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Internet Addiction and Its Associated Factors among Undergraduate Students in Kathmandu, Nepal

Internet Addiction and Its Associated Factors among Undergraduate Students in Kathmandu, Nepal Hindawi Journal of Addiction Volume 2023, Article ID 8782527, 9 pages https://doi.org/10.1155/2023/8782527 Research Article Internet Addiction and Its Associated Factors among Undergraduate Students in Kathmandu, Nepal Sonu Acharya , Laxmi Adhikari , Santosh Khadka , Shishir Paudel , and Maheshor Kaphle Department of Public Health, CiST College, Kathmandu 44600, Nepal Correspondence should be addressed to Maheshor Kaphle; maheshkafe@yahoo.com Received 23 October 2022; Revised 13 February 2023; Accepted 28 March 2023; Published 8 April 2023 Academic Editor: Mirko Duradoni Copyright © 2023 Sonu Acharya et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Internet has penetrated all processes of life and has become an unavoidable part of people’s daily life. Tis widespread use of the Internet has resulted in signifcant concerns with regard to problematic Internet behaviours and related conditions. Te aim of the study was to fnd out the prevalence of Internet addiction and its associated factors among undergraduate students in Kathmandu. Materials and Methods. We included all together 344 undergraduate students from diferent colleges afliated to Pokhara University for this cross-sectional study. We used self-administered questionnaire consisting of the Internet Addiction Test scale to assess the Internet addiction. We coded the data, entered it in Epi-Data 3.1 and transferred to IBM SPSS 25 for analysis. We applied bivariate and multivariate logistic regression analysis to identify factors associated with Internet addiction, and p value <0.05 was considered as statistically signifcantt. Results. Te prevalence of Internet addiction was found to be 29.90% (95% CI: 25.0–34.9). In the chi-squared test relationship with parents, parental control over the Internet use, perceived feeling of loneliness, and time spent on internet per day were found to be statistically associated (p< 0.05) with Internet addiction. Conclusion. Tis study revealed nearly one-third of the Internet addiction among undergraduate students. Relationship with parents, parental control over the internet use, perceived loneliness feelings, and time spent on internet per day were signifcantly associated with Internet addiction along other factors. Terefore, it is important to raise awareness among young generation, parents, and teachers towards risk of Internet addiction. user’s is 4.1% (i.e., 196 million people). Average duration of 1. Introduction the internet use is 6 hours and 57 minutes per day on Internet is considered to be the most widely used media in connected activities. Younger people tend to spend more the world, and it varies from other types of media. It has time online than older generations do, with young women penetrated all process of life [1]. Internet invention has spending the greatest amount of time using the internet [4]. changed the various aspects of individual life such as in the Worldwide, the prevalence of internet addiction has way individuals entertain themselves and interact with each been estimated at 6%, considering that only about 39% of the other, with the infnite social networking sites [2]. Te world population has internet access. Tere seems to be number of Internet users has grown exponentially in the a signifcant variation in rates of internet addiction between world; the number of active internet users in the world far countries [5]. Te prevalence of severe problematic Internet exceeds 4 billion people [3]. use (PIU)/Internet addiction ranged from 0 to 47.4%, Globally, there are 5.32 billion (67% of global pop- whereas the prevalence of Internet overuse/possible Internet ulation) mobile users, 5.00 billion people (63% of global addiction ranged from 7.4% to 46.4% among students from population) using the internet and there are 4.65 billion Southeast Asia [6]. Te extreme use of internet showed the (58.7% of global population) social media users around the addictive behaviour of the internet use [7]. Physical im- world in April 2022. Te annual growth rate of internet pairments in the form of insomnia (26.8%), daytime 2 Journal of Addiction sleepiness (20%), and eye strain (19%) were reported among prevalence rate and margin of error at 5%, using Cochran’s users [6]. Internet users in Nepal increased by 822 thousand formula, the initial sample size was estimated at 312 which (+7.7 percent) between 2021 and 2022 [8]. was optimized to 343 adjusting the 10% nonresponse rate. Tis widespread use of the Internet has resulted in the Simple random sampling technique was used to choose signifcant concerns with regards to problematic Internet six colleges from the Kathmandu district using a lottery behaviours and related conditions [9]. Internet addiction method where a total of 344 students were enrolled at the (IA) is the lack of ability to control the Internet use and time of the study. Te required number of undergraduates involvement leading to progressive loss of control. With from each selected colleges were estimated based on the negative social efects, Internet addicts use the web as a social number of students enrolled in each college and its programs and communication tool, once they experience higher levels to ensure proper representation of the undergraduate stu- of pleasure and satisfaction when online than in real life [10]. dents. Finally, all the estimated number of students from It reduces social interaction to family, friends, and com- each college and program were approached by enumerating munity leading to mental health problems such as de- all the students present in the randomly selected class at the pression, anxiety, and other psychological problems [11, 12]. time of data collection. When the number of students in class Internet addiction symptoms express the users’ urge to was more than the estimated sample size, the surplus data continue being connected despite the desire to stop, expe- were also taken. Te ethical approval for research was taken riencing unpleasant emotions when they do not succeed, from IRC-CiST prior to the data collection and the approval sleep disturbance, angry or agitated reaction when forced to was taken from concerned colleges to conduct the research. disconnect, and loosing track of time while online [13]. Informed consent was taken from the participants with People having a high level of anxiety use social media more explanation of aim and nature of the study prior to data which tentatively increase the negative emotions and leads to collection. mental consequences [14, 15]. Internet is being an unavoidable part of day-to-day life 2.4.DataCollectionToolsandProcedures. Internet Addiction because the usage of the internet has been growing explo- Test (IAT) is a validated instrument to measure Internet sively worldwide. With the increase in the dependence of addiction [16]. Te Internet Addiction Test was primarily internet, people are gradually getting addicted towards it. developed by Dr. Kimberly Young, which is a 20-item 5-point Tere are only limited studies conducted in Nepal which Likert scale ranging from 0 to 5 (0 = less extreme behaviour to primarily focus on adolescents and medical students. Tis 5 = most extreme behaviour) that measures the severity of study focuses on undergraduate level students as this age self-reported compulsive use of the internet [17]. Te sum of group mostly uses internet for diferent purposes. Tey are the ratings was calculated for the 20-item responses for the particularly vulnerable to problematic internet use due to total IAT score. Te maximum IAT score is 100 points. IAT several factors such as the psychological and developmental scores were categorized as internet users who scored <50 were characteristics of late adolescence/young adulthood, ready considered average user and who scored ≥50 were considered access to the internet, and an expectation of computer/in- internet addicted. Te questionnaire consisted of four sections ternet use. Majority of internet users in Nepal are aged where the frst section consisted of sociodemographic in- 18–24 years, typical age group of undergraduate students [8]. formation, second section consist of behavioural factors re- So, this study helps to identify the prevalence of Internet lated to the internet use, third section was about the perceived addiction among undergraduate university students and to psychological status and interpersonal relationship, and the reveal factors associated with internet addiction in Kath- fnal section consisted of Young’s 20-item Internet Addiction mandu, Nepal. Test (IAT). Te questionnaire was pretested among 10% of the sample population prior to data collection. Data were col- 2. Materials and Methods lected by the help of a self-administered method. During the process of data collection, the nature of the study was 2.1.StudyDesign,Period,Setting,andPopulation. Tis cross- explained in detail to the participants; the details regarding the sectional study was conducted among the undergraduate duration of the study, informed consent, and confdentiality students of Kathmandu district of Nepal and data were concerns. Questionnaires were distributed to all the students collected during 30 June to 15 July of 2022. possessing inclusion criteria. 2.2. Inclusion and Exclusion Criteria. Undergraduate stu- 2.5. Data Quality Control Issues. One of the researchers was dents above the age of 18 years currently enrolled at any self-involved for data collection and supervised by the other course of Pokhara University inside Kathmandu district one. Each day at the end of data collection, every ques- were eligible to be included in this study and no exclusion tionnaire was checked, reviewed by the supervisor, and were made in terms of any attributes. organized for completeness and consistency. Pretesting of the questionnaire was carried out in one of the management 2.3. Sample Size Determinations, Sampling Techniques, and colleges afliated to Tribhuvan University in Kathmandu. Procedures. Te past study conducted among higher sec- Pretesting of the instrument was carried out in 10% of the sample. Cronbach’s alpha of IAT tools was found to be 0.799 ondary students in Kathmandu had noted the prevalence of internet addiction at 34.35% [16]. Considering this in this study. Journal of Addiction 3 Table 1: Sociodemographic profle of the participants. 