Internet addiction and associated factors among undergraduate students of Jimma University; Jimma, South West Ethiopia, institutional based cross-sectional study
BMC Psychiatry volume 23, Article number: 721 (2023)
Internet addiction affects cognitive function, has a harmful impact on students’ academic performance, and increases their risk of experiencing psychological crises.
Examining the prevalence of internet addiction and its contributing factors among regular undergraduate students at Jimma University in south-west Ethiopia.
An institutional-based cross-sectional study was conducted among study participants between August 1 and August 30, 2021. A total of 772 Participants were involved in the study using a multistage random sampling technique. Data was collected using pretested and structured questionnaires with self-administered techniques. The Young Internet Addiction Test (YIAT) was used to measure internet addiction. The data was entered into the computer using Epi Data version 4.6, and then it was exported to the Statistical Package for Social Science (SPSS) version 25 for analysis. The association between each independent variable and the outcome variable was examined using bivariate analysis. Variables with a p-value of less than 0.25 in bivariate analysis were included in the multivariate logistic regression model to determine how each independent variable affected the outcome variable.
The prevalence of internet addiction among study participants was 53.6% (95% Confidence Interval (CI)) (49.99%, 57.15%). Findings from multivariate logistic regression analysis suggested a variety of related factors had significant associations with internet addiction. Being dissatisfied with a major study, having a cumulative grade point average of a promoted grade report, using the internet for entertainment, using the internet for Facebook, using the internet for telegram, depression, social anxiety, and poor social support.
This study revealed a comparatively high frequency of internet addiction among study participants. Internet addiction has been linked to psychosocial, academic, and purpose-related aspects of internet use. As a result, incorporating stakeholders’ efforts to improve the identified variables would be a helpful start toward lowering this high incidence.
The internet is one of the major forms of media transforming youth learning and social communication in the 21st century . This medium has evolved into one of our most useful and advanced technological advancements. Today, the internet allows access to various services such as e-mail, the World Wide Web, and social media sites such as Facebook and Telegram .
Internet addiction (IA), which is recognized as problematic internet use is defined as a person’s inability to stop using the internet, despite the negative effects on their physical, mental, and psychosocial health [3, 4]. Internet addiction (IA), which is a person’s loss of control over their internet usage, is regarded as a thoughtful mental health problem because it causes distress and functional impairment in one’s daily activities . The global prevalence of internet addiction is estimated to be 6%, with rates varying across the globe .
An increased interest in and an investment of resources (time, energy, money, etc.) in online activities are common signs of internet addiction. Additionally, when not using the internet, a person may have undesirable emotions (such as worry, depression, or emptiness) that are alleviated by using the internet . Although the internet was initially designed to promote communication and research, usage has dramatically increased recently, leading to pathological use or internet addiction, which affects academic performance, psychological wellbeing, social interaction with peers, and families .
Various research studies have found links between IA and substance abuse, depression, and social anxiety [11, 12]. It is also associated with men’s constant online availability, a lower use of the internet for academic purposes, making new friends online, and forming friendships online . Several international articles have opposing views on the incidence of internet addiction and related factors among university students. As a result, conducting research among college students added value to the contradictory findings on the prevalence of IA and provided new findings for our nation.
As far as the investigators’ level of knowledge goes, little is explored about internet addiction and its associated factors in our country, and they failed to address important variables such as student academic performance, depression, social anxiety, and social support. This indicates that the topic requires continued attention, and the study’s findings may serve as a scientific benchmark in the Ethiopian context as well as a foundation for other researchers to conduct additional research in the area. Furthermore, the study’s findings will assist policymakers, planners, and those working in higher education in identifying appropriate methods to provide information and raise awareness about IA, academic performance, depression, and social anxiety. Moreover, the study’s findings may assist mental health professionals in developing and implementing specific intervention strategies to assist students with internet addiction.
