
| Mild | 2.7 | 1.2 | |
| Moderate | 16.5 | 5.6 | |
| Severe | 11.1 | 4.4 | |
| Extremely Severe | 12.6 | 14.8 | |
| Depression, Percent | 0.000 | ||
| Normal | 29.5 | 5.8 | |
| Mild | 7.3 | 2.7 | |
| Moderate | 22.5 | 6.3 | |
| Severe | 7.5 | 9.7 | |
| Extremely Severe | 1.9 | 6.8 |
SD- Standard deviation
Table 6.
Analysis of the relationship between the score of the questionnaire, age at first use, and average screen time
| Variables | Correlations | Average Screen Time per Day (hours) | Internet Addiction | Insomnia Severity Index | Stress Scale | Anxiety Scale | Depression Scale | Age at first Mobile use (in Years) |
|---|---|---|---|---|---|---|---|---|
| Average Screen Time per Day (in hours) | Pearson Correlation | 1 | 0.212 (**) | 0.245 (**) | 0.178 (**) | 0.091 | 0.092 | -0.059 |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.064 | 0.061 | 0.229 | ||
| n | 413 | 413 | 413 | 413 | 413 | 413 | 413 | |
| Internet Addiction | Pearson Correlation | 0.212 (**) | 1 | 0.460 (**) | 0.406 (**) | 0.282 (**) | 0.373 (**) | 0.069 |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.163 | ||
| n | 413 | 413 | 413 | 413 | 413 | 413 | 413 | |
| Insomnia Severity Index | Pearson Correlation | 0.245 (**) | 0.460 (**) | 1 | 0.540 (**) | 0.472 (**) | 0.478 (**) | -0.020 |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.681 | ||
| n | 413 | 413 | 413 | 413 | 413 | 413 | 413 | |
| Stress Scale | Pearson Correlation | 0.178 (**) | 0.406 (**) | 0.540 (**) | 1 | 0.545 (**) | 0.554 (**) | 0.043 |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.384 | ||
| n | 413 | 413 | 413 | 413 | 413 | 413 | 413 | |
| Anxiety Scale | Pearson Correlation | 0.091 | 0.282 (**) | 0.472 (**) | 0.545 (**) | 1 | 0.650 (**) | -0.039 |
| Sig. (2-tailed) | 0.064 | 0.000 | 0.000 | 0.000 | 0.000 | 0.427 | ||
| n | 413 | 413 | 413 | 413 | 413 | 413 | 413 | |
| Depression Scale | Pearson Correlation | 0.092 | 0.373 (**) | 0.478 (**) | 0.554 (**) | 0.650 (**) | 1 | -0.066 |
| Sig. (2-tailed) | 0.061 | 0.000 | 0.000 | 0.000 | 0.000 | 0.179 | ||
| n | 413 | 413 | 413 | 413 | 413 | 413 | 413 | |
| Age at first Mobile use (in Years) | Pearson Correlation | -0.059 | 0.069 | -0.020 | 0.043 | -0.039 | -0.066 | 1 |
| Sig. (2-tailed) | 0.229 | 0.163 | 0.681 | 0.384 | 0.427 | 0.179 | ||
| n | 413 | 413 | 413 | 413 | 413 | 413 | 413 |
DISCUSSION
Our study reveals 31.2% of medical students have potential internet addiction, which is higher than the findings of previous Indian studies,[14,15,16,17,18] studies from other countries,[10,19,20] and in a meta-analysis[21] but was less than few earlier studies.[15] In our study, first professional year medical students were the most internet addicted with 11.9% and lowest in final professional students with 6.1%, showing that first year of study is a risk factor for internet addiction.