2.6. Data Processing and Analysis. Data were entered in Epi- Data (version-3.1) and exported to Statistical Package on Characteristics n (%) Social Sciences (IBM SPSS) version 25. Te Pearson chi- Age squared test and binary logistic regression were applied to <20 years 100 (29.1) access the association between diferent independent variables ≥20 years 244 (70.9) and dependent variables (internet addiction) at 95% conf- Gender dence interval and 5% level of signifcance i.e., p value<0.05. Male 159 (46.2) Te unadjusted odds ratio (UOR) has also been reported along Female 185 (53.8) with the adjusted odds ratio (AOR) for those variables which Marital status were signifcant in bivariate analysis. For the multivariate Married 14 (4.1) analysis, the variance infation factor (VIF) test was performed Unmarried 330 (95.9) to check multicollinearity among the independent variables. Type of family Te Hosmer–Lemeshow test (the HL test) for goodness-of-ft Nuclear 218 (63.4) and Nagelkerke R square test were also performed for Joint/extended 126 (36.6) the model. Education status of fathers Illiterate 18 (5.2) 3. Results Literate 85 (24.7) Primary level 47 (13.7) A total of 400 questionnaires were distributed among the Secondary level 83 (24.1) Higher education 111 (32.3) undergraduate students of the selected colleges, of which 344 questionnaires were received covering complete response to Education status of mothers all the provided questions. Tus, the response rate of 86% Illiterate 35 (10.2) Literate 79 (23.0) was achieved. Primary level 77 (22.4) Secondary level 85 (24.7) 3.1. Prevalence of Internet Addiction. Out of total 344 stu- Higher education 68 (19.8) dents who provided their complete response, 164 (47.7%) Family economic status were found to had a mild level of Internet addiction under Low 17 (4.9) the IAT score of (31–49) whereas 96 (27.9%) were found to Middle 310 (90.1) had a moderate addiction level (score 50–79) and 7(2.0%) High 17 (4.9) were found to be severe addict (score 80–100). Te overall prevalence of possible addict/internet addict based on IAT had a lower self-esteem. Almost two-third 226 (65.7%) re- with a score of ≥50 was at 29.9% (95% CI: 25.0–34.9) ported having a wonderful relation with their parents while only few reported to have wonderful relation with their teachers and peers. More than half of the students i.e., 193 3.2. Sociodemographic Characteristics of Respondents. Te (56.1%) experienced loneliness sometimes in past one week mean age of the students who participated in the study was (Table 3). 20.68± 1.863 years while the minimum and maximum age of the participants ranged between 18 and 30 years. Tere was nearly equal participation of the female and male undergraduate 3.5. Factors Associated with Internet Addiction. In bivariate students as 53.8% female and 46.2% were male. Te majority analysis, no statistically signifcant relationship was observed were unmarried and reported to have a nuclear family (Table 1). between student’s sociodemographic characteristics such as age, gender, marital status, parental education, and eco- nomic status of the family and their internet addiction status 3.3. Internet Use-Related Characteristics of Respondents. (Table 4). In regards to the internet use pattern, a statistically With regards to the internet use pattern, most (208 i.e., signifcant relationship was observed between time spent by 60.5%) of the participants reported have started using in- the students on daily basis over internet and their internet ternet between 11 and 15 years of age. Likewise, more than addiction status. Similarly, use of internet for social net- half 201(58.4%) noted that they spent more than 5 hours per working and media was also found to be associated with day over the internet. More than two-third (72.1%) of the internet addiction at p< 0.05 (Table 5). students reported that their internet use is mostly for ed- In context of perceived psychological factors and in- ucational, recreational, and entertainment purposes, i.e., 256 terpersonal relationship of the students, nature of students, (74.4%). Similarly, half of the participants (50.9%) reported relationship with their parents, parental control over their that their social media as one of the major platforms for their internet use, and the perceived level of loneliness were found internet use while 39 (11.3%) reported to be engaged in to have a statistically signifcant relationship with their in- online jobs (Table 2). ternet addiction status at p< 0.05 (Table 6). For multivariate analysis, the variance infation factor 3.4. Perceived Psychological Status and Interpersonal Re- (VIF) test among the independent variables was performed where the highest reported VIF was 1.894 suggesting no issue lationship of Respondents. It was noted that nearly a quarter of the participants perceived themselves to be stressed and of multicollinearity. Students who spent fve hours or more on 4 Journal of Addiction Table 2: Internet use pattern. Table 3: Perceived psychological status and interpersonal relationship. Characteristics n (%) Characteristics n (%) Starting age of Internet use <10 years 50 (14.5) Perceived stress 11–15 years 208 (60.5) Presence 101 (29.4) >16 years 86 (25.0) Absence 243 (70.6) Time spent per day Perceived depression <5 hours 143 (41.6) Presence 17 (4.9) ≥5 hours 201 (58.4) Absence 327 (95.1) Major use of Internet for education/recreation Perceived self-esteem Yes 248 (72.1) Low 101 (29.4) No 96 (27.9) High 243 (70.6) Major use of Internet for entertainment/refreshment Relationship with parents Yes 256 (74.4) Wonderful 226 (65.7) No 88 (25.6) Good 65 (18.9) Satisfactory 53 (15.4) Major use of Internet for Internet gaming Yes 129 (37.5) Parental control over Internet use No 215 (62.5) Not at all 85 (24.7) Sometimes 187 (54.4) Major use of Internet for social networking Often/almost always 72 (20.9) Yes 175 (50.9) No 169 (49.1) Relationship with teachers Wonderful 108 (31.4) Major use of Internet for online jobs Good 139 (40.4) Yes 39 (11.3) Satisfactory 97 (28.2) No 305 (88.7) Relationship with peer Major use of Internet to watch pornography Wonderful 110 (32.0) Yes 41 (11.9) Good 154 (44.8) No 303 (88.1) Satisfactory 80 (23.3) Perceived level of self-control the internet were found to be almost twice more at odds (AOR: Not at all 44 (12.8) 1.780, 95% CI: 1.052–3.012) of experience internet addiction as Sometimes 177 (51.5) compared to those who used internet for less than 5 hours Often/almost always 123 (35.8) a day. Similarly, higher odds of internet addiction were ob- Perceived loneliness served among the students who had good (AOR: 1.957, 95% Not at all 52 (15.1) CI: 1.022–3.745) and satisfactory (AOR: 2.832, 95% CI: Sometimes 193 (56.1) 1.354–5.614) relation with their parents as compared to those Often/almost always 99 (28.8) who had wonderful relation. Likewise, students who reported their parents have higher control over their internet use were A study conducted among medical students in Nepal found to be thrice more at odds (AOR: 3.643, 95% CI: found low prevalence of Internet addiction than this study 1.687–7.863) of internet addiction as compared to students where out of 100 students, 21 students were found to be whose parents do not control their internet use. Students slightly addicted to using the Internet [22]. In a study, experiencing loneliness most of the time were also found to be among adolescents in a peri-urban setting in Nepal, 21.5% of have three-folds increase in their odds (AOR: 3.105, 95% CI: the participants were identifed with borderline Internet 1.264–7.629) of internet addiction in comparison to those who addiction and 13.3% with possible internet addiction [23]. A reported not experiencing loneliness (Table 7). study among adolescent Turkish students prevalence of IA was 17.7%. 4. Discussion A study conducted among undergraduate students in It was seen that the prevalence of internet addiction among Nepal revealed 35.4% prevalence of Internet addiction [24] and a study among higher secondary level students in this study group was 29.90% which was supported by a meta- analysis conducted in prevalence of Internet addiction in Kathmandu district revealed possible addicts/Internet ad- dicts to be 34.35% [16] which is slightly higher than medical students in diferent countries, the prevalence of IA was 30.1%, [18]. Furthermore, a study conducted among this study. undergraduate university students in Ethiopia was found to Te possible reason for variations in prevalence of In- be 29.4% moderate to severe IA [19]. Another study con- ternet addiction across diferent country or even in the study ducted among young adults in Bangladesh revealed prev- conducted in same country might be because of sample size, alence of Internet addiction was 27.1%, [20], and online sampling procedure, diference in social context and survey of problematic Internet use (PIU) and its correlates background of the participants, purpose of the Internet use, among undergraduate medical students of Nepal found the knowledge about the problems of Internet addiction, or prevalence of PIU to be 31.9% [21]. aware about the proper use of Internet. Journal of Addiction 5 Table 4: Internet addiction and its association with sociodemo- Table 5: Internet addiction and its association with the Internet use graphic characteristics. pattern. Internet addiction Internet addiction 2 2 Variables x Variables x p value Average user n Possible Possible value Average user n (%) (%) addict n (%) addict n (%) Age Starting age of Internet use <20 77 (77.0) 23 (23.0) <10 years 32 (64.0) 18 (36.0) 3.239 0.072 ≥20 164 (67.2) 80 (32.8) 11–15 years 148 (71.2) 60 (28.8) 