Study setting, and period
The research was carried out at Jimma University in Southwest Ethiopia. Jimma town is located 351 km southwest of Addis Ababa, Ethiopia’s capital city. Jimma University is one of Ethiopia’s public higher education institutions, with four campuses: Jimma Institute of Technology (JIT Campus), College of Agriculture and Veterinary Medicine (CAVM Campus), College of Business and Economics (BECO Campus), and the main campus. The main campus was the study area, which included one institute and five colleges. These are institute of health science, college of natural and computational science, college of social science and humanity, college of business and economics, college of educational and behavioral science, and college of law and governance. During the study period, the main campus had a total of 5011 regular undergraduate students. The study was carried out between August 1 and August 30, 2021.
An institutional-based cross-sectional study design was used.
All regular undergraduate students at Jimma University’s main campus.
Regular undergraduate students in randomly selected departments at Jimma University’s main campus during the study period.
Students who were severely ill were excluded from the study.
Sample size determination and sampling method
The minimum sample size for this study was determined using a single population proportion formula with assumptions of a 95% confidence level of 1.96, a margin of error of d = 5%, and a proportion of internet addiction (p = 0.352) . The maximum sample size calculated for this study was 772 after incorporating a design effect of 2 and a 10% nonresponse rate. A multistage cluster sampling technique was used to recruit study participants, first among the colleges (college of natural and computational science, college of social science and humanities, college of business and economics, college of educational and behavioral science, college of law and governance, and one institute (institute of health). All colleges and the institute found in Jimma University’s main campus were involved. In the second stage, 13 departments (4 from the institute of health, 3 from the college of natural and computational science, 3 from the college of social science and humanities, 1 from the college of business and economics, 1 from the college of educational and behavioral science, and 1 from the college of law and governance) were selected proportionally using the lottery method. Then the students were selected proportionally from each selected department and year of study using the simple random sampling lottery method by taking a list of undergraduate students from each department. Finally, the selected students filled out the questionnaire.
Study variables and measurements
The Youngs internet addiction test (YIAT) was used for the collection of data regarding the components of internet addiction. With an overall alpha coefficient of 0.79, this scale has been widely used for screening and measuring the level of internet addiction among Nigerian university students . Internal consistency was high in the current study (Cronbach’s 0.885). Each YIAT item is evaluated on a scale of “rarely” to “always.“ It consists of 20 questions answered on a five-point Likert scale, ranging from “Does Not Apply” to “Always.“ For “Does Not Apply,“ “Occasionally,“ “Frequently,“ “Often,“ and “Always,“ the items were scored 0, 1, 2, 3, 4, and 5, respectively. Subjects would be classified as normal (0–30), mild (3–49), moderate (50–79), or severe (80–100) internet addicts based on their scores . Since there is no gold standard for differentiating between IA and non-IA, we considered those with mild, moderate, or severe Internet addiction to have Internet addiction. The existing literature fairly supports this classification of internet addiction [8, 17].
Depression was assessed by the patient health questionnaire (PHQ-9). The PHQ-9 questionnaire has been validated among Ethiopian patients with specificity and sensitivity of 67% and 86%, respectively, with a cut-off point of 10 or greater, and is used to screen for depression .
A self-reported version of the Liebowitz social anxiety scale was used to assess social anxiety (LSAS). It is a scale that evaluates fear or anxiety as well as social avoidance . It includes 11 items about social interaction and 13 items about public performance. Study participants were given 24 statements explaining various social situations (for example, “meeting strangers”) and asked to rate their level of fear on a 4-point scale ranging from 0 (none) to 3 (severe), as well as their level of avoidance in the previous week, ranging from 0 (never avoided this activity) to 3 (always avoided this activity) (usually avoided this activity).
Based on scoring subjects classified as having a moderate social phobia score of 50–65, a marked social phobia score of 65–80, a severe social phobia score of 80–95, and a very severe social phobia score of more than 95, a total score was calculated by adding up all of the fear and avoidance ratings . Oslo 3-item social support scale was used to assess social support .
