|
 
 |
| ORIGINAL ARTICLE |
|
| Year : 2022 | Volume
: 12
| Issue : 6 | Page : 257-264 |
|
Association of internet addiction with anxiety, stress and quality of life among undergraduate students
Panneerselvam Periasamy1, Vajiravelu Suganthi1, Sasikala Gunasekaran2, Jayaraman Narenkumar3, Vasudevan Ramachandran3, Ajitha Kannabiran3
1 Department of Physiology, Vinayaka Mission's Kirupananda Variyar Medical College and Hospital, Vinayaka Mission's Research Foundation (Deemed to be University), Salem, Tamil Nadu, India
2 Department of Nursing, Government Erode Medical College and Hospital, Erode, Tamil Nadu, India
3 Centre for Materials Engineering and Regenerative Medicine, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| Date of Submission | 17-Jun-2022 |
| Date of Decision | 26-Aug-2022 |
| Date of Acceptance | 03-Oct-2022 |
| Date of Web Publication | 29-Dec-2022 |
Correspondence Address:
Dr. Panneerselvam Periasamy
Vinayaka Mission's Kirupananda Variyar Medical College and Hospital, Vinayaka Mission's Research Foundation (Deemed to be University), Salem, Tamil Nadu
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/cmrp.cmrp_54_22
Background: This study examined undergraduate MBBS and nursing students' quality of life with internet addiction and the association of anxiety and stress.
Aim: We aimed to partially replicate the participant's online activities, changes in behaviour by internet addiction, quality of life when internet access was not possible and frequency of internet use.
Materials and Methods: A cross-sectional study was applied in included university involving 400 MBBS and nursing students. World Health Organization Quality of Life-BREF Questionnaire (WHOQOL-BREF) scale for assessment of the personal quality of life. Scores of internet addiction test, Beck Anxiety Inventory and WHOQOL-BREF were compared by using the Mann–Whitney test or Kruskal–Wallis test.
Results: The main findings revealed various risk factors associated with internet addictions, such as psychological distress, anxiety, mood disorders, suicidality, aggression, stress and sleep problems. Most of the intervention studies used an invariant behavioural therapy approach, although other interventions appeared effective in reducing addiction symptoms.
Conclusion: Based on the students' addiction finding of an association between quality of life with internet addiction and anxiety and stress problems across combination with high prevalence rates amongst adolescents and university students, the individuals will use these tools. The implication of these findings is further discussed, and research is needed to develop and implement prevention strategies and treatment offers.
Keywords: Internet addiction, internet addiction test, MBBS and nursing students, quality of life, World Health Organization Quality of Life -BREF Questionnaire
How to cite this article:
Periasamy P, Suganthi V, Gunasekaran S, Narenkumar J, Ramachandran V, Kannabiran A. Association of internet addiction with anxiety, stress and quality of life among undergraduate students. Curr Med Res Pract 2022;12:257-64 |
How to cite this URL:
Periasamy P, Suganthi V, Gunasekaran S, Narenkumar J, Ramachandran V, Kannabiran A. Association of internet addiction with anxiety, stress and quality of life among undergraduate students. Curr Med Res Pract [serial online] 2022 [cited 2023 Feb 5];12:257-64. Available from: http://www.cmrpjournal.org/text.asp?2022/12/6/257/366170 |
| Introduction | |  |
In our day-to-day life, the internet is being integrated as a part of life because the usage of the internet has been growing to explore worldwide. Internet addiction has emerged as a potential problem in young people, which refers to excessive use of computers that interfere people, which refers to excessive computer use that interferes with their daily lives. The most popular social networks are YouTube, Facebook and WhatsApp. Studies indicate that the risk of internet addiction seems to be rising, especially amongst young people.[1] Several studies have been conducted in India on internet addiction.[2] Internet addiction is defined as any online-related, compulsive behaviour that interferes with normal living and causes severe stress on family, friends and one's work environment. Internet addiction research has looked into a number of psychological factors that are related to it, including shyness, self-consciousness, anxiety, sadness, and interpersonal relationships.[3] Internet addiction is an inability to control the use of the internet accompanied by preoccupation with internet use, developing tolerance and withdrawal symptoms.[4] Students were experiencing stress mood and having anxiety problems because they use smartphones more frequently to alleviate negative emotions, thus potentially causing further social isolation and a possible increase in smartphone overuse.