1.025 0.599 >16 years 61 (70.9) 25 (29.1) Gender Male 109 (68.6) 50 (31.4) Time spent per day over Internet 0.319 0.572 Female 132 (71.4) 53 (28.6) <5 hours 111 (77.6) 32 (24.4) 6.676 0.010 ≥5 hours 130 (62.2) 71 (35.3) Marital Status Married 12 (85.7) 2 (14.3) Major use of Internet for education/recreation 1.75 0.192 Unmarried 229 (69.4) 101 (30.6) Yes 176 (71.0) 72 (29.0) 0.612 0.434 No 65 (67.7) 31 (32.3) Type of family Nuclear 146 (67.0) 72 (33.0) Major use of Internet for entertainment/refreshment 2.701 0.100 Yes 176 (68.8) 80 (31.3) Joint/extended 95 (75.4) 31 (24.6) 0.907 0.341 No 65 (73.9) 23 (26.1) Fathers’ education status Higher Major use of Internet for Internet gaming 112 (74.7) 38 (25.3) education 2.693 0.101 Yes 86 (66.7) 43 (33.3) 1.132 0.287 Basic education 129 (66.5) 65 (33.5) No 155 (72.1) 60 (27.9) Mothers’ education status Major use of Internet for social networking Higher Yes 128 (75.7) 41 (24.3) 135 (70.7) 56 (29.3) 5.112 0.024 education 0.079 0.778 No 113 (64.6) 62 (35.4) Basic education 106 (69.3) 47 (30.7) Major use of Internet for online jobs Family economic status Yes 26 (66.7) 13 (33.3) 0.271 0.603 Low 13 (76.5) 4 (23.5) No 215 (70.5) 90 (29.5) Middle 213 (68.7) 97 (31.3) 3.280 0.194 Major use of Internet to watch pornography High 15 (88.2) 2 (11.8) Yes 27 (65.9) 14 (34.1) 0.392 0.531 No 214 (70.6) 89 (29.4) Statistical signifcance at p< 0.05. Te association between time spent on Internet per day and IA was found to be statistically signifcant (p value 0.010). Tis association is supported by various studies such Majority of the participants used internet for enter- as a study conducted in Nepal [16], Ethiopia [19], Bangla- tainment and refreshment purpose followed by education or desh [20], and Bengaluru, India [25]. As the dependence on to get new information. Use of Internet for social networking Internet increases, individuals spent more time on Internet. purpose showed signifcant association with IA (p value Relationship with parents and IA have signifcant associa- 0.024). Tis fnding is lined with the study conducted in Northern Tanzania [40] and Saudi Arabia [41]. Most tion (p < 0.001) which is also indicated by several studies [16, 26–30]. Internet addiction was found to be higher commonly used app/website was found to be social media among those who had satisfactory relationships within among the participants supported by the study among young family. Self-control was negatively linked to Internet ad- adults in Bangladesh [20]. diction as prevalence of Internet addiction was found to be Te restrictive parenting approach was found to be higher in individuals with poor self-control than individuals signifcantly associated with IA (p < 0.001) similar to the with good self-control [30–32]. Te loneliness of the par- fnding of the study conducted among adolescents in Hong ticipants was found to be signifcantly associated with In- Kong, the greater the number of rules and the stricter the ternet addiction (p < 0.001) similar with the various studies enforcement of rules concerning the Internet use, the more likely it is that adolescents will become addictive users [29]. [6, 33, 34]. Lonely individuals prefer to increase their communication through social networks to meet their 4.1. Strength and Limitations. Te target population of this emotional needs that was included in the meta-analysis conducted in Iran [35]. Perceived stress did not show any study was undergraduate university students of Nepal, signifcant association with Internet addiction which is which group is highly vulnerable to Internet addiction and found to be consistent with the study conducted in East majority of Internet users are also of this group. Tis study Malaysia [36] and the study conducted among Chinese included a considerably high number of socio-demographic adolescents [37]. However some previous studies reported variables and also the Internet use and behavioural related signifcant association between perceived self-esteem and and family and college related variables. internet addiction, [38, 39] but we did not fnd the asso- Te fnding of the study is based on the primary in- ciation i.e., low self-esteem increased susceptibility to IA. formation collected using the standard tool by the active involvement of the researchers. Since this is a cross-sectional Terefore, more studies are needed to understand the sig- nifcant association of self-esteem and IA. study conducted with minimal required sample size, it could 6 Journal of Addiction Table 6: Internet addiction and its association with the perceived psychological status and interpersonal relationship. Internet addiction Variables x p value Possible Average user n (%) addict n (%) Perceived stress Presence 75 (74.2) 26 (25.8) 1.202 0.273 Absence 166 (68.3) 77 (31.7) Perceived depression Presence 9 (52.9) 8 (47.1) 2.498 0.114 Absence 232 (70.9) 95 (29.1) Perceived self-esteem Low 71 (70.3) 30 (29.7) 0.114 0.736 High 170 (70.0) 73 (30.0) Relationship with parents Wonderful 175 (77.4) 51 (22.6) ∗∗ Good 40 (61.5) 25 (38.5) 19.254 <0.001 Satisfactory 26 (49.1) 27 (50.9) Parental control over Internet use Not at all 69 (81.2) 16 (18.8) ∗∗ Sometimes 134 (71.7) 53 (28.3) 15.487 <0.001 Often/almost always 38 (52.8) 34 (47.2) Relationship with teachers Wonderful 82 (75.9) 26 (24.1) Good 100 (71.9) 39 (28.1) 5.950 0.054 Satisfactory 55 (59.1) 38 (39.2) Relationship with peer Wonderful 81 (73.6) 29 (26.4) Good 109 (70.8) 45 (29.2) 2.227 0.328 Satisfactory 51 (63.8) 29 (36.3) Perceived level of self-control Not at all 30 (68.2) 14 (31.8) Sometimes 121 (68.4) 56 (31.6) 1.072 0.585 Often/almost always 90 (73.2) 33 (26.8) Perceived loneliness Not at all 43 (82.7) 9 (17.3) ∗∗ Sometimes 143 (74.1) 50 (25.9) 15.381 <0.001 Often/almost always 55 (55.6) 44 (44.4) ∗∗ Statistical signifcance at p< 0.001. Table 7: Predictors of Internet addiction. Bivariate logistic regression Multivariate logistic regression Variables UOR 95% CI AOR 95% CI Time spent per day over Internet <5 hours Ref Ref ∗ ∗ ≥5 hours 1.894 1.163–3.087 1.780 1.052–3.012 Major use of Internet for social networking Yes 1.713 1.072–2.737 1.640 0.987–2.724 No Ref Ref Relationship with parents Wonderful Ref Ref ∗ ∗ Good 2.145 1.190–3.865 1.957 1.022–3.745 ∗ ∗ Satisfactory 3.563 1.912–6.639 2.832 1.354–5.614 Parental control over Internet use Not at all Ref Ref Sometimes 1.706 0.908–3.203 1.636 0.838–3.185 ∗ ∗ Often/almost always 3.859 1.889–7.880 3.643 1.687–7.863 Perceived loneliness Not at all Ref Sometimes 1.671 0.760–3.671 1.667 0.715–3.886 ∗ ∗ Often/almost always 3.822 1.682–8.683 3.105 1.264–7.629 Statistical signifcance at p< 0.05, Nagelkerke R square: 0.185, Hosmer and Lemeshow chi-square: 5.015, p � 0.756. Journal of Addiction 7 not cover all disciplines and universities hence insufcient to IQR: Interquartile range conclude that Internet addiction is high among un- IRC-CiST: Institutional Review Committee CiST dergraduate students in Nepal. So, further studies using larger PIU: Problematic Internet use sample size is necessary for fnding the causal associations on UOR: Unadjusted odds ratio Internet addiction among undergraduate students in Nepal. VIF: Variance infation factor. Data Availability 4.2. Implications of the Study. Problematic use of Internet/ Internet addiction can result in the various health problems Te data used to support the fndings of this study are and many factors are associated with it. Tis study identifed available from the corresponding author upon request. the purpose of the Internet use, level of Internet addiction, and factors associated with Internet addiction. So, by identifying Ethical Approval the factors associated with Internet addiction and strength of association between them, possible intervention action can be Ethical approval for research was taken from IRC-CiST (REF taken and will ultimately help to reduce the various health and NO IRC 190/078/079) prior to the data collection, and the educational problems, social life, and family relationship re- approval was also taken from concerned colleges to conduct lated problems arising due to excessive use of Internet. the research. 5. Conclusions and Policy Recommendations Consent 5.1. Conclusion. Prevalence of possible addiction of In- Informed consent was taken from the participants with ternet use was about one-third among undergraduate explanation of aim and nature of the study. Participants were students. Tere are numbers of factors which play a sig- not forced to participate in the research and were allowed to nifcant role to point out the alarming situation such as withdraw from the study. time spent on Internet per day, relationship with parents, parental control over Internet use, and loneliness feelings Conflicts of Interest were found to be statistically associated with Internet addiction. Major purpose of Internet use was found to be Te authors declare that they have no conficts of interest regarding the publication of this article. entertainment and refreshment, and use of internet for social networking showed signifcant association with IA. Authors’ Contributions Most commonly used app/website was found to be social media among the participants. SA generated concept, developed proposal, collected data, As family relationship and a restrictive parenting ap- and prepared preliminary manuscript. LA contributed to proach were found to be signifcantly associated, family- data entry, cleaning, and result preparation and supported based prevention strategies need to be developed and applied manuscript preparation. SK supported for proposal fnal- to achieve healthy family interactions through improving ization, supervision on data collection and quality control, parents-child communication and strengthening family and manuscript writing. SP coordinated with college prior to functionality rather than directly restricting the Internet use. data collection, analysed data, and wrote the manuscript. Further studies should be conducted in the consequences of MK fnalized the proposal, analysed data, and prepared and problematic internet use in mental health. reviewed manuscript. All authors have read and fnalized the manuscript. 