|Year : 2014 | Volume
| Issue : 1 | Page : 23-28
A cross-sectional online survey of compulsive internet use and mental health of young adults in Malaysia
Nizar A. M. Kutty1, Chandrashekhar T Sreeramareddy2
1 Department of Pre Clinical Sciences, Faculty of Medicine and Health Sciences, Tunku Abdul Rahman University, Selangor, Malaysia
2 Department of Population Medicine, Faculty of Medicine and Health Sciences, Tunku Abdul Rahman University, Selangor, Malaysia
|Date of Web Publication||14-Mar-2014|
Nizar A. M. Kutty
Department of Pre Clinical Sciences, Faculty of Medicine and Health Sciences, Tunku Abdul Rahman University, Jalan Sungai Long, Cheras, Kajang, Selangor 43000
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: The last decade has seen the emergence of the internet as the prime communication medium changing the way people live and interact. Studies from various countries have reported on internet addiction and its association with mental health, but none have come from Malaysia. Objectives: We aimed at assessing the frequency of the use of various internet applications and exploring the association of compulsive internet use with mental health and socio-demographic factors. Materials and Methods: A cross-sectional online survey was carried out among participants registered for the monthly opinion poll survey of University Tunku Abdul Rahman, Malaysia. The questionnaire contained socio-demographic information, the use of various internet applications on a five-point Likert scale, compulsive internet use scale (CIUS) and 12 item general health questionnaire (GHQ-12). Correlations and linear regression analyzes were carried out. Results: Of the 330 respondents, 182 were females and 148 were males. The mean age was 23.17 (SD = 3.84). Mean CIUS score was 19.85 (SD = 10.57) and mean GHQ score was 15.47 (SD = 6.29). Correlation coefficients of CIUS score with age, years of use and daily hours of internet use were −0.118 (P = 0.03), −0.014 (P = 0.81) and 0.242 (P < 0.001) respectively. Multiple linear regression analysis showed that age (β = −0.111, P = 0.033) and marital status (β = −0.124, P = 0.018) were negatively associated with CIUS scores whereas daily hours of internet use (β = 0.269, P = 0.001) and GHQ score (β = 0.259, P = 0.001) were positively associated with the CIUS score. Conclusions: Compulsive internet use was correlated with GHQ score. More research is needed to confirm our results. Psychologists may consider assessing internet addiction when evaluating young psychiatric patients.
Keywords: Internet addiction, mental health, well-being
|How to cite this article:|
Kutty NA, Sreeramareddy CT. A cross-sectional online survey of compulsive internet use and mental health of young adults in Malaysia. J Fam Community Med 2014;21:23-8
|How to cite this URL:|
Kutty NA, Sreeramareddy CT. A cross-sectional online survey of compulsive internet use and mental health of young adults in Malaysia. J Fam Community Med [serial online] 2014 [cited 2019 Aug 18];21:23-8. Available from: http://www.jfcmonline.com/text.asp?2014/21/1/23/128770
| Background|| |
Internet has become a ubiquitous feature at home, school and the work place as the world-wide population of internet users has grown rapidly in the recent years. Internet user population world-wide has doubled from 1.15 billion to 2.27 billion and has grown from 418 million to 1 billion in the last 5 years in Asia.  Rapid expansion and proliferation of the internet has provided better opportunities for communication, information and social interaction. However, the excessive undisciplined use by, some individuals has led to the emergence of the concept of internet addiction. ,, The proposed diagnostic criteria for internet addiction are obsessive thoughts about internet and the use of the internet more than is intended or despite its negative consequences including withdrawal symptoms and tolerance. , Studies have shown that young internet users and males have a higher risk of addiction. ,, This addiction has been linked to intermediate psychosocial variables such as avoidance, (escapism) male gender  and applications such as online games. 
