|Year : 2011 | Volume
| Issue : 2 | Page : 158-162
Internet addiction and modeling its risk factors in medical students, Iran
Farhad Ghamari1, Abolfazl Mohammadbeigi2, Narges Mohammadsalehi3, Amir Almasi Hashiani4
1 Department of Occupational health, Health School, Arak University of Medical Sciences, Arak, Iran
2 Qom University of Medical Sciences, Qom, Iran
3 Health Vic-chancellor, Arak University of Medical Sciences, Arak, Iran
4 Department of Epidemiology, Health School, Arak University of Medical Sciences, Arak, Iran
|Date of Web Publication||20-Jan-2012|
Health Faculty, Qom
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background : Today's internet is a usual and common method for identifying and fulfilling unknown practices. Internet network has been prepared rapid and comfortable access to information. Internet addiction is a new and attractive subject that has been regarded as behavior-based addiction recently. Purpose : To estimate the prevalence of internet addiction and some of the related factors among medical students, Iran. Materials and Methods : An analytical cross-sectional study was conducted on 426 students selected through two-stage sampling method. Yang standard internet addiction questionnaire was used for data collection. After data entry, χ 2 , t-test, and Pearson coefficient statistical tests were applied. 0.05 was considered as the significance level. Results : The overall prevalence of internet addiction was 10.8%, with moderate and severe internet addiction equal to 8% and 2.8%, respectively. Mean and standard deviation of Yang internet addiction score was calculated as 32.74±14.52. Internet addiction was associated with sex, marital status, father's job, rate of knowledge about computer and internet, and educational level (P<0.05). But it was not associated with the parents' education, residential area, field of study and level, and school of education (P>0.05). Conclusion : Because internet addiction leads to wasting of the students' leisure time and also useful time, it affects the educational situation inversely. Some measures should be taken to plan and improve the use of internet.
Keywords: Addiction, educational situation, internet, students, Yang
|How to cite this article:|
Ghamari F, Mohammadbeigi A, Mohammadsalehi N, Hashiani AA. Internet addiction and modeling its risk factors in medical students, Iran. Indian J Psychol Med 2011;33:158-62
|How to cite this URL:|
Ghamari F, Mohammadbeigi A, Mohammadsalehi N, Hashiani AA. Internet addiction and modeling its risk factors in medical students, Iran. Indian J Psychol Med [serial online] 2011 [cited 2020 Mar 31];33:158-62. Available from: http://www.ijpm.info/text.asp?2011/33/2/158/92068
| Introduction|| |
Internet is growing worldwide for information and is a user-friendly communication medium that is cost-effective and fast useful tool in education.  Today, internet is a global system that is increasingly used by all people as one of the most important devices for access to information in the world, especially young adults and medical students use it as a source of health-related information. ,,, So, it has become an important part of daily life for college people for both academic and recreational purposes. , The report of a recent study across the world which highlights the high-speed and inexpensive internet penetrance in South Korean households as being equal to 90%. Also, there has been more than 1300% increase in the number of internet users in developing countries, Africa, and the Middle East from 2000 to 2009. 
Internet addiction is a new and attractive subject considered as a behavior-based addiction in recent years.  Although computer and Internet are the least used source, Internet is a tool for any person in any major city for communication and connection with other coworkers and also for imparting information.  Yang believed that addiction is applicable to internet users, because internet addiction symptoms are the same as those in alcohol and drug addiction. 
Severe internet addicts use it for film, music, cartoon, computer games, social sites, and chat rooms, but normal users use it for news, events, trade of goods, and educational and universal sites. Also, internet addicts use internet in a adrift manner and in private places. 
Today's internet is a crucial tool for many of people for acquiring valuable information.  Researches showed that the prevalence of internet addiction varies from 1.5% to 25% in different population ,,, and the quality and quantity of internet use in these users are associated with their skills in using computer and internet, and duration and severity of internet use. , The internet addiction is related to some factors including sex, mental health, and other psychological symptoms, deficiency in social support, neurotic personality characteristics of users, skill in the use of internet and its use at night. ,,,
Universities have a common group of internet users. This group consider internet as a reliable source and use it for private and occupational objectives.  So, medical universities pay millions of dollars for buying full-text articles and books.  But internet abuse and the students' curious search in careless manner alienate them from original mission, leading to the waste of their leisure time. This study was conducted to estimate the prevalence of internet addiction and some of the related factors among Arak medical students, Iran.
| Materials and Methods|| |
An analytical cross-sectional study was conducted on 426 medical students of Arak University in spring, 2009. The inclusion criteria were being in second-term and higher and consent for participating in the study. Sampling was performed using two-stage method; first, the students were selected through stratified proportional sampling method based on level and school. Then in each stratum, the prone students were selected by random sampling method. There were eight strata in our study with approximately equal population in each stratum. Calculation of the sample size was performed based on the results of Dargahi's study adapted from the Yang score.  In calculation of the sample size, α and precision(d) were considered as 0.05. Statistical population consisted of all of the students studying in Arak University of Medical Sciences in the data collection period. Ethics committee of Arak University of Medical Sciences approved the study and informed consent was taken from the students before collecting the data.