The questionnaires used to assess socio-demographic and economic features, academic data, time-related elements, and the purposes of internet use were adopted from various works in previous literature.
Substance use was assessed by a modified version of the Alcohol, Smoking, and Substance Involvement Screening Tool (ASSIST). A worldwide group of substance abuse researchers developed the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) for the World Health Organization (WHO) to detect psychoactive substance use and related problems in primary care patients .
Furthermore, according to YIAT, a score of 0–30 is normal internet use. A score of 31–49 is mild internet addiction. A score of 50–79 is moderate internet addiction. A score of 80–100 is severe internet addiction .
According to the LSAS, a score of 50–65 is moderate social phobia. A score of 65–80 is marked by social phobia. A score of 80–95 is severe social phobia. A score greater than 95 is a very severe social phobia .
Academic performance: The JU grade reporting scale was used depending on students’ reports. Students last semester CGPA was taken to categorize students as follows: great distinction > 3.75 distinction 3.25–3.75, and promoted to 2.00–3.24.
Lifetime substance use students who had ever used substances in their lives Current substance use students who used substances in the past three months .
University students: students who are attending their education at Jimma University in the 2021 academic year.
Satisfaction with major study: perceived satisfaction with major study by students as poor, moderate, or good.
Data collection procedures
Four B.Sc. degree holders in psychiatry nursing collected the data and were supervised by two supervisors with M.Sc. degrees in integrated clinical and community mental health. In addition, data collectors and supervisors received intensive one-day training. The information was gathered through a self-administered technique using a well-structured and organized questionnaire.
Data quality control
To keep the data collection tool consistent, the questionnaire was written in English and translated into local languages (Afan Oromo and Amharic) by language experts. To weigh the acceptability and applicability of the procedures and tools, a pretest was administered to 5% of the total study participants, who were chosen at random from the College of Agriculture and Veterinary Medicine (JUCAVM) one week before data collection. After the pretest, grammatical errors, spelling errors, and ambiguous terms were corrected. Cronbach’s alpha was also determined for the current study. Data collectors were strictly supervised by supervisors throughout the data gathering process to guarantee the uniformity and completeness of the questionnaire. The collected data was reviewed and checked for missing data, completeness, and consistency before data entry by the principal investigators.
Data management and analysis
The data was coded and entered into the computer using Epi Data version 4.6 after being examined for accuracy and consistency, and then it was exported to the Statistical Package for Social Science (SPSS) version 25 for analysis. Simple descriptive statistics were employed for the study of the data that was collected. The association between each independent variable and the outcome variable was examined using bivariate analysis. In order to determine how each independent variable affected the outcome variables, only variables with a p-value of less than 0.25 in bivariate analysis were included in the multivariate logistic regression model. A p-value of less than 0.05 was finally deemed statistically significant, and the strength of the association was determined using an adjusted odds ratio with a 95.0% confidence interval (CI). The model’s goodness of fit was evaluated using the Hosmer-Leeshawn statistic and the omnibus test. In order to determine the correlation between independent variables, the multicollinearity test was also performed using the VIF, the standard error (SE), and the tolerance test.
After receiving ethical approval from Jimma University’s Institutional Review Board (IRB), the study was carried out. The participants were informed that the information would be used solely for research purposes and that their names would not be disclosed. The information was kept private and anonymous. Participants’ written consent was obtained after being informed of the study’s goal. For the purpose of assisting students with issues linked to Internet addiction, substance abuse, depression, and social anxiety, the principal investigator’s contact information and name were included on the questionnaire. Additionally, the upkeep of confidentiality and anonymity was guaranteed. During the data collection period, appropriate COVID-19 prevention methods were taken into account to protect data, collectors, and participants.
Socio-demographic characteristics of participants
Out of the total of 772 students, 745 returned the properly filled questionnaires, with a response rate of 96.5%.