Excessive use and pre-occupation about the internet has been shown to affect day-to-day life activities, time management, marriage, sexual life, productivity at work, education and academic work. ,,, Studies have shown that excessive use of the internet is associated with such psychiatric conditions as anxiety, depression, social phobia, impulsiveness and substance abuse, ,,,, as well as the induction of seizures, insufficient sleep and social isolation. ,
An increasing number of young Malaysians are resorting to the internet as the medium of choice for information and entertainment. A study by an international marketing firm Taylor Nelson Sofres which observed the habits and behavior of internet users found that the largest number of social network partners belonged to Malaysian internet users with the highest average number of 233 digital friends when compared with their peers from other countries.  Another survey by Microsoft conducted among 2800 internet users in 11 countries in the Asia-pacific region has shown that 71% of Malaysian internet users spend more than 1 h/day on cyber social activities and that the largest number of internet friends on Facebook belonged to Malaysian internet users.  Studies from various countries have reported on internet addiction, its association with mental health, yet no reports have come from Malaysia. Therefore, an online survey was carried out of regular internet users to assess the frequency of the use of various internet applications and explore the association of compulsive internet use with mental health and socio-demographic factors.
| Materials and Methods|| |
Design and setting
A cross-sectional online survey was carried out by University Tunku Abdul Rahman (UTAR). The UTAR opinion poll survey is an initiative undertaken by a committee with representatives from all faculties of the university. Every month, the committee plans and executes opinion poll surveys on various topics of general interest about the youth in Malaysia (http://poll.utar.edu.my). The registered UTAR opinion poll survey participants are young adults from various parts of Malaysia, most of whom are young university students with access to the internet. Sampling method and sample size calculation were not used since this was an exploratory study.
Data was collected through UTAR opinion poll survey of February, 2012. Invitations by E-mail were sent out to all the registered participants. E-mail with all the instructions, provided a link to the survey. The survey also provided an option for participants to invite their friends to take part. Consent was taken from the participants at the time of registration. Confidentiality and anonymity about the survey responses were assured for all the participants. All UTAR opinion poll surveys are approved by the research ethics committee of UTAR.
The online survey questionnaire had sections on demographic information such as age, gender, marital status, monthly income, educational status, employment status, questions on availability of internet at the work place and the typical number of hours spent online per day and applications or the purpose for which the internet is used. Frequency of using various internet applications such as gaming, social network, E-mails etc., were assessed on a five-point Likert scale as "never", "seldom", "sometimes", "often" and "always". The survey also contained a 12-item general health questionnaire (GHQ-12) and compulsive internet use scale (CIUS).
GHQ is a widely used instrument for measuring the status of mental health. It can be administered in the general population as well as in young populations.  GHQ-12 designed by Goldberg, has reliability coefficients ranging from 0.78 to 0.95 in various studies with a well-established international validity.  GHQ-12 uses a four-point Likert scale. For example, the options given for each question in GHQ-12 and the scores assigned (as shown in parenthesis) are better than usual - (0), same as usual - (1), less than usual - (2) and much less than usual - (3). GHQ-12 scores are calculated by summing up the scores of all 12 items giving a score range of 0-36. A score >15 suggests evidence of distress and a score >20 suggests severe problems and psychological distress. GHQ-12 and its scoring is available at http://www.workhealth.org/UCI%202007/practicum%2008%20GHQ%202007.pdf.
CIUS is an easily administered, psychometrically sound, valid instrument used to assess the severity of compulsive internet use.  CIUS originates from an analysis of criteria for dependence and obsessive-compulsive disorder as found in the DSM-IV,  literature on behavioral addictions  and from qualitative research on self-declared internet addicts.  CIUS has shown good factorial stability across time, various samples and subsamples. It has a high internal consistency and high correlations with concurrent and criterion variables demonstrating its good validity. CIUS consists of 14 items ratable on a five-point Likert scale. For example, the options for each question and the scores assigned (as shown in parenthesis) are never - (0), seldom - (1), sometimes - (2), often - (3) and always - (4). Scores are calculated by summing up the scores of all 14 items giving a score range of 0-36. However, it has no cut-off point to categorize a participant as an internet addict. 