[5] Normally students faced more stress and anxiety problem because of attending laboratory sessions, new academic studies and patient attending problems. The new problem was using the internet, and addiction to that is also another problem to become stressful. Since the late 1990s, it has been argued that the majority of individuals who use the internet excessively are not addicted to it per se but rather use it to feed other, more particular addictions. In other words, most people have addictions to the internet rather than to it.[6] The primary negative consequence of the internet is described as the inability to overcome one's desire to use the internet excessively, constantly remaining busy without the internet worthless, experiencing a state of extreme anger and aggression in the event of its absence and the deterioration of one's professional, social and family life.[7] The students suffer from anxiety as some may use the internet to distract themselves from their worries and fears. On the other hand, many internet addicts suffer from addictions such as drugs, alcohol, gambling and sex. The addicts often use social networking sites, instant messaging or online gaming as a safe way of establishing new relationships and more confidently relating to others. They become unhappy because some might be wondering where they fit in, and the internet could feel more comfortable than real-life friends.[8] Anxiety is an emotion characterised by the subjective experience of anxious affect, physical changes and anxious affect, physical changes and anxious thoughts and stress-related chronic non-specific arousal, tension, agitation and irritability. In contrast, depression is associated with anhedonia, dysphoria, hopelessness and loss of interest in studies.[9] Internet addiction, according to Block, comprises four elements. Tolerance includes the need for better computer equipment, more software, and longer use hours. Negative effects include arguments, lying, subpar performance, social isolation, and exhaustion. Excessive internet use is associated with a loss of sense of time or a neglect of basic drivers, and it includes anger, tension, and depression when the computer is unavailable.[10] Internet addicts, so the theory goes, want to use the internet more frequently and feel excited when they do. They compulsively use the internet and show symptoms and admit that internet use negatively affects their lives in the area such as school, health and parental relationships. Internet use and failure to control the amount of time spent on the internet lead to problems with time management. Additional studies also point out interpersonal and health problems. There are some ways to avoid developing an internet addiction for the benefit of students' education, including monitoring and limiting internet use by setting a timer, making distractions for yourself to put off using the internet, removing the temptation to browse, and blocking time-wasting websites on your internet browser, forming healthier routines: utilise relaxation techniques to ease stress and anxiety, replace internet use with a healthy activity.[11] This study is a preliminary step towards understanding the extent of internet addiction amongst undergraduate MBBS and nursing students in Erode, Tamil Nadu. By studying the association of internet usage and its effects on human behaviour, we can formulate interventions such as setting boundaries and detecting early warning signs of underlying psychopathology at the earliest. The main objective of the present study is to determine the frequency of internet addiction amongst undergraduate MBBS and nursing students, assess students' quality of life, stress, anxiety, as well as compare internet addictions between MBBS and nursing students in different years.
| Methods | | |
Participants
Four hundred MBBS and nursing students of both sexes studying at IRT Medical College were selected. The purpose of the study was explained to all the participants, and written informed consent was taken from them. We provided them a proforma with information on demographics, including name, age, gender, weight, and height; designation of internet use purpose (by selecting from options like education, entertainment, news, gaming, or social networking); monthly total expenditure; amount spent each month on the internet; and location of access (home, cybercafé, or college). Time of day when people use the internet most, the average amount of time spent on other extracurricular activities, the amount of time spent on other extracurricular activities, and whether people are addicted to drugs (tea, coffee, or other substances). The proforma also covered historical history of psychiatric disorder, whether present or absent.