Nearly two-thirds (484, 65%) of the respondents were male. The mean age of the study participants was 23.01, with a (SD ± 2.3) range of 18–29 years. Most of the respondents were Orthodox by religion (47.0%, n = 350). About three-fourths (76.9%, n = 573) were single. Regarding the residence area, the majority of the study participants (60%, n = 447) were from urban areas. The majority (88.7%, n = 661) of the respondents have a high average family monthly income (Table 1).
Nearly half of the participants (47.5%, n = 354) were from the college of health science. Regarding the year of the study, most of the students (40.4%, n = 301) were in their 3rd year. Half (50.9%, n = 379) of the study participants reported that they had good satisfaction with the major study. (Table 2)
Time related factors
Among the study respondents, more than half (57.7%, n = 430) had used the internet for more than 12 months. The majority (63%, n = 469) of the study participants had been using the internet for between 30 min and 1 h per day. Approximately one-fifth (38.5%, n = 287) of all respondents had used the internet at least once per day. The majority (63.8%, n = 475) of the respondents use Wi-Fi or wireless, while nearly one third (30.7%, n = 229) use mobile data as a common mode of internet access. (Table 3)
Purpose of internet use
Nearly all of the study participants (90.5%, n = 674) use the internet for Facebook. Similarly, most (90.3%, n = 673) of the respondents use the internet for academic purposes, while more than half (57%, n = 385) use it for entertainment. (Fig. 1)
Psychosocial related factors
From the total study participants, 43.5% (n = 324) were found to be depressed. Regarding social anxiety, only 3.2% (n = 24) of the participants had it. Regarding social support, 20.3% (n = 151) of the participants had strong social support, 40.5% (n = 302) had moderate social support, and 39.2% (n = 292) had poor social support. 18% (n = 91) of study participants chewed khat at least once in their lifetime, while 12.2% (n = 91) were currently using khat. Regarding alcohol drinking habits, 43.2% (n = 322) had drunk alcohol at least once in their lifetime, while 34.0% (n = 253) had drunk alcohol over the past 3 months. Of the total participants, 7.1% (n = 53) smoked cigarettes at least once in their lifetime, whereas 4.1% (n = 36) smoked cigarettes over the past 3 months. (Fig. 2)
Prevalence of internet addiction
The prevalence of internet addiction among study participants was 53.6% (n = 399). Of these, 36.9% (n = 275), 14.6% (n = 109), and 2.1% (n = 16) had mild, moderate, and severe internet addiction, respectively.
Factors associated with internet addiction
Bivariate analysis revealed that being in the sixth year of study, having a promoted grade report, dissatisfaction with major, purpose of internet use (Facebook, Imo, Telegram, entertainment), depression, social anxiety, poor social support, and lifetime khat use were significantly associated with internet addiction at a p-value of 0.25 and entered into a multivariate logistic regression model.
After adjusted analysis, the CGPA of the promoted grade report, poor satisfaction with the major study, purpose of internet use (entertainment, Facebook, Telegram), depression, social anxiety, and poor social support were found to be significantly associated with internet addiction. (Table 4)
In this study, the prevalence of Internet addiction among regular undergraduate students at Jimma University was 53.6%, with a 95% CI of 49.97–57.15, which falls within the prevalence rates reported in different study results across the world from similar study populations. The prevalence of internet addiction found in this study was in line with studies done in Malaysia (54%), and in the USA (57.2%) [25, 26].
This higher frequency could be due to unlimited and easy access to the internet, the introduction of smart phones, and the easy availability of Wi-Fi. However, the figure of the current study is much lower than the studies done in Ethiopia (85%), Nigeria (59%), Uganda (70.3%), Namibia (59.2%), and Saudi Arabia (67.5%) [8, 27,28,29].
This discrepancy might be due to the heterogeneity of the study samples. For instance, a study conducted in Malaysia involved only medical students, while the current study includes various degree programs besides medical students. Furthermore, it might be due to the difference in sociodemographic and economic characteristics and different internet-using habits among the study participants. Internet addiction and academic performance were found to have a strong association. Students who passed with promoted academic performance and who had poor satisfaction with major study were more likely to develop Internet addictions than students who passed with great distinction and who had good satisfaction with major study, respectively. Our findings were consistent with studies done in Sri Lanka, India, and Taiwan, which have said that poor satisfaction with academic performance is negatively associated with internet addiction [33,34,35].