Data extracted into Microsoft excel was converted into Statistical Package for Social Sciences (SPSS Inc. Chicago, USA) version 19 for analysis. Descriptive statistics and proportions of participants with mild and severe psychological distress were calculated. The relationship of CIUS score with demographic factors and GHQ was explored using bivariate statistics. Correlation coefficients for GHQ score and CIUS score with age, daily hours and years of internet use were calculated. Mean GHQ and CIUS scores were compared with categories of demographic variables and the statistical significance of the difference in mean scores was tested using independent samples t-test or ANOVA as appropriate. The relationship of CIUS score with GHQ, daily hours and years of internet use and socio-demographic variables was tested by linear regression analysis. A P < 0.05 was considered as significant.
| Results|| |
Of the 330 respondents who completed the online survey, 185 (55.2%) were females and 145 (44.8%) were males with a mean age of 23.17 (SD = 3.84). Two hundred and three (61.5%) respondents were full-time students, 247 (74.8%) were single; 214 (64.8%) had a bachelor's degree and 204 (61.8%) had monthly income of <2000 Malaysian Ringgits (1 Malaysian Ringgit 0.32 US Dollar) [Table 1]. The average years of internet use was 8.7 (SD = 3.85) and the mean hours of daily internet use was 6.56 (SD = 3.73) [Table 2]. A majority of participants had an internet connection either at home (93.3%), or in college or at the work place (96.1%) and 62.7% had an internet connection on their mobile phones (data not shown).
|Table 1: Association of demographic factors with CIUS score and GHQ score |
Click here to view
|Table 2: Bivariate correlation coefficients of CIUS score and GHQ scores with age, durations of internet use and GHQ score |
Click here to view
The overall mean CIUS score was 19.85 (SD = 10.57) and the mean GHQ score was 15.47 (SD = 6.29). According to the recommended GHQ-12 cut-off scores, 55.5% (183/330) respondents showed evidence of psychological distress and 28.2% (93/330) had severe distress. Bivariate correlation between CIUS score and GHQ score was 0.249 (P < 0.001). Mean CIUS scores were compared with categories of socio-demographic variables of gender, education, income, marital status and job status. Of these, the mean CIUS scores were significantly high among respondents who had a master's or a higher degree [Table 1]. CIUS score was compared with age, years of internet use and the number of hours the internet is used daily, using bivariate correlations and the correlation coefficients were − 0.118, −0.014 and 0.242 respectively. All the correlations were weak, but statistically significant for age (P = 0.03) and hours of internet use daily (P < 0.001) [Table 1]. A comparison of the mean GHQ scores among categories of gender, education, income, marital status and job status exhibited no statistically significant differences. A comparison of GHQ score with age, years of internet use and daily hours of internet use by bivariate correlations gave correlation coefficients of 0.040, 0.033 and −0.017 respectively suggesting that there was no correlation and no statistical significance (P > 0.05) [Table 2].
A majority of the participants responded that they often/always used the internet for social networking (85.5%) and personal E-mails (78.5%), followed by work-related surfing (67.2%) and general information search (63.9%). The frequency of the use of the YouTube and movie websites on the internet and to download (Software, movies, music etc.) reported as often/always was 54.2% [Table 3].
By multiple linear regression analysis age (β = −0.111, P = 0.033) and marital status (β = −0.124, P = 0.018) were negatively associated with CIUS score whereas daily hours of internet use (β = 0.269, P = 0.001) and GHQ score (β = 0.259, P = 0.001) were positively associated with CIUS score after adjusting for the effect of socio-demographic variables [Table 4].