Assessment tools
Basic sociodemographic information, including age, gender, religious beliefs, number of children in the family, living conditions, perception of personal health and weight, study pressure and relationships with classmates, teacher and family, was collected by a data collection sheet designed for this survey. The following self-assessable questionnaire and tools were used to get the needed information. Internet addiction test (IAT), Beck Anxiety Inventory (BAI), Depression, Anxiety and Stress Scale 42 (DASS-42), World Health Organization Quality of Life-BREF Questionnaire (WHOQOL-BREF) and Epworth Sleepiness Scale (ESS) were tested.
Internet addiction test
IAT, which has satisfactory psychometric properties, has been validated in many countries.[12] A 20-item 5-point Likert scale measures the severity of self-reported compulsion use of the internet. Total internet addiction scores were calculated, with possible scores for 20 items ranging from 20 to 100. According to Young's criteria, total IAT scores 20–49 represent average users with complete control by their internet use, scores 40–79 represent overusers with frequent problems caused by their internet use and scores 80–100 represent internet addicts with significant problems caused by their internet use.
Beck Anxiety Inventory
BAI created[13] is a 21-item multiple-choice self-report inventory that measures the severity of anxiety in adults and adolescents. The value for each item is summed, yielding an overall or total score for 21 symptoms that can range between 0 and 63 points. The values for all 21 symptoms can range between 0 and 63 points. A total score 0–7 is interpreted as a 'minimal' level of anxiety, 8–15 as 'mild', 16–25 as 'moderate' and 26–63 as 'severe'.
Depression, Anxiety and Stress Scale 42
The DASS is a 42-item self-report tool designed to measure depression, anxiety and stress.[14] Items 3, 5, 10, 13, 16, 17, 21, 26, 31, 34, 37, 38 and 42 form the depression scale and assess dysphoria, hopelessness, deviation of life, self-deprecation, lack of interest or involvement and inertia. The anxiety scale has 14 items: 2, 4, 7, 9, 15, 19, 20, 23, 25, 28, 30, 36, 40 and 41 which measures autonomic arousal, and the skeletal stress scale has items 1, 6, 8, 11, 12, 14, 18, 22, 27, 29, 32, 33 and 39 which quantifies the difficulty in relaxing, nervous arousal and being easily upset or agitated or irritable.
World Health Organization Quality of Life-BREF Questionnaire
A self-report questionnaire with 26 items from the WHOQOLBREF assessment scale to assess environment domains making up the WHOQOLBREF short version. A higher score indicates a higher QOL. Qualitative data were expressed as percentages and quantitative data were expressed as mean standard deviation. Scores of IAT, Beck Anxiety Inventory and WHOQOL-BREF were compared by using the Mann–Whitney test or Kruskal–Wallis test followed by Dunn's post hoc multiple comparisons.[15] P < 0.05 was considered statistically significant. Spearman's rank correlation was assessed a statistical correlation between internet addiction and the above-stated psychiatric variables.
Epworth Sleepiness Scale
The ESS is a tool to identify daytime sleepiness symptoms, and the ESS was introduced by Dr. Murray W Johns in 1991 (30–32). The ESS is an eight-item questionnaire that assesses the severity of daytime sleepiness in various situations. ESS has been validated mainly for obstructive sleep apnea (OSA) (36–39, 48 and 49). Subjects respond to the following question on the scale: 'How likely are you to doze off or fall asleep in the situations described below, in contrast to just feeling tired '? This refers to your usual way of life in recent times.[16] Even if you have not done some of these things recently, try to work out how they would have affected you. 'Each item on the ESS is assessed on a Likert scale of dosing (0 = would never doze, 1 = slight chance of dozing, 2 = moderate chance of dozing and 3 = high chance of dozing) for the following items: sitting and reading, watching television, sitting inactive in public like theatre or a meeting, driving a car for 1 h without a break, lying down to rest in the afternoon when circumstances permit, sitting and talking to someone, sitting quietly after lunch without alcohol, or in a car, and while stopped for a few minutes in traffic. A score between 11 and 24 is deemed abnormal and symptomatic of excessive daytime somnolence; a score between 0 and 10 is regarded normal. The probability of dozing ranges from 0 (would never doze), 1, 2, and 3 (slight, moderate, and high chances, respectively).[17] A total score of students is divided into excessive daytime sleepiness (EDS) and no excessive daytime sleepiness (non-EDS) groups based on the total score. A score of ≤10 was considered non-EDS group and the score >10 was considered EDS group.[18]
Statistical analyses
When appropriate and applicable, independent sample t-tests, Kruskal-Wallis H-tests, Mann- Whitney U-tests, and Chi-square tests were used to conduct statistical analyses on demographic variables, compare MBBS and nursing students, and compare students with and without IA regarding sociodemographic characteristics. ESS analyses were used to evaluate the relationship between continuous fundamental demographic parameters. The association between continuous basic demographic characteristics was examined using ESS analyses. IAT scores were assessed by demographic variables; there were many variables analysed to know about the student addiction on internet usage. Anthropometric variables, relationships between ESS score and internet usage data, relationships between internet score level and demographic data, and relationships between internet score level and internet-related variables. The variables were calculated using Chi-square test and Kruskal–Wallis H-test.