The probable reason might be that students addicted to the internet spend excessive time on the internet, so they cannot spend enough quality time on their study subjects, which may cause problems with their schoolwork and lead to poor academic productivity. The other probable reason might be that since they participate in extra-curricular activities, they become degraded both physically and mentally, so they lose the ability to concentrate and focus on their studies.
Also, another explanation for this relationship might be that college students with lower learning satisfaction may spend time online seeking a sense of accomplishment.
The use of social media like Facebook and Telegram is statistically found to have a strong association with internet addiction. This finding is consistent with studies conducted at Bahir Dar University, Switzerland, and China, which have said that social networking sites have been found to be associated with internet addiction [7, 36, 37]. The reason for this association might be described by the fact that, with the emergency of social networking sites such as Facebook, overall SNS usage has accelerated .
The other probable reason might be because Facebook is a platform for freely and anonymously expressing opinions, which may increase the probability of becoming addicted to the internet . Also, it might be because students may rely on social media to satisfy their entertainment and information seeking needs and forget their problems.
In our findings, depression was associated with internet addiction. The findings of our study agree with a study conducted in Iran that showed the an association between depressive disorder and internet addiction . Our findings were also supported by a study done in Taiwan that suggested an association between depressive disorder and internet addiction . The reason for this association might be due to the fact that internet use results in a negative effect on psychological well-being, which may indicate that internet addiction might lead to depression . The other probable reason might be a self-medication model for substance use disorder that indicates adolescents with depression may adjust their emotional condition through internet use because it is perceived as less harmful than illegal substances .
Another reason might be Students with depressive symptoms may choose internet surfing as a coping strategy, which increases the risk of becoming addicted . The finding of the current study shows a significant association of internet addiction and social anxiety. Our study strongly supported by a study done in Spain, which revealed a significant association between internet addiction and social anxiety .
Furthermore, a study conducted in Pakistan and China discovered a significant link between internet addiction and social anxiety, which supports our findings [44, 45]. This association might be explained by the fact that people with social anxiety perceive that they are safer and more in control in an online social environment due to their inability to form meaningful connections with other people.
The other probable reason might be social phobia. It may occur due to addiction to the internet as they spend more time online and avoiding socializing due to problems faced by peers. So, the individual tends to turn to internet surfing to escape from their feelings and prefers to stay there longer than needed.
Also, it might be due to individuals with social anxiety who prefer individual activities to face-to-face activities and real-life relationships, and who may spend more time online and become addicted to the internet.
In the current study, there is a strong association between poor social support and internet addiction. The result of our finding agrees with a study done in Turkey, which showed internet addiction decreased as family and teacher support increased . Also, a study conducted in China found social support had a significant negative predictive effect on internet addiction, which can support our study .
This might be due to the fact that the social support students receive from family and teachers is critical during adolescence and young adulthood as it might protect them from looking for excessive and artificial social connectedness through compulsive internet surfing.
Strength of the study and limitations of study
It included a relatively large sample size and a sufficient response rate. The most important variables like depression, social anxiety, social support, and substance use were addressed, as were the limitations and recommendations of the previous study. This study had several limitations that should be considered when interpreting its findings. The data in this study were obtained only from a single public university, which may not represent all Universities found in Ethiopia, which includes both public and private Universities. Since the data was collected using self-administered questionnaires, measurement bias might have occurred. Additionally, since we have used YIAT to assess the status of internet use among students for over a month, recall bias might be occurred. Furthermore, the research design was cross-sectional, so no causal relationship could be examined.