5.2. Policy Recommendations. Family-based prevention strategies need to be developed and practice to achieve Acknowledgments healthy family interactions through improving parents-child Te authors want to acknowledge all persons who helped communication and strengthening family functionality them directly and indirectly during research. We want to rather than directly restricting the Internet use. Education acknowledge all colleges which gave them authority to programs need to be carried out on regular basis regarding collect data and all respondents for providing the data the addictive behaviour and coping strategies also involving during data collection. education and sensitization of the students as well as teachers. References Abbreviations [1] R. Ali, N. Mohammed, and H. Aly, “Internet addiction among medical students of Sohag University, Egypt,” Journal of the AOR: Adjusted odds ratio Egyptian Public Health Association, vol. 92, no. 2, pp. 86–95, CI: Confdence interval HT test: Hosmer–Lemeshow test [2] A. M. Al Mukhaini, F. A. Al Houqani, and R. M. Al Kindi, IA: Internet addiction “Internet addiction and depression among postgraduate IAI: Internet Addiction Index residents: a cross-sectional survey,” Sultan Qaboos University IAT: Internet Addiction Test Medical Journal, vol. 21, no. 3, pp. 408–415, 2021. 8 Journal of Addiction [3] E. Ramon-Arbues, J. M. Granada-Lopez, B. Martinez-Abadia, university students in Ethiopia: a community university- E. Echaniz-Serrano, I. Anton-Solanas, and M. Nash, “Prev- basedcross-sectional study,” BMC Psychol, vol. 9, no. 1, 2021. alence and factors associated with problematic internet use in [20] T. Hassan, M. M. Alam, A. Wahab, and M. D. Hawlader, a population of Spanish university students,” International “Prevalence and associated factors of internet addiction Journal of Environmental Research and Public Health, vol. 18, among young adults in Bangladesh,” Journal of the Egyptian no. 14, p. 7620, 2021. Public Health Association, vol. 95, no. 3, p. 8, 2020. [4] S. Kemp, Global Statshot Report, Datareportal, Vancouver, [21] P. Sharma, R. Shakya, S. Singh, and Y. P. S. Balhara, “An Canada, 2022. online survey of problematic internet use and its correlates [5] M. Hull and D. A. Proulx, Internet addiction facts and sta- among undergraduate medical students of Nepal,” Neurology tistics, Te Recovery Village, Umatilla, FL, USA, 2022. Psychiatry and Brain Research, vol. 37, pp. 95–99, 2020. [6] Y. S. Balhara, A. Mahapatra, P. Sharma, and R. Bhargava, [22] N. Upadhayay and S. Guragain, “Internet use and its addiction “Problematic internet use among students in South-East Asia: level in medical students,” Advances in Medical Education and current state of evidence,” Indian Journal of Public Health, Practice, vol. 8, pp. 641–647, 2017. vol. 62, no. 3, pp. 197–210, 2018. [23] K. Karki, D. R. Singh, D. Maharjan, Sushmitha, S. Shrestha, [7] H. Khazaie, J. Y. Lebni, J. Abbas et al., “Internet addiction and D. K. Tapa, “Internet addiction and sleep quality among status and related factors among medical students: a cross- adolescents in a peri-urban setting in Nepal: a cross- sectional study in western iran,” International Quarterly of sectionalschool-based survey,” PLoS One, vol. 16, no. 2, Ar- Community Health Education, vol. 15, ticle ID e0246940, 2021. p. 0272684X211025438, 2021. [24] P. M. Bhandari, D. Neupane, S. Rijal, K. Tapa, S. R. Mishra, [8] S. Kemp, Digital 2022: NepalDatareportal, Vancouver, Can- and A. K. Poudyal, “Sleep quality, internet addiction and ada, 2022. depressive symptoms among undergraduate students in [9] D. X. Y. Chia, C. W. L. Ng, G. Kandasami et al., “Prevalence of Nepal,” BMC Psychiatry, vol. 17, no. 1, p. 106, 2017. internet addiction and gaming disorders in Southeast Asia: [25] S. Krishnamurthy and S. K. Chetlapalli, “Internet addiction: a meta-analysis,” International Journal of Environmental prevalence and risk factors: a cross-sectional study among Research and Public Health, vol. 17, no. 7, Article ID 2582, college students in Bengaluru, the Silicon Valley of India,” Indian Journal of Public Health, vol. 59, no. 2, pp. 115–121, [10] A. Stravogiannis, “Internet addiction: a case report,” Revista Brasileira de Psiquiatria, vol. 31, no. 1, pp. 78-79, 2009. [26] K. Ahmadi, “Internet addiction among Iranian adolescents: [11] M. Aqeel, T. Rehna, K. H. Shuja, and J. Abbas, “Comparison a nationwide study,” Acta Medica Iranica, vol. 52, no. 6, of students’ mental wellbeing, anxiety, depression, and quality pp. 467–472, 2014. of life during COVID-19’s full and partial (smart) lockdowns: [27] E. Marzilli, L. Cerniglia, G. Ballarotto, and S. Cimino, “In- a follow-up study at a 5-month interval,” Frontiers in Psy- ternet addiction among young adult university students: the chiatry, vol. 13, Article ID 835585, 2022. [12] J. Abbas, M. Aqeel, J. Abbas et al., “Te moderating role of complex interplay between family functioning, impulsivity, social support for marital adjustment, depression, anxiety, depression, and anxiety,” International Journal of Environ- and stress: evidence from Pakistani working and nonworking mental Research and Public Health, vol. 17, no. 21, p. 8231, women,” Journal of Afective Disorders, vol. 244, pp. 231–238, [28] O. Senormanci, G. Senormanci, O. Guclu, and R. Konkan, [13] M. Peris, U. de la Barrera, K. Schoeps, and I. Montoya- “Attachment and family functioning in patients with internet Castilla, “Psychological risk factors that predict social net- addiction,” General Hospital Psychiatry, vol. 36, no. 2, working and internet addiction in adolescents,” International pp. 203–207, 2014. Journal of Environmental Research and Public Health, vol. 17, [29] C. S. T. Wu, H. T. Wong, K. F. Yu et al., “Parenting ap- no. 12, Article ID 4598, 2020. proaches, family functionality, and internet addiction among [14] J. Abbas, D. Wang, Z. Su, and A. Ziapour, “Te role of social Hong Kong adolescents,” BMC Pediatrics, vol. 16, no. 1, p. 130, media in the advent of covid-19 pandemic: crisis manage- ment, mental health challenges and implications,” Risk [30] A. Akın, S. Arslan, N. Arslan, R. Uysal, and U. Sahranç, “Self- Management and Health Care Policy, vol. 14, pp. 1917–1932, control management and internet addiction,” International Online Journal of Educational Sciences, vol. 7, no. 3, pp. 95– [15] J. Abbas, J. Aman, M. Nurunnabi, and S. Bano, “Te impact of 100, 2015. social media on learning behavior for sustainable education: [31] C. Li, J. Dang, X. Zhang, Q. Zhang, and J. Guo, “Internet evidence of students from selected universities in Pakistan,” addiction among Chinese adolescents: the efect of parental Sustainability, vol. 11, no. 6, p. 1683, 2019. behavior and self-control,” Computers in Human Behavior, [16] I. Karmacharya, K. Bhujel, D. Kumar Yadav, and K. Subedi, vol. 41, pp. 1–7, 2014. “Prevalence of internet addiction among higher secondary [32] S. Li, P. Ren, M. M. Chiu, C. Wang, and H. Lei, “Te re- level students in Kathmandu district,” Journal of Health and lationship between self-control and internet addiction among Allied Sciences, vol. 7, no. 1, pp. 40–46, 2019. students: a meta-analysis,” Frontiers in Psychology, vol. 12, [17] H. Cash, C. D Rae, A. H Steel, and A. Winkler, “Internet Article ID 735755, 2021. addiction: a brief summary of research and practice,” Current [33] E. Khatcherian, D. Zullino, D. De Leo, and S. Achab, “Feelings Psychiatry Reviews, vol. 8, no. 4, pp. 292–298, 2012. of loneliness: understanding the risk of suicidal ideation in [18] M. W. B. Zhang, R. B. C. Lim, C. Lee, and R. C. M. Ho, adolescents with internet addiction. A theoretical model to “Prevalence of internet addiction in medical students: a meta- answer to a systematic literature review, without results,” analysis,” Academic Psychiatry, vol. 42, no. 1, pp. 88–93, 2018. [19] Y. Zenebe, K. Kunno, M. Mekonnen et al., “Prevalence and International Journal of Environmental Research and Public associated factors of internet addiction among undergraduate Health, vol. 19, no. 4, p. 2012, 2022. Journal of Addiction 9 [34] Y. R. Cho and H. J. Lee, “A study on a model for internet addiction of adolescents,” Taehan Kanho Hakhoe Chi, vol. 34, no. 3, pp. 541–551, 2004. [35] H. Mozafar Saadati, H. Mirzaei, B. Okhovat, and F. Khodamoradi, “Association between internet addiction and loneliness across the world: a meta-analysis and systematic review,” SSM Population Health, vol. 16, Article ID 100948, [36] W. L. Cheah, M. S. Bo, L. H. Yee et al., “Internet addiction in preuniversity students in East Malaysia: prevalence and its association with stress and depression,” Malaysian Journal of Psychiatry, vol. 31, no. 1, pp. 13–18, 2022. [37] J. Tang, Y. Yu, Y. Du, Y. Ma, D. Zhang, and J. Wang, “Prevalence of internet addiction and its association with stressful life events and psychological symptoms among ad- olescent internet users,” Addictive Behaviors, vol. 39, no. 3, pp. 744–747, 2014. [38] S. A. Bahrainian, K. H. Alizadeh, M. R. Raeisoon, O. H. Gorji, and A. Khazaee, “Relationship of Internet addiction with self- esteem and depression in university students,” Journal of Preventive Medicine and Hygiene, vol. 55, no. 3, pp. 86–89, [39] L. Naseri, J. Mohamadi, K. Sayehmiri, and Y. Azizpoor, “Perceived social support, self-esteem, and internet addiction among students of Al-zahra university, tehran, Iran,” Iranian Journal of Psychiatry and Behavioral Sciences, vol. 9, no. 3, Article ID e421, 2015. [40] I. B. Mboya, B. J. Leyaro, A. Kongo, C. Mkombe, E. Kyando, and J. George, “Internet addiction and associated factors among medical and allied health sciences students in northern Tanzania: a cross-sectional study,” BMC Psychol, vol. 8, no. 1, p. 73, 2020. [41] D. M. Abdel-Salam, H. I. Alrowaili, H. K. Albedaiwi, A. I. Alessa, and H. A. Alfayyadh, “Prevalence of Internet addiction and its associated factors among female students at Jouf University, Saudi Arabia,” Journal of the Egyptian Public Health Association, vol. 94, no. 1, p. 12, 2019. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Addiction Hindawi Publishing Corporation