A two-section structured questionnaire was applied for data collection. The first section contained background variables such as age, sex, educational field and course, skill rate in using computer and internet, the use of different sites, their purposes of searching, and other related questions. The second section was the reliable and standard Yang questionnaire for measuring internet addiction containing twenty questions of Likert type. Based on some studies, this questionnaire is a valid and reliable instrument for use in further research for detection of Internet addiction.  This questionnaire was validated in Persian language by Dargahi.  The internal consistency of the questionnaire in this study was equal to 0.89.
The Persian language version of the questionnaire was distributed among eligible participants. Each participating acquired one score from one (never) to five (always) based on the response to questions and total score gained from summing of all the questions. The minimum and maximum scores were 20 and 100, respectively. The participants were divided into three levels based on their score, i.e., less than 50 as normal, 50 to 79 as light, and higher than 79 as severe addicts. In this study, data were analyzed based on two groups of normal and addict.
After collecting data and coding and entering to SPSS software, analysis was done by t-test, χ 2, and spearman correlation coefficient tests. Significant level considered at 0.05.
| Results|| |
The overall prevalence of internet addiction was calculated equal to 10.8% moderate and severe internet addiction being 8.1% and 2.8%, respectively. In average, the students had worked 4.63±3.87 hours and 3.36±3.46 hours by computer and internet, respectively. Also, the mean of Yang questionnaire score was 32.74±14.52.
Of all the participants, 64.9% were females and 76.6% lived in dormitory. The most important objectives of the students in using internet were research and scientific surveys (48.4%), computer games and hobbies (20.5%), checking email (9.8%), political and social news (6.5%), and chat rooms (5.6%).
The results in [Table 1] showed that the mean age in the beginning of the use of internet and internet addiction score obtained from Yang questionnaire was significantly different between normal and addict students (P<0.05), but it did not show any difference by the age of the students (P >0.05). Also, internet addiction had a significant association with educational term (P <0.05), but it did not show any significant association with educational field, school, and level of the students (P >0.05).
|Table 1: Age, first use age of internet, and Yang score of the study participants|
Click here to view
As shown in [Table 2], the students' sex, marital status, father's occupation, and use of chat rooms had a significant association with internet addiction (P<0.05), but no significant relationship was found among residency place, living in the dormitory, research practices, and parents' education with internet addiction (P>0.05). Also, the students' knowledge about computer and internet, use of internet in privacy, and repetition in internet use showed a significant difference between normal and abnormal students (P<0.05), though addicted students intended to use internet in private places and repeatedly
|Table 2: Association of demographic variables with internet addiction in the participants of the study|
Click here to view
|Table 3: Association between computer and internet awareness and internet usage in the participants of the|
Click here to view
Logistic regression analysis showed that age less than 20 years, male gender, and use of chat rooms were the most important predictors of internet addiction among the students [Table 4].
|Table 4: Results of logistic regression for factors affecting internet addiction|
Click here to view
| Discussion|| |
The overall prevalence of internet addiction was equal to 10.8% and moderate and severe internet addictions were 8.1% and 2.8%, respectively. In Lam et al.'s study,  moderate internet addiction prevalence was 10.2% and severe internet addiction was 0.6%, which is similar to our results.
Johansson et al., based on Yang questionnaire, reported 10.66% internet addiction prevalence among Norwegian 12- to 18-year-old young people.  But in a study on Korean students, this prevalence was calculated to be 20%,  that is two-fold our results, because their study population contained younger age groups. Our results and those of other studies ,, showed that the younger age is a related factor for internet addiction. It is noticeable that in Deng et al.'s study,  the internet addiction disorder prevalence was 5.52%, which was less than that of our study.
The most important objectives of the students in the use of internet were research and scientific surveys, computerized games and hobbies, checking of email, political and social news, and use of chat rooms, respectively. In Simos et al.'s  study on Greek adults, computerized games had the most use and news services were in the second rank of internet use.