This study’s results demonstrated a high and alarming prevalence of internet addiction among undergraduate students at Jimma University. Also, the study has shown the association of multiple factors with internet addiction among undergraduate university students. Having a promoted grade report, having poor satisfaction with a major study, the purpose of internet uses (entertainment, Facebook, and Telegram), having depression, having social anxiety, and having poor social support were found to be predictors of internet addiction.
The intervention should focus on identifying students with internet addiction, raising awareness of its negative effects, providing counseling services regarding the proper use of the internet, and encouraging responsible internet use.
Engaging students with more academic activities like group assignments, field visits, workshops, and other extracurricular activities (sport club, anti-drug club, art club, various funny programs) so that they may not spend unnecessary time on internet surfing.
The raw data analyzed during the current study will be made available from the corresponding author on reasonable request.
Greydanus DE, Greydanus MM. Internet use, misuse, and addiction in adolescents: current issues and challenges. Int J Adolesc Med Health. 2012;24(4):283–9.
Waithaka MW, Arts MOF, Science I, INTERNET USE AMONG UNIVERSITY, STUDENTS IN KENYA. : A CASE STUDY OF THE UNIVERSITY OF NAIROBI. 2013;(September).
Simcharoen S, Pinyopornpanish M, Haoprom P, Kuntawong P, Wongpakaran N, Wongpakaran T. Prevalence, associated factors and impact of loneliness and interpersonal problems on internet addiction: A study in Chiang Mai medical students. Asian J Psychiatr [Internet]. 2018;31(October 2017):2–7. https://doi.org/10.1016/j.ajp.2017.12.017.
Bener A, Bhugra D. Lifestyle and depressive risk factors associated with problematic internet use in adolescents in an Arabian Gulf culture. J Addict Med. 2013;7(4):236–42.
Torous J, Franzan J, O’Connor R, Mathew I, Keshavan M, Kitts R, et al. Psychiatry residents’ use of educational websites: a pilot survey study. Acad Psychiatry. 2015;39(6):630–3.
Cheng C, Li AYL. Internet addiction prevalence and quality of (real) life: a meta-analysis of 31 nations across seven world regions. Cyberpsychology Behav Soc Netw. 2014;17(12):755–60.
Asrese K, Muche H. Online activities as risk factors for internet addiction among students in Bahir Dar University, North West Ethiopia: Hierarchical regression model. 2019;1–20.
Zenebe Y, Kunno K, Mekonnen M, Bewuket A, Birkie M, Necho M et al. Prevalence and associated factors of internet addiction among undergraduate university students in Ethiopia: a community university-based cross-sectional study. BMC Psychol [Internet]. 2021;9(1):1–10. https://doi.org/10.1186/s40359-020-00508-z.
Liu X, Bao Z, Wang Z. Internet use and internet addiction disorder among medical students: a case from China. Asian Soc Sci. 2009;6(1):28–34.
Wellman B, Boase J, Chen W. The Networked Nature of Community. IT Soc [Internet]. 2002;1(1):151–65. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.5544&rep=rep1&type=pdf
Ko CH, Yen JY, Yen CF, Chen CS, Chen CC. The association between internet addiction and psychiatric disorder: a review of the literature. Eur Psychiatry. 2012;27(1):1–8.
Ye S, Cheng H, Zhai Z, Liu H. Relationship between social anxiety and internet addiction in Chinese College Students Controlling for the Effects of Physical Exercise, demographic, and academic variables. Front Psychol. 2021;12(August):1–9.
Krishnamurthy S, Chetlapalli S. 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(2):115.
Muche H, Asrese K. Conditions exposing students of Bahir Dar University to internet addiction, Bahir Dar, Ethiopia: a phenomenological study. Afr J Soc Work. 2020;10(2):51–60.
Akpunne BC, Akinnawo EO, Alakija OA, Kumuyi DO. Psychometric Properties of Young’s internet addiction test in Nigeria. Int J High Risk Behav Addict. 2020;9(4):1–7.
Abreu CN, De. Clin Assess Internet-Addicted Clients. 2011;19–34.