| Results | | |
Demographic and anthropometric variables
The socioeconomic factors such as employment, education, income, marriage rates, birth and death rates are statically significant. [Table 1] shows the age, gender, course and study year of undergraduate medical students, nurses, and other paramedical students. Amongst the 318 respondents, 92 (28.93%) were male, and 226 (71.07%) were female. The mean age of males was 20.30 ± 4.41 years and that of females was 20.38 ± 3.81 years. 48.11% of the study participants were medical students and 30.50% were nursing students. 72.01% of the students are 1st- and 2nd-year students. In addition, they collected the data from students to know about IAT scores represented in [Figure 1]. We differentiated the levels by mild (n = 25, 78.93%), moderate (n = 55, 17.30%), and severe (n = 12, 3.77%) in order to know the final score of the test (n = 318, 100%). [Table 2] shows anthropometric variables of students, the weight was found to be n = 29, 9.12%, n = 31, 9.75% and height was observed n = 27, 8.49%, n = 44, 13.84% respectively. | Table 2: Distribution of Study participants based on anthropometric variables
Click here to view |
 | Figure 1: Pie diagram shows the level of internet addiction test score amongst undergraduate medical and nursing students
Click here to view |
Association between Epworth Sleepiness Scale score and internet usage
Excessive daytime sleepiness (EDS) scores are higher among students who spend more money per month to internet access the and also spend more time using the internet per day. It was confirmed using Kruskal–Wallis H-test shown in [Table 3]. The variables are calculated previously for the measurement. Anthropometric measurements are a series of quantitative measurements of the bone and adipose tissue used to assess the body's composition. As shown in [Table 3], the weight ranges from 40–70 kg, the height ranges from 140–170 cm, and the body mass index (BMI) ranges from underweight to obese. | Table 3: Association between Epworth Sleepiness Scale score and internet usage variables
Click here to view |
Association between the level of internet score and demographic variables
The students' aged less than 30 years and in their fourth and fifth year of study are significantly associated with more internet addiction score. Statistical significance was calculated by using Chi-square test represented in [Table 4]. According to the Young's criteria, total IAT scores 20–49 represent overusers with frequent problems caused by their internet use and scores 80–100 represent internet addicts with significant problems caused by internet use. Chi-square test was used to determine the internet score level and demographic factors under the students' IAT score, which were divided into mild, moderate, and severe. | Table 4: Association between level of internet score and demographic variables
Click here to view |
Level of internet score and internet-associated variables
Time between 8–12 PM has more internet addicts score and >6 h internet users have more internet addicts score which was statistical ly significant, shown in [Table 5]. Internet-related variables are verified by the purpose of using the internet, monthly total expenditure for internet access; internet accessed time of day, average duration of internet use per day, spent on other extracurricular activities per day, addiction of any substance, history and family history of psychiatric illness. | Table 5: Association between level of internet score and internet-associated variables
Click here to view |
Association between internet usage score and anthropometric variables
Additionally, the users who reported the lowest quality of life (n = 1, 3.45%) and the mildest weight results (n = 18; 62.07%) were used to construct the internet usage score and anthropometric. IAT score was severe (n = 2, 14.28%) while BMI was assessed as mild (36, 81.82%). Because they score more for addiction than other students, pupils with IA score higher than those who are overweight or obese. This result was analysed by statistical significance by using the Chi-Square test shown in [Table 6]. 61.6% (n = 196) of students have been benefited with the controlled usage of social media and 29.55% (n = 34) have been adversely affected due to the excess dependency on social media, which is statistically insignificant. | Table 6: Association between internet usage score and anthropometric variables