Our results showed that male gender was one of the main predictors of internet addiction; so, there is an increase of 3.5-fold probability of internet addiction as compared with females. In other studies such as those of Deng et al.,  Tsai et al.,  and Ceyhan in Turkey,  a significant difference was observed between the two sexes.
Age less than 20 years was one of the other risk factors for internet addiction; so, younger students were 4-fold at risk as compared with those over 20 years of age for internet addiction. In Dargahi's study,  younger people used internet 3-fold more than other people. In other studies in Tiwan  and China,  this disorder has shown a significant association with age and study in lower courses.
Use of chat rooms was another main predictor of internet addiction which increased the probability of the problem to 2.7 fold. This finding is similar to other studies showing that addicted people to internet use chat rooms more than any other activities. Dragahi showed that chat room conversation in severe addict users is more than three-fold that of other users and in these people, using film, music, games, and internet conversation is higher. 
In the univariate analysis, it was observed that internet addiction rate is correlated with internet and computer knowledge rate of students directly. In other studies, the level of internet addiction increases by the level of the students' awareness about computer and internet. Dargahi's study also obtained similar results.  He showed that the number of students using Internet was associated with their skill in the use of Internet, educational level, field of study, and number of related scientific works. But this study did not show any association between Internet addiction, field and level of education, and research activities; some of these differences can be attributed to different study population in the two studies.
Internet use in the privacy and frequency of Internet use showed a significant difference between two groups of students, normal and internet addict. Therefore, addicted students are more interested to use from it in privacy. Other studies have shown a significant relationship between loneliness, lack of confidence, lack of social skills, and Internet use. ,
However, the overall prevalence of internet addiction among Arak medical students is not much different from other regions, but because of the widespread and fast use of internet and computer, there is the threat that its prevalence increases. Since Internet addiction was related to male gender, skill and knowledge in the use of internet and computer, universities should try to educate the students to use internet meaningfully and appropriately. Also, the students should be encouraged to use internet for review of academic papers and cultural sites and use internet during their leisure time.
| Acknowledgments|| |
We are very thankful to all the students that cooperated in the present study. Also, we are very grateful to research vice-chancellor of Arak University of Medical Sciences who supported this research financially.
The authors would like to thank Dr. Nasrin Shokrpour at Center for Development of Clinical Research of Nemazee Hospital for editorial assistance.
| References|| |
|1.||Castrén J, Huttunen T, Kunttu K. Users and non-users of web-based health advice service among Finnish university students - chronic conditions and self-reported health status (a cross-sectional study). BMC Med Inform Decis Mak 2008;8:8. |
|2.||Aboujaoude E. Problematic internet use: An overview. World Psychiatry 2010;9:85-90. |
|3.||Ayatollahi J, Ayatollahi F, Bahrololoomi R. Using the internet among dental students in Yazd. Dent Res J (Isfahan) 2010;7:7-11. |
|4.||Ko CH, Yen JY, Chen CS, Chen CC, Yen CF. Psychiatric comorbidity of internet addiction in college students: An interview study. CNS Spectr 2008;13:147-53. |
|5.||Swaminath G. Internet addiction disorder: Fact or Fad? Nosing into Nosology. Indian J Psychiatry 2008;50:158-60. |
|6.||Dargahi H, Razavi SM. Internet addiction and its related factors: A study of an Iranian population. Payesh 2007;6:265-72. |
|7.||Vizeshfar F. Assessment of the internet addiction between larian net users. Fundamentals of Mental Health 2005;7 (25-26):27-33. |
|8.||Young KS. Caught in the net: How to recognize the sings of internet addiction and a winning strategy for recovery. 1 st Ed, New York: John wiley and Sons; 1998. |