Tsimtsiou Z, Haidich AB, Spachos D, Kokkali S, Bamidis P, Dardavesis T, et al. Internet addiction in Greek Medical students: an online survey. Acad Psychiatry. 2015;39(3):300–4.
Derajew H, Tolessa D, Feyissa GT, Addisu F, Soboka M. Prevalence of depression and its associated factors among primary caregivers of patients with severe mental illness in southwest, Ethiopia. 2017;1–8.
Rytwinski NK, Fresco DM, Heimberg RG, Coles ME, Liebowitz MR, Cissell S, et al. Screening for social anxiety disorder with the self-report version of the liebowitz social anxiety scale. Depress Anxiety. 2009;26(1):34–8.
Russell G, Shaw S. A study to investigate the prevalence of social anxiety in a sample of higher education students in the United Kingdom. J Ment Heal. 2009;18(3):198–206.
Kocalevent RD, Berg L, Beutel ME, Hinz A, Zenger M, Härter M, et al. Social support in the general population: standardization of the Oslo social support scale (OSSS-3). BMC Psychol. 2018;6(1):4–11.
WHO ASSIST Working Group. The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST): development, reliability and feasibility. … Internet]. 2010;97:1–74. Available from: http://onlinelibrary.wiley.com/doi/https://doi.org/10.1111/j.1360-0443.2007.02114.x/full.
Gelaye B, Williams MA, Lemma S, Deyessa N, Bahretibeb Y, Shibre T et al. Validity of the patient health questionnaire-9 for depression screening and diagnosis in East Africa. Psychiatry Res [Internet]. 2013;210(2):653–61. https://doi.org/10.1016/j.psychres.2013.07.015.
Abiola T, Udofia O, Zakari M. Psychometric Properties of the 3-Item Oslo Social Support Scale among Clinical Students of Bayero University Kano, Nigeria. Malaysian J Psychiatry [Internet]. 2013;22(2):32–41. Available from: http://www.mjpsychiatry.org/index.php/mjp/article/view/264/194.
Fortson BL, Scotti JR, Chen YC, Malone J, Del Ben KS. Internet use, abuse, and dependence among students at a southeastern regional university. J Am Coll Heal. 2007;56(2):137–44.
Balhara YPS, Gupta R, Atilola O, Knez R, Mohorović T, Gajdhar W, et al. Problematic internet use and its correlates among students from three medical schools across three countries. Acad Psychiatry. 2015;39(6):634–8.
Hasan AAH, Jaber AA. Prevalence of internet addiction, its association with psychological distress, coping strategies among undergraduate students. Nurse Educ Today [Internet]. 2019;81(July):78–82. https://doi.org/10.1016/j.nedt.2019.07.004.
Nath R, Chen L, Muyingi HN, Lubega JT. Internet addiction in Africa: a study of namibian and Ugandan College Students. Int J Comput ICT Res. 2013;7(2):9–22.
Okwaraji F, Aguwa E, Onyebueke G, Shiweobi-Eze C. Assessment of Internet Addiction and Depression in a sample of nigerian University Undergraduates. Int Neuropsychiatr Dis J. 2015;4(3):114–22.
Tateno M, Kim DJ, Teo AR, Skokauskas N, Guerrero APS, Kato TA. Smartphone addiction in japanese college students: usefulness of the japanese version of the smartphone addiction scale as a screening tool for a new form of internet addiction. Psychiatry Investig. 2019;16(2):115–20.
Ching SM, Awang H, Ramachandran V, Mohd S, Lim S, Aliaa W et al. Prevalence and factors associated with internet addiction among medical students - a cross-sectional study in Malaysia. 2017;72(1):7–11.
Sahu R. Internet addiction among professional courses students: a study from central India INTERNET ADDICTION AMONG PROFESSIONAL COURSES STUDENTS .
Arbabisarjou A. The Association of Internet Addiction with Academic Achievement, Emotional Intelligence and Strategies to Prevention of them from Student’s Perspectives. Int J Humanit Cult Stud [Internet]. 2016;3(1):1646–56. Available from: http://www.ijhcs.com/index.php/ijhcs/article/view/1896.