In this study, 64.4% of the students had insomnia (subthreshold and clinical), which is higher than the findings of a previous studies.[10,21] Clinical insomnia was 24.2%, which is higher than the findings of the previous Lebanese study[10] and an Indian study.[18] Previous studies have found that insomnia affects 8–40% of the general population.[22,23] We found prevalence of subthreshold insomnia to be more than clinical insomnia. We also found first professional medical student has highest prevalence of clinical insomnia (8.8%), thus showing that medical students are more susceptible to insomnia and first year of study could be a risk factor. In our study, potential internet addiction was significantly related to insomnia (P value = 0.000). Similar findings have been found in previous Nepalese and Lebanese studies[10,24] and certain Indian studies.[15,18,25] Interestingly, the highest prevalence of potential internet addiction was found in 13.1% of students who had clinical insomnia with moderate severity.
This study found 58.1% of the medical students had stress (mild, moderate, severe, and extremely severe), which is higher than the finding from other countries[10,26,27] but lower than that of an Egyptian study.[28] It was found that 17.7% had mild stress and moderate stress, which is higher than the findings from an Indian and Romanian study[16,26] but was almost equal and lower than the findings of the Lebanese study.[10] We also found 16.9% had severe stress, which was higher than the findings in the Lebanese[10] and Romanian studies.[26] In our study, most of the first professional students had severe stress and second professional students had mild and moderate stress, while third and final professional students had moderate stress mostly. In addition, potential internet addiction was significantly related to stress (P value = 0.000). This is similar to findings from studies of other countries[10,19,29] and Indian studies. The highest potential internet addiction prevalence of 10.4% was seen in students with severe stress.
In our study, 68.8% of students had anxiety, which is higher than the earlier findings of studies[10,27] but lower than the finding in certain Egyptian and Romanian studies.[26,28] While 27.4% of students were found to have extremely severe anxiety, most of first, second, third, and final professional medical students have extremely severe anxiety. In our study, potential internet addiction was significantly related to anxiety evaluated by DASS 21 questionnaire (P value = 0.000). A similar positive correlation has been found in other studies. The highest prevalence of internet addiction of 14.8% in students was seen in those who had extremely severe anxiety.
Among the participants, 64.6% had depression, which is higher than the findings of previous studies from Indian and other countries[10,16,27] but was almost equal to the findings of an Egyptian study[28] and lower than the findings of a Romanian study.[26] We found that most of first, second, and third professional medical students had moderate depression, while most of final professional students had severe depression. In addition, potential internet addiction was significantly related to depression (P value = 0.000). A similar positive correlation has been found in few Indian studies,[15,17] studies of other countries,[10,19] and a review in 2013.[14] We found the highest prevalence of potential internet addiction of 9.7% in students who had severe depression. We found the highest internet use was for studying followed by online shopping and instant messaging.
Potential internet addiction was significantly different between first, second, third, and final professional students (P value = 0.025), which could be explained with difference in syllabus and pressure of performance during these professional years of start and final exams. Neither gender, place of current stay, background nor age at first use was found to be significantly related to internet addiction in contrary to an Indian and Lebanese study where males were more internet-addicted.[10,18] Average screen time was significantly related to YIAT Score, ISI Score, and DASS-S Score (P value = 0.000) and potential internet addictions, and a significant association was seen for internet addiction with insomnia, stress, anxiety, and depression as in few previous studies,[10,17] which calls for future studies on this association and also planning of preventive as well as therapeutic interventions for this vulnerable professional students who are our future in health sector.
Limitations
The cross-sectional design has its inherent limitation, and a prospective study can be undertaken in future to understand the relationship between internet addiction and various clinical and psychosocial complications. In addition, this study was done during the period of COVID-19 in 2021 when the prevalent sociocultural, political situations, uncertainty, and the need for an online portal to study may have confounded the findings.
CONCLUSION
This study supports the evidence that internet addiction among medical students is a reality. Medical students are at a crossroad as they have multiple responsibilities, and it can be stressful and difficult to find time for studying and practicing medicine and relaxing, which might increase vulnerability to internet addiction. Addiction is causing to stay awake for long leading to insomnia, stress, anxiety, and depression, or whether medical students when stressed or anxious are using the internet for escape and landing into internet addiction and insomnia. This study calls for the need for redressal of these issues and calls for preventive strategies and psychological interventions for the vulnerable lots by medical schools.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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