Click here to view |
| Discussion | | |
Internet addiction has a negative impact on the health and safety of MBBS and nursing students by raising their levels of stress and anxiety. The new emergence of mobile technology has increased the social network for the use of student internet addiction, providing an easily accessible and immediate means to satisfy students' needs, possibly augmenting the potential for the use.[19] The demographic variables are represented in [Table 1]. Mobile broadband does not typically replace home Internet use when comparing the two uses of the internet. In the current survey, 8% more males than girls reported using the internet for leisure reasons each week, which is consistent with findings from earlier studies.[20] In other terms of the participants' relationship status, being single was also associated with spending more time online; this finding is consistent with other studies.[21] The participants' were examined regarding favoured online activities and fictitious anticipated shifts in online behaviour. There were two questions created. The participant shows a stronger preference for the following pursuits: accessing general information, social networking, email, conversing, and gaming that involves gathering reports.[22] These results are highly indicative that was internet user's specific activities with specific content. The anthropometric variables including the weight (n = 29, 9.12%); (n = 31, 9.75%) are shown in [Table 2].
In some cases, the computer may be the mechanism for administering or gaining the object of addiction. The job satisfaction of working individuals using the internet may help shape and promote the job satisfaction of working individuals.[23] Furthermore, in the present study, the more money and more time are spent for internet access and using internet daily. EDS is presented in [Table 3]. The internet scores of the students were analysed and reported in [Figure 1]. The student addiction is different with terms. However, the IA is the major problem for students because that will not give knowledge about the studies important for students' lives. The situation in the ESS is score for each subject ranging from 0 to 24. A range of 11–24 to abnormal to the range of dozing was verified to the range of student internet addiction. The study has not focused on the parents' awareness and involvement in monitoring their children's behaviour and feedback was not collected as majority of the students are staying in hostels.
| Conclusion | | |
This study aimed to find out the variables such as quality of life with internet addiction, self-esteem, anxiety and stress to undergraduate MBBS and nursing students. In contrast to studies that have been done so far, it was also examined whether these variables explain responses to subscales of internet addiction. The association between ESS score and Internet usage variables has been shown in the literature. Anxiety and depression were shown to be the two anthropometric characteristics in this study's results that had the greatest impact on online addiction, followed by internet usage score. Many studies have confirmed these findings.[24] For the students' physical and mental health, the adoption of educational programmes and preventive measures on IA, such as self-regulating interventions, education programmes for parents, teacher and student communication become stronger, and even cognitive behavioural therapy is required. As a result, IA has a detrimental effect on quality of life. As this is a global problem, preventive control measures stand valid to balance internet usage and academic activities. Awareness has to be created amongst the youth about the potential hazards to health and quality of life. The implementation of educational programmes and preventive measures on IA, such as self-regulating interventions, education programmes on parents, teacher and students' communication become stronger, and even cognitive behavioural therapy, is necessary for the students' health and mental well-being.