Kumar S, Kumar A, Badiyani B, Singh SK, Gupta A, Ismail MB. Relationship of internet addiction with depression and academic performance in indian dental students. Clujul Med. 2018;91(3):300–6.
Sachitra V, Internet, Addiction, Academic Performance and University Students. Orig Res Artic J Glob Res Educ Soc Sci [Internet]. 2015;3(4):179–86. Available from: www.ikpress.org.
Kuss DJ, Griffiths MD. Online social networking and addiction — a review of the psychological literature. 2011;3528–52.
Leung L, Lee PSN. Impact of internet literacy, internet addiction symptoms, and internet activities on academic performance. Soc Sci Comput Rev. 2012;30(4):403–18.
Gholamian B, Shahnazi H, Hassanzadeh A. The prevalence of internet addiction and its association with depression, anxiety, and stress, among high-school students. Int J Pediatr. 2017;5(4):4763–70.
Yen JY, Ko CH, Yen CF, Wu HY, Yang MJ. The Comorbid Psychiatric symptoms of internet addiction: attention deficit and hyperactivity disorder (ADHD), Depression, Social Phobia, and hostility. J Adolesc Heal. 2007;41(1):93–8.
Internet Paradox Revisited. Wired Homestd. 2018.
Drunen EVAN, Vuzevski V, Jon PTVMDE, Luider TM. Zyxwvutsrqp Zyxwvuts Zyxwvutsrq Zyxwvutsrqp Zyx. 1993;401(1 993):397–401.
Bozkurt H, Coskun M, Ayaydin H, Adak I, Zoroglu SS. Prevalence and patterns of psychiatric disorders in referred adolescents with internet addiction. Psychiatry Clin Neurosci. 2013;67(5):352–9.
Bernal-Ruiz C, Rosa-Alcázar AI, González-Calatayud V, Rosa-Alcázar E. Is there a relationship between problematic internet use and responses of social anxiety, obsessive-compulsive and psychological well-being among adolescents? An Psicol. 2017;33(2):269–76.
Bilal M. University of Sargodha the relationship between internet addiction and anxiety among students of University of Sargodha. Int J Humanit Soc Sci [Internet]. 2018;4(May):288–93. Available from: www.ijhssnet.com.
Ren Y, Yang J, Liu L. Social anxiety and internet addiction among rural left-behind children: the mediating effect of loneliness. Iran J Public Health. 2017;46(12):1659–68.
Kiran-Esen B, Gündoğdu M. The relationship between internet addiction, peer pressure and perceived social support among adolescents. Educ Res Assoc Int J Educ Res. 2010;2(1):29–36.
Tang CSK, Wu AMS, Yan ECW, Ko JHC, Kwon JH, Yogo M, et al. Relative risks of internet-related addictions and mood disturbances among college students: a 7-country/region comparison. Public Health. 2018;165(December 2019):16–25.
Jimma University was funded for the data collection but not for publication so, we would like to thank Jimma University for funding this study. Also, our heartfelt appreciation goes to the study supervisors, data collectors, and participants for their valuable time.
Ethics approval and consent to participate
The study was conducted after approval obtained from the ethical review board (ERB) of Faculty of Medicine Institute of health sciences, Jimma University with the refence number of (IHP6Nd/366/. Informed, voluntary, written and signed consent was obtained from each participant after providing clear explanation about the objective of the study before distributing the questionnaire. The collected data was kept confidential. The study was carried out in accordance with the Declaration of Helsinki.
Consent for publication
Not applicable since the manuscript does not contain individual level data.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Amano, A., Ahmed, G., Nigussie, K. et al. Internet addiction and associated factors among undergraduate students of Jimma University; Jimma, South West Ethiopia, institutional based cross-sectional study. BMC Psychiatry 23, 721 (2023). https://doi.org/10.1186/s12888-023-05197-7