Internet Addiction and Its Associated Factors among Undergraduate Students in Kathmandu, Nepal

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Hindawi Journal of Addiction Volume 2023, Article ID 8782527, 9 pages https://doi.org/10.1155/2023/8782527 Research Article Internet Addiction and Its Associated Factors among Undergraduate Students in Kathmandu, Nepal Sonu Acharya , Laxmi Adhikari , Santosh Khadka , Shishir Paudel , and Maheshor Kaphle Department of Public Health, CiST College, Kathmandu 44600, Nepal Correspondence should be addressed to Maheshor Kaphle; maheshkafe@yahoo.com Received 23 October 2022; Revised 13 February 2023; Accepted 28 March 2023; Published 8 April 2023 Academic Editor: Mirko Duradoni Copyright © 2023 Sonu Acharya et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Internet has penetrated all processes of life and has become an unavoidable part of people’s daily life. Tis widespread use of the Internet has resulted in signifcant concerns with regard to problematic Internet behaviours and related conditions. Te aim of the study was to fnd out the prevalence of Internet addiction and its associated factors among undergraduate students in Kathmandu. Materials and Methods. We included all together 344 undergraduate students from diferent colleges afliated to Pokhara University for this cross-sectional study. We used self-administered questionnaire consisting of the Internet Addiction Test scale to assess the Internet addiction. We coded the data, entered it in Epi-Data 3.1 and transferred to IBM SPSS 25 for analysis. We applied bivariate and multivariate logistic regression analysis to identify factors associated with Internet addiction, and p value <0.05 was considered as statistically signifcantt. Results. Te prevalence of Internet addiction was found to be 29.90% (95% CI: 25.0–34.9). In the chi-squared test relationship with parents, parental control over the Internet use, perceived feeling of loneliness, and time spent on internet per day were found to be statistically associated (p< 0.05) with Internet addiction. Conclusion. Tis study revealed nearly one-third of the Internet addiction among undergraduate students. Relationship with parents, parental control over the internet use, perceived loneliness feelings, and time spent on internet per day were signifcantly associated with Internet addiction along other factors. Terefore, it is important to raise awareness among young generation, parents, and teachers towards risk of Internet addiction. user’s is 4.1% (i.e., 196 million people). Average duration of 1. Introduction the internet use is 6 hours and 57 minutes per day on Internet is considered to be the most widely used media in connected activities. Younger people tend to spend more the world, and it varies from other types of media. It has time online than older generations do, with young women penetrated all process of life [1]. Internet invention has spending the greatest amount of time using the internet [4]. changed the various aspects of individual life such as in the Worldwide, the prevalence of internet addiction has way individuals entertain themselves and interact with each been estimated at 6%, considering that only about 39% of the other, with the infnite social networking sites [2]. Te world population has internet access. Tere seems to be number of Internet users has grown exponentially in the a signifcant variation in rates of internet addiction between world; the number of active internet users in the world far countries [5]. Te prevalence of severe problematic Internet exceeds 4 billion people [3]. use (PIU)/Internet addiction ranged from 0 to 47.4%, Globally, there are 5.32 billion (67% of global pop- whereas the prevalence of Internet overuse/possible Internet ulation) mobile users, 5.00 billion people (63% of global addiction ranged from 7.4% to 46.4% among students from population) using the internet and there are 4.65 billion Southeast Asia [6]. Te extreme use of internet showed the (58.7% of global population) social media users around the addictive behaviour of the internet use [7]. Physical im- world in April 2022. Te annual growth rate of internet pairments in the form of insomnia (26.8%), daytime 2 Journal of Addiction sleepiness (20%), and eye strain (19%) were reported among prevalence rate and margin of error at 5%, using Cochran’s users [6]. Internet users in Nepal increased by 822 thousand formula, the initial sample size was estimated at 312 which (+7.7 percent) between 2021 and 2022 [8]. was optimized to 343 adjusting the 10% nonresponse rate. Tis widespread use of the Internet has resulted in the Simple random sampling technique was used to choose signifcant concerns with regards to problematic Internet six colleges from the Kathmandu district using a lottery behaviours and related conditions [9]. Internet addiction method where a total of 344 students were enrolled at the (IA) is the lack of ability to control the Internet use and time of the study. Te required number of undergraduates involvement leading to progressive loss of control. With from each selected colleges were estimated based on the negative social efects, Internet addicts use the web as a social number of students enrolled in each college and its programs and communication tool, once they experience higher levels to ensure proper representation of the undergraduate stu- of pleasure and satisfaction when online than in real life [10]. dents. Finally, all the estimated number of students from It reduces social interaction to family, friends, and com- each college and program were approached by enumerating munity leading to mental health problems such as de- all the students present in the randomly selected class at the pression, anxiety, and other psychological problems [11, 12]. time of data collection. When the number of students in class Internet addiction symptoms express the users’ urge to was more than the estimated sample size, the surplus data continue being connected despite the desire to stop, expe- were also taken. Te ethical approval for research was taken riencing unpleasant emotions when they do not succeed, from IRC-CiST prior to the data collection and the approval sleep disturbance, angry or agitated reaction when forced to was taken from concerned colleges to conduct the research. disconnect, and loosing track of time while online [13]. Informed consent was taken from the participants with People having a high level of anxiety use social media more explanation of aim and nature of the study prior to data which tentatively increase the negative emotions and leads to collection. mental consequences [14, 15]. Internet is being an unavoidable part of day-to-day life 2.4.DataCollectionToolsandProcedures. Internet Addiction because the usage of the internet has been growing explo- Test (IAT) is a validated instrument to measure Internet sively worldwide. With the increase in the dependence of addiction [16]. Te Internet Addiction Test was primarily internet, people are gradually getting addicted towards it. developed by Dr. Kimberly Young, which is a 20-item 5-point Tere are only limited studies conducted in Nepal which Likert scale ranging from 0 to 5 (0 = less extreme behaviour to primarily focus on adolescents and medical students. Tis 5 = most extreme behaviour) that measures the severity of study focuses on undergraduate level students as this age self-reported compulsive use of the internet [17]. Te sum of group mostly uses internet for diferent purposes. Tey are the ratings was calculated for the 20-item responses for the particularly vulnerable to problematic internet use due to total IAT score. Te maximum IAT score is 100 points. IAT several factors such as the psychological and developmental scores were categorized as internet users who scored <50 were characteristics of late adolescence/young adulthood, ready considered average user and who scored ≥50 were considered access to the internet, and an expectation of computer/in- internet addicted. Te questionnaire consisted of four sections ternet use. Majority of internet users in Nepal are aged where the frst section consisted of sociodemographic in- 18–24 years, typical age group of undergraduate students [8]. formation, second section consist of behavioural factors re- So, this study helps to identify the prevalence of Internet lated to the internet use, third section was about the perceived addiction among undergraduate university students and to psychological status and interpersonal relationship, and the reveal factors associated with internet addiction in Kath- fnal section consisted of Young’s 20-item Internet Addiction mandu, Nepal. Test (IAT). Te questionnaire was pretested among 10% of the sample population prior to data collection. Data were col- 2. Materials and Methods lected by the help of a self-administered method. During the process of data collection, the nature of the study was 2.1.StudyDesign,Period,Setting,andPopulation. Tis cross- explained in detail to the participants; the details regarding the sectional study was conducted among the undergraduate duration of the study, informed consent, and confdentiality students of Kathmandu district of Nepal and data were concerns. Questionnaires were distributed to all the students collected during 30 June to 15 July of 2022. possessing inclusion criteria. 2.2. Inclusion and Exclusion Criteria. Undergraduate stu- 2.5. Data Quality Control Issues. One of the researchers was dents above the age of 18 years currently enrolled at any self-involved for data collection and supervised by the other course of Pokhara University inside Kathmandu district one. Each day at the end of data collection, every ques- were eligible to be included in this study and no exclusion tionnaire was checked, reviewed by the supervisor, and were made in terms of any attributes. organized for completeness and consistency. Pretesting of the questionnaire was carried out in one of the management 2.3. Sample Size Determinations, Sampling Techniques, and colleges afliated to Tribhuvan University in Kathmandu. Procedures. Te past study conducted among higher sec- Pretesting of the instrument was carried out in 10% of the sample. Cronbach’s alpha of IAT tools was found to be 0.799 ondary students in Kathmandu had noted the prevalence of internet addiction at 34.35% [16]. Considering this in this study. Journal of Addiction 3 Table 1: Sociodemographic profle of the participants. 