Acknowledgement
We sincerely thank all the participants for taking the time to complete our survey. The authors thank the Government Erode Medical College and Hospital, Perundurai, Tamil Nadu, for providing the necessary facilities.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Gholami A, Jahromi LM, Zarei E, Dehghan A. Application of WHOQOL-BREF in measuring quality of life in health-care staff. Int J Prev Med 2013;4:809-17.  |
| 2. | |
| 3. | Shete AN, Garkal KD. A study of stress, anxiety, and depression among postgraduate medical students. CHRISMED J Health Res 2015;2:119-23. [Full text] |
| 4. | Ayaroglu NS. The relationship between Internet use and loneliness of university students. Master's thesis, middle East technical university. Scand J Psychol 2002;54:14-8. [doi: 10.1111/sjop. 12049]. |
| 5. | Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: Psychometric properties. J Consult Clin Psychol 1988;56:893-7. |
| 6. | Chathoth V, Kodavanji B, Arunkumar N, Pai SR. Internet behaviour pattern inundergraduate medical students in Mangalore. Int J Innovative Res Sci Eng Tech 2013;2:2133-6. |
| 7. | Çikrıkci Ö. The effect of internet use on well-being: Meta-analysis. Comput Hum Behav 2016;65:560-6. |
| 8. | Durkee T, Kaess M, Carli V, Parzer P, Wasserman C, Floderus B, et al. Prevalence of pathological internet use among adolescents in Europe: Demographic and social factors. Addiction 2012;107:2210-22. |
| 9. | Griffiths MD, Pontes HM. Internet addiction disorder and internet gaming disorder are not the same. J Addict Res Ther 2014;5:e124. |
| 10. | Griffiths MD. Internet addiction: Internet fuels other addictions. Stud Br Med J 1999;7:428-9. |
| 11. | Herbert M, Johns MW, Doré C. Factor analysis of analogue scales measuring subjective feelings before and after sleep. Br J Med Psychol 1976;49:373-9. |
| 12. | Cash H, Rae CD, Steel AH, Winkler A. Internet addiction: A brief summary of research and practice. Curr Psychiatry Rev 2012;8:292-8. |
| 13. | Kim H, Chung YW. The use of social networking services and their relationship with the big five personality model and job satisfaction in Korea. Cyberpsychol Behav Soc Netw 2014;17:658-63. |
| 14. | Kim YJ, Jang HM, Lee Y, Lee D, Kim DJ. Effects of internet and smartphone addictions on depression and anxiety based on propensity score matching analysis. Int J Environ Res Public Health 2018;15:E859. |
| 15. | |
| 16. | Johnson LC, Spinweber CL, Gomez SA, Matteson LT. Daytime sleepiness, performance, mood, nocturnal sleep: The effect of benzodiazepine and caffeine on their relationship. Sleep 1990;13:121-35. |
| 17. | Nagori N, Ashok U, Vala D, Bharat N, Panchal V, Imran J, et al. Association of anxiety and quality of life with internet addiction among medical students. Dep Psychiatry 2016;7:9-13. |
| 18. | Pontes HM, Griffiths MD, Patrao IM. Internet addiction and loneliness among children and adolescents in the education setting: An empirical pilot study. Aloma Rev Psicol Ciències Educ Esport 2014;32:91-8. |
| 19. | Richardson GS, Carskadon MA, Flagg W, Van den Hoed J, Dement WC, Mitler MM. Excessive daytime sleepiness in man: Multiple sleep latency measurement in narcoleptic and control subjects. Electroencephalogr Clin Neurophysiol 1978;45:621-7. |
| 20. | Ramesh Masthi NR, Pruthvi S, Phaneendra MS. A Comparative Study on Social Media Usage and Health Status among Students Studying in Pre-University Colleges of Urban Bengaluru. Indian J Community Med. 2018;43:180-4. |
| 21. | Tsai CC, Lin SS. Analysis of attitudes toward computer networks and internet addiction of Taiwanese adolescents. Cyberpsychol Behav 2001;4:373-6. |
| 22. | Upadhyay P, Jain R, Tripathi VN. A study on the prevalence of internet addiction and itsassociation with psychopathology in Indian adolescents. Indian J Neurosciences 2017;3:56-60. |
| 23. | Young KS. Internet addiction: A new clinical phenomenon and its consequences. Behav Sci 2004;48:402-15. |
| 24. | Young KS. Treatment outcomes using CBT-IA with internet-addicted patients. J Behav Addict 2013;2:209-15. |
[Figure 1]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]
|
Search Pubmed for