|Table 4: Association of CIUS score with GHQ-12 score, age, sex, marital status and hours of internet use per day |
Click here to view
| Discussion|| |
Social networking, personal E-mails and general information search were the most frequently used internet applications. Mean CIUS score was not high, but its range was wide and weakly correlated with daily hours of internet use, age and marital status and GHQ scores. By GHQ case criterion, nearly a quarter of the respondents had severe psychological distress, but the proportion with internet addiction could not be reported since unlike the more recent internet addiction tests, CIUS does not have a cut-off score. We, therefore, could not report prevalence of internet addiction. However, studies using internet addiction test have reported the prevalence of internet addiction of adolescents from Taiwan as 31 (19.8%), China 32 (6.44%), Greece 33 (11.6%), Japan 34 (8-10%) and Malaysia 35 (29%). ,,,
The pattern of internet usage in our study was similar to the findings of a survey by Accenture (a multinational management consulting, technology services and outsourcing company) which has reported that social network sites usage (SNS) was the "preferred" method of communication among adolescents in Malaysia, which ranks first as the most SNS active country (Malaysian Communications and Multimedia Commission).  Similar to the results of our study and those from USA  and Korea,  the same study also showed that SNS usage had a significant negative effect on psychological well-being. Other studies about mental health and internet use have reported that mental health problems such as depression, low self-esteem, high level of stress, distorted view of body image, loneliness and prior addictions increased the risk of internet addiction. ,,,,,,
CIUS scores in the higher range are consistent with the frequency of internet addiction reported in studies from China , and Korea.  Research has suggested that the ease of access will tend to facilitate the use of internet from home, school/the work place, or mobile networks. This convenience has encouraged the use of the internet to the extent of addictive behavior.  Mann has proposed "availability as a law of addiction." It is reasonable to argue that easy access to the internet may be another possible risk factor for addiction.  The internet is an essential tool for learning in the modern education system, but a study from Taiwan  has reported availability of the internet as a possible risk factor for addiction among college students. Nevertheless, it is not easy to determine the optimal use of internet or put restrictions on the use of various applications.
In our study, the number of hours of daily internet use was associated with CIUS score, but years of internet use was not since most (>90%) of our participants had an internet connection at home or place of work/study, increasing the likelihood of their using it for longer hours. Our findings of positive correlation between CIUS and GHQ scores are in agreement with the emerging concept of association between internet addiction and mental health. ,,,,,,
As cross-sectional design can only identify the views and reported behavior of respondents at a particular point in time. The associations we found lacked temporal relation. Further research to observe actual behavior or a prospective study to follow-up the internet users might be helpful. Despite online anonymous survey, some responses may have been biased leading to a reporting bias. The sample surveyed might have been relatively healthy, accounting for relatively low to moderate correlations found between the CIUS score and the GHQ score. Only about 30% of registered participants responded to the survey, but there was no information about those who did not complete the survey (non-respondents) for us to assess how non-response may have affected our results. Future research directed at answering questions on the relationship between problematic or addictive use of the internet and mental health should include a more heterogeneous sample, in terms of age, occupation (i.e. non-students) and education.
| Conclusion|| |
Compulsive internet use was weakly correlated with GHQ score and hours of internet use daily. Psychologists may assess the level of internet addiction when evaluating young psychiatric patients. Internet users should be educated about the ill effects of over-use and ways of spending time away from internet. Our findings should be verified in larger studies.
| Acknowledgment|| |
The authors thank the members of the UTAR Opinion POLL research committee for their approval to conduct the survey on this topic, their feedback on content and design of the survey questionnaire and for designing the online survey. We also, thank all the participants for their valuable time given for the survey.