2.6. Data Processing and Analysis. Data were entered in Epi- Data (version-3.1) and exported to Statistical Package on Characteristics n (%) Social Sciences (IBM SPSS) version 25. Te Pearson chi- Age squared test and binary logistic regression were applied to <20 years 100 (29.1) access the association between diferent independent variables ≥20 years 244 (70.9) and dependent variables (internet addiction) at 95% conf- Gender dence interval and 5% level of signifcance i.e., p value<0.05. Male 159 (46.2) Te unadjusted odds ratio (UOR) has also been reported along Female 185 (53.8) with the adjusted odds ratio (AOR) for those variables which Marital status were signifcant in bivariate analysis. For the multivariate Married 14 (4.1) analysis, the variance infation factor (VIF) test was performed Unmarried 330 (95.9) to check multicollinearity among the independent variables. Type of family Te Hosmer–Lemeshow test (the HL test) for goodness-of-ft Nuclear 218 (63.4) and Nagelkerke R square test were also performed for Joint/extended 126 (36.6) the model. Education status of fathers Illiterate 18 (5.2) 3. Results Literate 85 (24.7) Primary level 47 (13.7) A total of 400 questionnaires were distributed among the Secondary level 83 (24.1) Higher education 111 (32.3) undergraduate students of the selected colleges, of which 344 questionnaires were received covering complete response to Education status of mothers all the provided questions. Tus, the response rate of 86% Illiterate 35 (10.2) Literate 79 (23.0) was achieved. Primary level 77 (22.4) Secondary level 85 (24.7) 3.1. Prevalence of Internet Addiction. Out of total 344 stu- Higher education 68 (19.8) dents who provided their complete response, 164 (47.7%) Family economic status were found to had a mild level of Internet addiction under Low 17 (4.9) the IAT score of (31–49) whereas 96 (27.9%) were found to Middle 310 (90.1) had a moderate addiction level (score 50–79) and 7(2.0%) High 17 (4.9) were found to be severe addict (score 80–100). Te overall prevalence of possible addict/internet addict based on IAT had a lower self-esteem. Almost two-third 226 (65.7%) re- with a score of ≥50 was at 29.9% (95% CI: 25.0–34.9) ported having a wonderful relation with their parents while only few reported to have wonderful relation with their teachers and peers. More than half of the students i.e., 193 3.2. Sociodemographic Characteristics of Respondents. Te (56.1%) experienced loneliness sometimes in past one week mean age of the students who participated in the study was (Table 3). 20.68± 1.863 years while the minimum and maximum age of the participants ranged between 18 and 30 years. Tere was nearly equal participation of the female and male undergraduate 3.5. Factors Associated with Internet Addiction. In bivariate students as 53.8% female and 46.2% were male. Te majority analysis, no statistically signifcant relationship was observed were unmarried and reported to have a nuclear family (Table 1). between student’s sociodemographic characteristics such as age, gender, marital status, parental education, and eco- nomic status of the family and their internet addiction status 3.3. Internet Use-Related Characteristics of Respondents. (Table 4). In regards to the internet use pattern, a statistically With regards to the internet use pattern, most (208 i.e., signifcant relationship was observed between time spent by 60.5%) of the participants reported have started using in- the students on daily basis over internet and their internet ternet between 11 and 15 years of age. Likewise, more than addiction status. Similarly, use of internet for social net- half 201(58.4%) noted that they spent more than 5 hours per working and media was also found to be associated with day over the internet. More than two-third (72.1%) of the internet addiction at p< 0.05 (Table 5). students reported that their internet use is mostly for ed- In context of perceived psychological factors and in- ucational, recreational, and entertainment purposes, i.e., 256 terpersonal relationship of the students, nature of students, (74.4%). Similarly, half of the participants (50.9%) reported relationship with their parents, parental control over their that their social media as one of the major platforms for their internet use, and the perceived level of loneliness were found internet use while 39 (11.3%) reported to be engaged in to have a statistically signifcant relationship with their in- online jobs (Table 2). ternet addiction status at p< 0.05 (Table 6). For multivariate analysis, the variance infation factor 3.4. Perceived Psychological Status and Interpersonal Re- (VIF) test among the independent variables was performed where the highest reported VIF was 1.894 suggesting no issue lationship of Respondents. It was noted that nearly a quarter of the participants perceived themselves to be stressed and of multicollinearity. Students who spent fve hours or more on 4 Journal of Addiction Table 2: Internet use pattern. Table 3: Perceived psychological status and interpersonal relationship. Characteristics n (%) Characteristics n (%) Starting age of Internet use <10 years 50 (14.5) Perceived stress 11–15 years 208 (60.5) Presence 101 (29.4) >16 years 86 (25.0) Absence 243 (70.6) Time spent per day Perceived depression <5 hours 143 (41.6) Presence 17 (4.9) ≥5 hours 201 (58.4) Absence 327 (95.1) Major use of Internet for education/recreation Perceived self-esteem Yes 248 (72.1) Low 101 (29.4) No 96 (27.9) High 243 (70.6) Major use of Internet for entertainment/refreshment Relationship with parents Yes 256 (74.4) Wonderful 226 (65.7) No 88 (25.6) Good 65 (18.9) Satisfactory 53 (15.4) Major use of Internet for Internet gaming Yes 129 (37.5) Parental control over Internet use No 215 (62.5) Not at all 85 (24.7) Sometimes 187 (54.4) Major use of Internet for social networking Often/almost always 72 (20.9) Yes 175 (50.9) No 169 (49.1) Relationship with teachers Wonderful 108 (31.4) Major use of Internet for online jobs Good 139 (40.4) Yes 39 (11.3) Satisfactory 97 (28.2) No 305 (88.7) Relationship with peer Major use of Internet to watch pornography Wonderful 110 (32.0) Yes 41 (11.9) Good 154 (44.8) No 303 (88.1) Satisfactory 80 (23.3) Perceived level of self-control the internet were found to be almost twice more at odds (AOR: Not at all 44 (12.8) 1.780, 95% CI: 1.052–3.012) of experience internet addiction as Sometimes 177 (51.5) compared to those who used internet for less than 5 hours Often/almost always 123 (35.8) a day. Similarly, higher odds of internet addiction were ob- Perceived loneliness served among the students who had good (AOR: 1.957, 95% Not at all 52 (15.1) CI: 1.022–3.745) and satisfactory (AOR: 2.832, 95% CI: Sometimes 193 (56.1) 1.354–5.614) relation with their parents as compared to those Often/almost always 99 (28.8) who had wonderful relation. Likewise, students who reported their parents have higher control over their internet use were A study conducted among medical students in Nepal found to be thrice more at odds (AOR: 3.643, 95% CI: found low prevalence of Internet addiction than this study 1.687–7.863) of internet addiction as compared to students where out of 100 students, 21 students were found to be whose parents do not control their internet use. Students slightly addicted to using the Internet [22]. In a study, experiencing loneliness most of the time were also found to be among adolescents in a peri-urban setting in Nepal, 21.5% of have three-folds increase in their odds (AOR: 3.105, 95% CI: the participants were identifed with borderline Internet 1.264–7.629) of internet addiction in comparison to those who addiction and 13.3% with possible internet addiction [23]. A reported not experiencing loneliness (Table 7). study among adolescent Turkish students prevalence of IA was 17.7%. 4. Discussion A study conducted among undergraduate students in It was seen that the prevalence of internet addiction among Nepal revealed 35.4% prevalence of Internet addiction [24] and a study among higher secondary level students in this study group was 29.90% which was supported by a meta- analysis conducted in prevalence of Internet addiction in Kathmandu district revealed possible addicts/Internet ad- dicts to be 34.35% [16] which is slightly higher than medical students in diferent countries, the prevalence of IA was 30.1%, [18]. Furthermore, a study conducted among this study. undergraduate university students in Ethiopia was found to Te possible reason for variations in prevalence of In- be 29.4% moderate to severe IA [19]. Another study con- ternet addiction across diferent country or even in the study ducted among young adults in Bangladesh revealed prev- conducted in same country might be because of sample size, alence of Internet addiction was 27.1%, [20], and online sampling procedure, diference in social context and survey of problematic Internet use (PIU) and its correlates background of the participants, purpose of the Internet use, among undergraduate medical students of Nepal found the knowledge about the problems of Internet addiction, or prevalence of PIU to be 31.9% [21]. aware about the proper use of Internet. Journal of Addiction 5 Table 4: Internet addiction and its association with sociodemo- Table 5: Internet addiction and its association with the Internet use graphic characteristics. pattern. Internet addiction Internet addiction 2 2 Variables x Variables x p value Average user n Possible Possible value Average user n (%) (%) addict n (%) addict n (%) Age Starting age of Internet use <20 77 (77.0) 23 (23.0) <10 years 32 (64.0) 18 (36.0) 3.239 0.072 ≥20 164 (67.2) 80 (32.8) 11–15 years 148 (71.2) 60 (28.8) 1.025 0.599 >16 years 61 (70.9) 25 (29.1) Gender Male 109 (68.6) 50 (31.4) Time spent per day over Internet 0.319 0.572 Female 132 (71.4) 53 (28.6) <5 hours 111 (77.6) 32 (24.4) 6.676 0.010 ≥5 hours 130 (62.2) 71 (35.3) Marital Status Married 12 (85.7) 2 (14.3) Major use of Internet for education/recreation 1.75 0.192 Unmarried 229 (69.4) 101 (30.6) Yes 176 (71.0) 72 (29.0) 0.612 0.434 No 65 (67.7) 31 (32.3) Type of family Nuclear 146 (67.0) 72 (33.0) Major use of Internet for entertainment/refreshment 2.701 0.100 Yes 176 (68.8) 80 (31.3) Joint/extended 95 (75.4) 31 (24.6) 0.907 0.341 No 65 (73.9) 23 (26.1) Fathers’ education status Higher Major use of Internet for Internet gaming 112 (74.7) 38 (25.3) education 2.693 0.101 Yes 86 (66.7) 43 (33.3) 1.132 0.287 Basic education 129 (66.5) 65 (33.5) No 155 (72.1) 60 (27.9) Mothers’ education status Major use of Internet for social networking Higher Yes 128 (75.7) 41 (24.3) 135 (70.7) 56 (29.3) 5.112 0.024 education 0.079 0.778 No 113 (64.6) 62 (35.4) Basic education 106 (69.3) 47 (30.7) Major use of Internet for online jobs Family economic status Yes 26 (66.7) 13 (33.3) 0.271 0.603 Low 13 (76.5) 4 (23.5) No 215 (70.5) 90 (29.5) Middle 213 (68.7) 97 (31.3) 3.280 0.194 Major use of Internet to watch pornography High 15 (88.2) 2 (11.8) Yes 27 (65.9) 14 (34.1) 0.392 0.531 No 214 (70.6) 89 (29.4) Statistical signifcance at p< 0.05. Te association between time spent on Internet per day and IA was found to be statistically signifcant (p value 0.010). Tis association is supported by various studies such Majority of the participants used internet for enter- as a study conducted in Nepal [16], Ethiopia [19], Bangla- tainment and refreshment purpose followed by education or desh [20], and Bengaluru, India [25]. As the dependence on to get new information. Use of Internet for social networking Internet increases, individuals spent more time on Internet. purpose showed signifcant association with IA (p value Relationship with parents and IA have signifcant associa- 0.024). Tis fnding is lined with the study conducted in Northern Tanzania [40] and Saudi Arabia [41]. Most tion (p < 0.001) which is also indicated by several studies [16, 26–30]. Internet addiction was found to be higher commonly used app/website was found to be social media among those who had satisfactory relationships within among the participants supported by the study among young family. Self-control was negatively linked to Internet ad- adults in Bangladesh [20]. diction as prevalence of Internet addiction was found to be Te restrictive parenting approach was found to be higher in individuals with poor self-control than individuals signifcantly associated with IA (p < 0.001) similar to the with good self-control [30–32]. Te loneliness of the par- fnding of the study conducted among adolescents in Hong ticipants was found to be signifcantly associated with In- Kong, the greater the number of rules and the stricter the ternet addiction (p < 0.001) similar with the various studies enforcement of rules concerning the Internet use, the more likely it is that adolescents will become addictive users [29]. [6, 33, 34]. Lonely individuals prefer to increase their communication through social networks to meet their 4.1. Strength and Limitations. Te target population of this emotional needs that was included in the meta-analysis conducted in Iran [35]. Perceived stress did not show any study was undergraduate university students of Nepal, signifcant association with Internet addiction which is which group is highly vulnerable to Internet addiction and found to be consistent with the study conducted in East majority of Internet users are also of this group. Tis study Malaysia [36] and the study conducted among Chinese included a considerably high number of socio-demographic adolescents [37]. However some previous studies reported variables and also the Internet use and behavioural related signifcant association between perceived self-esteem and and family and college related variables. internet addiction, [38, 39] but we did not fnd the asso- Te fnding of the study is based on the primary in- ciation i.e., low self-esteem increased susceptibility to IA. formation collected using the standard tool by the active involvement of the researchers. Since this is a cross-sectional Terefore, more studies are needed to understand the sig- nifcant association of self-esteem and IA. study conducted with minimal required sample size, it could 6 Journal of Addiction Table 6: Internet addiction and its association with the perceived psychological status and interpersonal relationship. Internet addiction Variables x p value Possible Average user n (%) addict n (%) Perceived stress Presence 75 (74.2) 26 (25.8) 1.202 0.273 Absence 166 (68.3) 77 (31.7) Perceived depression Presence 9 (52.9) 8 (47.1) 2.498 0.114 Absence 232 (70.9) 95 (29.1) Perceived self-esteem Low 71 (70.3) 30 (29.7) 0.114 0.736 High 170 (70.0) 73 (30.0) Relationship with parents Wonderful 175 (77.4) 51 (22.6) ∗∗ Good 40 (61.5) 25 (38.5) 19.254 <0.001 Satisfactory 26 (49.1) 27 (50.9) Parental control over Internet use Not at all 69 (81.2) 16 (18.8) ∗∗ Sometimes 134 (71.7) 53 (28.3) 15.487 <0.001 Often/almost always 38 (52.8) 34 (47.2) Relationship with teachers Wonderful 82 (75.9) 26 (24.1) Good 100 (71.9) 39 (28.1) 5.950 0.054 Satisfactory 55 (59.1) 38 (39.2) Relationship with peer Wonderful 81 (73.6) 29 (26.4) Good 109 (70.8) 45 (29.2) 2.227 0.328 Satisfactory 51 (63.8) 29 (36.3) Perceived level of self-control Not at all 30 (68.2) 14 (31.8) Sometimes 121 (68.4) 56 (31.6) 1.072 0.585 Often/almost always 90 (73.2) 33 (26.8) Perceived loneliness Not at all 43 (82.7) 9 (17.3) ∗∗ Sometimes 143 (74.1) 50 (25.9) 15.381 <0.001 Often/almost always 55 (55.6) 44 (44.4) ∗∗ Statistical signifcance at p< 0.001. Table 7: Predictors of Internet addiction. Bivariate logistic regression Multivariate logistic regression Variables UOR 95% CI AOR 95% CI Time spent per day over Internet <5 hours Ref Ref ∗ ∗ ≥5 hours 1.894 1.163–3.087 1.780 1.052–3.012 Major use of Internet for social networking Yes 1.713 1.072–2.737 1.640 0.987–2.724 No Ref Ref Relationship with parents Wonderful Ref Ref ∗ ∗ Good 2.145 1.190–3.865 1.957 1.022–3.745 ∗ ∗ Satisfactory 3.563 1.912–6.639 2.832 1.354–5.614 Parental control over Internet use Not at all Ref Ref Sometimes 1.706 0.908–3.203 1.636 0.838–3.185 ∗ ∗ Often/almost always 3.859 1.889–7.880 3.643 1.687–7.863 Perceived loneliness Not at all Ref Sometimes 1.671 0.760–3.671 1.667 0.715–3.886 ∗ ∗ Often/almost always 3.822 1.682–8.683 3.105 1.264–7.629 Statistical signifcance at p< 0.05, Nagelkerke R square: 0.185, Hosmer and Lemeshow chi-square: 5.015, p � 0.756. Journal of Addiction 7 not cover all disciplines and universities hence insufcient to IQR: Interquartile range conclude that Internet addiction is high among un- IRC-CiST: Institutional Review Committee CiST dergraduate students in Nepal. So, further studies using larger PIU: Problematic Internet use sample size is necessary for fnding the causal associations on UOR: Unadjusted odds ratio Internet addiction among undergraduate students in Nepal. VIF: Variance infation factor. Data Availability 4.2. Implications of the Study. Problematic use of Internet/ Internet addiction can result in the various health problems Te data used to support the fndings of this study are and many factors are associated with it. Tis study identifed available from the corresponding author upon request. the purpose of the Internet use, level of Internet addiction, and factors associated with Internet addiction. So, by identifying Ethical Approval the factors associated with Internet addiction and strength of association between them, possible intervention action can be Ethical approval for research was taken from IRC-CiST (REF taken and will ultimately help to reduce the various health and NO IRC 190/078/079) prior to the data collection, and the educational problems, social life, and family relationship re- approval was also taken from concerned colleges to conduct lated problems arising due to excessive use of Internet. the research. 5. Conclusions and Policy Recommendations Consent 5.1. Conclusion. Prevalence of possible addiction of In- Informed consent was taken from the participants with ternet use was about one-third among undergraduate explanation of aim and nature of the study. Participants were students. Tere are numbers of factors which play a sig- not forced to participate in the research and were allowed to nifcant role to point out the alarming situation such as withdraw from the study. time spent on Internet per day, relationship with parents, parental control over Internet use, and loneliness feelings Conflicts of Interest were found to be statistically associated with Internet addiction. Major purpose of Internet use was found to be Te authors declare that they have no conficts of interest regarding the publication of this article. entertainment and refreshment, and use of internet for social networking showed signifcant association with IA. Authors’ Contributions Most commonly used app/website was found to be social media among the participants. SA generated concept, developed proposal, collected data, As family relationship and a restrictive parenting ap- and prepared preliminary manuscript. LA contributed to proach were found to be signifcantly associated, family- data entry, cleaning, and result preparation and supported based prevention strategies need to be developed and applied manuscript preparation. SK supported for proposal fnal- to achieve healthy family interactions through improving ization, supervision on data collection and quality control, parents-child communication and strengthening family and manuscript writing. SP coordinated with college prior to functionality rather than directly restricting the Internet use. data collection, analysed data, and wrote the manuscript. Further studies should be conducted in the consequences of MK fnalized the proposal, analysed data, and prepared and problematic internet use in mental health. reviewed manuscript. All authors have read and fnalized the manuscript. 