|9.||Sunwoo K, Rando K. A study of internet addiction: Status Causes and remedies. J Korean Home Eco Assoc 2002:3:1-19. |
|10.||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 Health 2007;56:137-44. |
|11.||Deng YX, Hu M, Hu GQ, Wang LS, Sun ZQ. An investigation on the prevalence of internet addiction disorder in middle school students of Hunan province. Zhonghua Liu Xing Bing Xue Za Zhi 2007;28:445-8. |
|12.||Johansson A, Götestam KG. Internet addiction: Characteristics of a questionnaire and prevalence in Norwegian youth (12- 18 years). Scand J Psychol 2004;45:223-9. |
|13.||June KJ, Sohn SY, So AY, Yi GM, Park SH. A study of factors that influence Internet addiction, smoking, and drinking in high school students. Taehan Kanho Hakhoe Chi 2007;37:872-82. |
|14.||Tsai HF, Cheng SH, Yeh TL, Shih CC, Chen KC, Yang YC, et al. The risk factors of Internet addiction-a survey of university freshmen. Psychiatry Res 2009;167:294-9. |
|15.||Bao XM. Challenges and opportunities: A report of the 1998 library survey of internet users at Seton Hall University. Coll Res Libr 1998;59:535-43. |
|16.||Ur Rehman S, Ramzy V. Internet use by health professionals at the Health Sciences Centre of Kuwait University. Online Inf Rev 2004;28:53-60. |
|17.||Lam LT, Peng ZW, Mai JC, Jing J. Factors Associated with Internet Addiction among Adolescents. Cyberpsychol Behav 2009;12:551-5. |
|18.||Ceyhan AA. Predictors of problematic Internet use on Turkish university students. Cyberpsychol Behav 2008;11:363-6. |
|19.||Yang LS, Zhang ZH, Hao JH, Sun YH. [Association between adolescent internet addiction and suicidal behaviors]. Zhonghua Liu Xing Bing Xue Za Zhi 2010;31:1115-9. |
|20.||Mohagheghzadeh MS, Abdollahi M. Scopes and using type of internet center members of Shiraz university of medical sciences about center facilities and its impact on research works of them. Commun Sci 2002:18:1-10. |
|21.||Ershad Sarabi R, Mirzazadeh A. The rate of using internet and full-text databases by academic members referring to the IT center of Kerman University of medical sciences. Strides Dev Med Educ 2007;4:57-63. |
|22.||Widyanto L, McMurran M. The psychometric properties of the internet addiction test. Cyberpsychol Behav 2004;7:443-50. |
|23.||Eitel DR, Yankowitz J, Ely JW. Use of Internet technology by obstetricians and family physicians. JAMA 1998;280:1306-7. |
|24.||Ni X, Yan H, Chen S, Liu Z. Factors influencing internet addiction in a sample of freshmen university students in China. Cyberpsychol Behav 2009;12:327-30. |
|25.||Siomos KE, Dafouli ED, Braimiotis DA, Mouzas OD, Angelopoulos NV. Internet Addiction among Greek Adolescent Students. Cyberpsychol Behav 2008;11:653-7. |
|26.||Mashayekhi M, Borjali A. Internet use and feeling of loneliness among high school studies. Adv Cogn Sci 2003;5;39-44. |
|27.||Ghassemzadeh L, Shahraray M, Moradi A. Prevalence of internet addiction and comparison of internet addicts and non-addicts in Iranian High Schools. Cyberpsychol Behav 2008;11:731-3. |
[Table 1], [Table 2], [Table 3], [Table 4]
|This article has been cited by|
||Psychosocial Correlates of Internet Addiction Among Jordanian University Students
| ||Abdulkarim Alzayyat,Ekhlas Al-Gamal,Muayyad M. Ahmad |
| ||Journal of Psychosocial Nursing and Mental Health Services. 2015; 53(4): 43 |
|[Pubmed] | [DOI]|
||Internet Addiction in Greek Medical Students: an Online Survey
| ||Zoi Tsimtsiou,Anna-Bettina Haidich,Dimitris Spachos,Stamatia Kokkali,Panagiotis Bamidis,Theodoros Dardavesis,Malamatenia Arvanitidou |
| ||Academic Psychiatry. 2015; |
|[Pubmed] | [DOI]|
||Prevalence of Internet Addiction and Associated Factors Among Medical Students From Mashhad, Iran in 2013
| ||Maryam Salehi,Mina Norozi Khalili,Seyed Kaveh Hojjat,Mahta Salehi,Ali Danesh |
| ||Iranian Red Crescent Medical Journal. 2014; 16(5) |
|[Pubmed] | [DOI]|
||Internet addiction in students: Prevalence and risk factors
| ||Kuss, D.J. and Griffiths, M.D. and Binder, J.F. |
| ||Computers in Human Behavior. 2013; 29(3): 959-966 |
||Internet Addiction and its Predictors in Guilan Medical Sciences Students, 2012
| ||Shahla Asiri,Fatemeh Fallahi,Atefeh Ghanbari,Ehsan Kazemnejad-leili |
| ||Nursing and Midwifery Studies. 2013; 1(4): 234 |
|[Pubmed] | [DOI]|
||Internet addiction in students: Prevalence and risk factors
| ||Daria J. Kuss,Mark D. Griffiths,Jens F. Binder |
| ||Computers in Human Behavior. 2013; 29(3): 959 |
|[Pubmed] | [DOI]|