| References|| |
|1.||Internet World Stats: Internet world users by language, 2011. Available from: http://www.internetworldstats.com/stats7.htm. [Last accessed on 2013 Sep 20]. |
|2.||Griffiths M, Wood RT. Risk factors in adolescence: The case of gambling, videogame playing, and the internet. J Gambl Stud 2000;16:199-225. |
|3.||Young KS, Case CJ. Internet abuse in the workplace: New trends in risk management. Cyberpsychol Behav 2004;7:105-11. |
|4.||Liu T, Potenza MN. Problematic internet use: Clinical implications. CNS Spectr 2007;12:453-66. |
|5.||Ko CH, Yen JY, Chen SH, Yang MJ, Lin HC, Yen CF. Proposed diagnostic criteria and the screening and diagnosing tool of internet addiction in college students. Compr Psychiatry 2009;50:378-84. |
|6.||Tao R, Huang X, Wang J, Zhang H, Zhang Y, Li M. Proposed diagnostic criteria for internet addiction. Addiction 2010;105:556-64. |
|7.||Widyanto L, McMurran M. The psychometric properties of the internet addiction test. Cyberpsychol Behav 2004;7:443-50. |
|8.||Ferraro G, Caci B, D'Amico A, Di Blasi M. Internet addiction disorder: An Italian study. Cyberpsychol Behav 2007;10:170-5. |
|9.||Choi K, Son H, Park M, Han J, Kim K, Lee B, et al. Internet overuse and excessive daytime sleepiness in adolescents. Psychiatry Clin Neurosci 2009;63:455-62. |
|10.||Billieux J, Chanal J, Khazaal Y, Rochat L, Gay P, Zullino D, et al. Psychological predictors of problematic involvement in massively multiplayer online role-playing games: Illustration in a sample of male cybercafé players. Psychopathology 2011;44:165-71. |
|11.||Ha JH, Kim SY, Bae SC, Bae S, Kim H, Sim M, et al. Depression and Internet addiction in adolescents. Psychopathology 2007;40:424-30. |
|12.||Krajewska-Ku³ak E, Ku³ak W, Marcinkowski JT, Damme-Ostapowicz KV, Lewko J, Lankau A, et al. Internet addiction among students of the medical university of bia³ystok. Comput Inform Nurs 2011;29:657-61. |
|13.||Brenner V. Psychology of computer use: XLVII. Parameters of Internet use, abuse and addiction: The first 90 days of the Internet Usage Survey. Psychol Rep 1997;80:879-82. |
|14.||Young KS. Internet addiction: The emergence of a new clinical disorder. Cyberpsychol Behav 1998;1:237-44. |
|15.||Grant JE, Potenza MN, Weinstein A, Gorelick DA. Introduction to behavioral addictions. Am J Drug Alcohol Abuse 2010;36:233-41. |
|16.||Yen JY, Ko CH, Yen CF, Chen SH, Chung WL, Chen CC. Psychiatric symptoms in adolescents with Internet addiction: Comparison with substance use. Psychiatry Clin Neurosci 2008;62:9-16. |
|17.||Yen JY, Ko CH, Yen CF, Wu HY, Yang MJ. The comorbid psychiatric symptoms of Internet addiction: Attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. J Adolesc Health 2007;41:93-8. |
|18.||Baer S, Bogusz E, Green DA. Stuck on screens: Patterns of computer and gaming station use in youth seen in a psychiatric clinic. J Can Acad Child Adolesc Psychiatry 2011;20:86-94. |
|19.||Batthyány D, Müller KW, Benker F, Wölfling K. Computer game playing: Clinical characteristics of dependence and abuse among adolescents. Wien Klin Wochenschr 2009;121:502-9. |
|20.||Cao F, Su L, Liu T, Gao X. The relationship between impulsivity and Internet addiction in a sample of Chinese adolescents. Eur Psychiatry 2007;22:466-71. |
|21.||Chuang YC. Massively multiplayer online role-playing game-induced seizures: A neglected health problem in internet addiction. Cyberpsychol Behav 2006;9:451-6. |
|22.||Bélanger RE, Akre C, Berchtold A, Michaud PA. A U-shaped association between intensity of Internet use and adolescent health. Pediatrics 2011;127:e330-5. |