5.2. Policy Recommendations. Family-based prevention strategies need to be developed and practice to achieve Acknowledgments healthy family interactions through improving parents-child Te authors want to acknowledge all persons who helped communication and strengthening family functionality them directly and indirectly during research. We want to rather than directly restricting the Internet use. Education acknowledge all colleges which gave them authority to programs need to be carried out on regular basis regarding collect data and all respondents for providing the data the addictive behaviour and coping strategies also involving during data collection. education and sensitization of the students as well as teachers. References Abbreviations [1] R. Ali, N. Mohammed, and H. Aly, “Internet addiction among medical students of Sohag University, Egypt,” Journal of the AOR: Adjusted odds ratio Egyptian Public Health Association, vol. 92, no. 2, pp. 86–95, CI: Confdence interval HT test: Hosmer–Lemeshow test [2] A. M. Al Mukhaini, F. A. Al Houqani, and R. M. Al Kindi, IA: Internet addiction “Internet addiction and depression among postgraduate IAI: Internet Addiction Index residents: a cross-sectional survey,” Sultan Qaboos University IAT: Internet Addiction Test Medical Journal, vol. 21, no. 3, pp. 408–415, 2021. 8 Journal of Addiction [3] E. Ramon-Arbues, J. M. Granada-Lopez, B. Martinez-Abadia, university students in Ethiopia: a community university- E. Echaniz-Serrano, I. Anton-Solanas, and M. Nash, “Prev- basedcross-sectional study,” BMC Psychol, vol. 9, no. 1, 2021. alence and factors associated with problematic internet use in [20] T. Hassan, M. M. Alam, A. Wahab, and M. D. Hawlader, a population of Spanish university students,” International “Prevalence and associated factors of internet addiction Journal of Environmental Research and Public Health, vol. 18, among young adults in Bangladesh,” Journal of the Egyptian no. 14, p. 7620, 2021. Public Health Association, vol. 95, no. 3, p. 8, 2020. [4] S. Kemp, Global Statshot Report, Datareportal, Vancouver, [21] P. Sharma, R. Shakya, S. Singh, and Y. P. S. Balhara, “An Canada, 2022. online survey of problematic internet use and its correlates [5] M. Hull and D. A. Proulx, Internet addiction facts and sta- among undergraduate medical students of Nepal,” Neurology tistics, Te Recovery Village, Umatilla, FL, USA, 2022. Psychiatry and Brain Research, vol. 37, pp. 95–99, 2020. [6] Y. S. Balhara, A. Mahapatra, P. Sharma, and R. Bhargava, [22] N. Upadhayay and S. Guragain, “Internet use and its addiction “Problematic internet use among students in South-East Asia: level in medical students,” Advances in Medical Education and current state of evidence,” Indian Journal of Public Health, Practice, vol. 8, pp. 641–647, 2017. vol. 62, no. 3, pp. 197–210, 2018. [23] K. Karki, D. R. Singh, D. Maharjan, Sushmitha, S. Shrestha, [7] H. Khazaie, J. Y. Lebni, J. Abbas et al., “Internet addiction and D. K. Tapa, “Internet addiction and sleep quality among status and related factors among medical students: a cross- adolescents in a peri-urban setting in Nepal: a cross- sectional study in western iran,” International Quarterly of sectionalschool-based survey,” PLoS One, vol. 16, no. 2, Ar- Community Health Education, vol. 15, ticle ID e0246940, 2021. p. 0272684X211025438, 2021. [24] P. M. Bhandari, D. Neupane, S. Rijal, K. Tapa, S. R. Mishra, [8] S. Kemp, Digital 2022: NepalDatareportal, Vancouver, Can- and A. K. Poudyal, “Sleep quality, internet addiction and ada, 2022. depressive symptoms among undergraduate students in [9] D. X. Y. Chia, C. W. L. Ng, G. Kandasami et al., “Prevalence of Nepal,” BMC Psychiatry, vol. 17, no. 1, p. 106, 2017. internet addiction and gaming disorders in Southeast Asia: [25] S. Krishnamurthy and S. K. Chetlapalli, “Internet addiction: a meta-analysis,” International Journal of Environmental prevalence and risk factors: a cross-sectional study among Research and Public Health, vol. 17, no. 7, Article ID 2582, college students in Bengaluru, the Silicon Valley of India,” Indian Journal of Public Health, vol. 59, no. 2, pp. 115–121, [10] A. Stravogiannis, “Internet addiction: a case report,” Revista Brasileira de Psiquiatria, vol. 31, no. 1, pp. 78-79, 2009. [26] K. Ahmadi, “Internet addiction among Iranian adolescents: [11] M. Aqeel, T. Rehna, K. H. Shuja, and J. Abbas, “Comparison a nationwide study,” Acta Medica Iranica, vol. 52, no. 6, of students’ mental wellbeing, anxiety, depression, and quality pp. 467–472, 2014. of life during COVID-19’s full and partial (smart) lockdowns: [27] E. Marzilli, L. Cerniglia, G. Ballarotto, and S. Cimino, “In- a follow-up study at a 5-month interval,” Frontiers in Psy- ternet addiction among young adult university students: the chiatry, vol. 13, Article ID 835585, 2022. [12] J. Abbas, M. Aqeel, J. Abbas et al., “Te moderating role of complex interplay between family functioning, impulsivity, social support for marital adjustment, depression, anxiety, depression, and anxiety,” International Journal of Environ- and stress: evidence from Pakistani working and nonworking mental Research and Public Health, vol. 17, no. 21, p. 8231, women,” Journal of Afective Disorders, vol. 244, pp. 231–238, [28] O. Senormanci, G. Senormanci, O. Guclu, and R. Konkan, [13] M. Peris, U. de la Barrera, K. Schoeps, and I. Montoya- “Attachment and family functioning in patients with internet Castilla, “Psychological risk factors that predict social net- addiction,” General Hospital Psychiatry, vol. 36, no. 2, working and internet addiction in adolescents,” International pp. 203–207, 2014. Journal of Environmental Research and Public Health, vol. 17, [29] C. S. T. Wu, H. T. Wong, K. F. Yu et al., “Parenting ap- no. 12, Article ID 4598, 2020. proaches, family functionality, and internet addiction among [14] J. Abbas, D. Wang, Z. Su, and A. Ziapour, “Te role of social Hong Kong adolescents,” BMC Pediatrics, vol. 16, no. 1, p. 130, media in the advent of covid-19 pandemic: crisis manage- ment, mental health challenges and implications,” Risk [30] A. Akın, S. Arslan, N. Arslan, R. Uysal, and U. Sahranç, “Self- Management and Health Care Policy, vol. 14, pp. 1917–1932, control management and internet addiction,” International Online Journal of Educational Sciences, vol. 7, no. 3, pp. 95– [15] J. Abbas, J. Aman, M. Nurunnabi, and S. Bano, “Te impact of 100, 2015. social media on learning behavior for sustainable education: [31] C. Li, J. Dang, X. Zhang, Q. Zhang, and J. Guo, “Internet evidence of students from selected universities in Pakistan,” addiction among Chinese adolescents: the efect of parental Sustainability, vol. 11, no. 6, p. 1683, 2019. behavior and self-control,” Computers in Human Behavior, [16] I. Karmacharya, K. Bhujel, D. Kumar Yadav, and K. Subedi, vol. 41, pp. 1–7, 2014. “Prevalence of internet addiction among higher secondary [32] S. Li, P. Ren, M. M. Chiu, C. Wang, and H. Lei, “Te re- level students in Kathmandu district,” Journal of Health and lationship between self-control and internet addiction among Allied Sciences, vol. 7, no. 1, pp. 40–46, 2019. students: a meta-analysis,” Frontiers in Psychology, vol. 12, [17] H. Cash, C. D Rae, A. H Steel, and A. Winkler, “Internet Article ID 735755, 2021. addiction: a brief summary of research and practice,” Current [33] E. Khatcherian, D. Zullino, D. De Leo, and S. Achab, “Feelings Psychiatry Reviews, vol. 8, no. 4, pp. 292–298, 2012. of loneliness: understanding the risk of suicidal ideation in [18] M. W. B. Zhang, R. B. C. Lim, C. Lee, and R. C. M. Ho, adolescents with internet addiction. A theoretical model to “Prevalence of internet addiction in medical students: a meta- answer to a systematic literature review, without results,” analysis,” Academic Psychiatry, vol. 42, no. 1, pp. 88–93, 2018. [19] Y. Zenebe, K. Kunno, M. Mekonnen et al., “Prevalence and International Journal of Environmental Research and Public associated factors of internet addiction among undergraduate Health, vol. 19, no. 4, p. 2012, 2022. Journal of Addiction 9 [34] Y. R. Cho and H. J. Lee, “A study on a model for internet addiction of adolescents,” Taehan Kanho Hakhoe Chi, vol. 34, no. 3, pp. 541–551, 2004. [35] H. Mozafar Saadati, H. Mirzaei, B. Okhovat, and F. Khodamoradi, “Association between internet addiction and loneliness across the world: a meta-analysis and systematic review,” SSM Population Health, vol. 16, Article ID 100948, [36] W. L. Cheah, M. S. Bo, L. H. Yee et al., “Internet addiction in preuniversity students in East Malaysia: prevalence and its association with stress and depression,” Malaysian Journal of Psychiatry, vol. 31, no. 1, pp. 13–18, 2022. [37] J. Tang, Y. Yu, Y. Du, Y. Ma, D. Zhang, and J. Wang, “Prevalence of internet addiction and its association with stressful life events and psychological symptoms among ad- olescent internet users,” Addictive Behaviors, vol. 39, no. 3, pp. 744–747, 2014. [38] S. A. Bahrainian, K. H. Alizadeh, M. R. Raeisoon, O. H. Gorji, and A. Khazaee, “Relationship of Internet addiction with self- esteem and depression in university students,” Journal of Preventive Medicine and Hygiene, vol. 55, no. 3, pp. 86–89, [39] L. Naseri, J. Mohamadi, K. Sayehmiri, and Y. Azizpoor, “Perceived social support, self-esteem, and internet addiction among students of Al-zahra university, tehran, Iran,” Iranian Journal of Psychiatry and Behavioral Sciences, vol. 9, no. 3, Article ID e421, 2015. [40] I. B. Mboya, B. J. Leyaro, A. Kongo, C. Mkombe, E. Kyando, and J. George, “Internet addiction and associated factors among medical and allied health sciences students in northern Tanzania: a cross-sectional study,” BMC Psychol, vol. 8, no. 1, p. 73, 2020. [41] D. M. Abdel-Salam, H. I. Alrowaili, H. K. Albedaiwi, A. I. Alessa, and H. A. Alfayyadh, “Prevalence of Internet addiction and its associated factors among female students at Jouf University, Saudi Arabia,” Journal of the Egyptian Public Health Association, vol. 94, no. 1, p. 12, 2019.

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Published: Apr 8, 2023

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