|23.||Zamhari A. Malaysia punyai rakan digital paling ramai. Taylor Nelson Sofres (TNS), Malaysiakini; Oct:2010. |
|24.||Haghighi BT, Othman M, Hashim FH. Internet Addiction and dependency: A case study in UNITEN, Malaysia: IEEE; 2011. p. 1-4. |
|25.||Banks MH. Validation of the General Health Questionnaire in a young community sample. Psychol Med 1983;13:349-53. |
|26.||Goldberg D. General Health Questionnaire (GHQ-12). Windsor, UK: Nfer-Nelson; 1992. |
|27.||Meerkerk GJ, Van Den Eijnden RJ, Vermulst AA, Garretsen HF. The compulsive internet use scale (CIUS): Some psychometric properties. Cyberpsychol Behav 2009;12:1-6. |
|28.||American Psychiatric Association and American Psychiatric Association. Task Force on DSM-IV. Diagnostic and Statistical Manual of Mental Disorders: DSM-IV. Amer: Psychiatric Pub Inc.; 1994. |
|29.||Griffiths M. Internet addiction: Fact or fiction? Psychologist 1999;12:246-50. |
|30.||Tsai CC, Lin SS. Internet addiction of adolescents in Taiwan: An interview study. Cyberpsychol Behav 2003;6:649-52. |
|31.||Ko CH, Yen JY, Yen CF, Chen CC, Yen CN, Chen SH. Screening for Internet addiction: An empirical study on cut-off points for the Chen internet addiction scale. Kaohsiung J Med Sci 2005;21:545-51. |
|32.||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. |
|33.||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. |
|34.||Sato T. Internet addiction among students: Prevalence and psychological problems in Japan. Jpn Med Assoc J 2006;49:279-83. |
|35.||Malaysian Communications and Multimedia Commission. Q2 2008 Communications and Multimedia: Selected Facts and Figures. Quarterly Report by the Malaysian Communications and Multimedia Commission, Cyberjaya, Selangor, Malaysia; 2008. |
|36.||Sinha R. Chronic stress, drug use, and vulnerability to addiction. Ann N Y Acad Sci 2008;1141:105-30. |
|37.||Yoo YS, Cho OH, Cha KS. Associations between overuse of the internet and mental health in adolescents. Nurs Health Sci 2013; [In Press]. |
|38.||Chou C, Hsiao MC. Internet addiction, usage, gratification, and pleasure experience: The Taiwan college students' case. Comput Educ 2000;35:65-80. |
|39.||Wu HR, Zhu KJ. Path analysis on related factors causing internet addiction disorder in college students. Chin J Public Health 2004;20:1363-4. |
|40.||Ling CS, Ramadass S, Altehar A, Arjuman NC. Malaysian internet surfing addiction (MISA): Factors affecting the internet use and its consequences. In: Computer Applications and Industrial Electronics (ICCAIE), 2011 IEEE International Conference on, IEEE, 2011. p. 585-90. |
|41.||Mann RE. Availability as a law of addiction. Addiction 2005;100:924-5. |
|42.||Lin MP, Ko HC, Wu JY. Prevalence and psychosocial risk factors associated with internet addiction in a nationally representative sample of college students in Taiwan. Cyberpsychol Behav Soc Netw 2011;14:741-6. |
[Table 1], [Table 2], [Table 3], [Table 4]
|This article has been cited by|
||Risk factors contributing to vulnerability of mental health disorders and the protective factors among Malaysian youth
| ||Jin Kuan Kok,Sew Kim Low |
| ||International Journal of School & Educational Psychology. 2019; 7(2): 102 |
|[Pubmed] | [DOI]|
||Views of young people in Malaysia on mental health, help-seeking and unusual psychological experiences
| ||Clio Berry,Daniel Michelson,Ellisha Othman,Jun C. Tan,Brioney Gee,Joanne Hodgekins,Rory E. Byrne,Alvin L. O. Ng,Nigel V. Marsh,Sian Coker,David Fowler |
| ||Early Intervention in Psychiatry. 2019; |
|[Pubmed] | [DOI]|
||Measuring internet use: comparisons of different assessments and with internet addiction
| ||Stéphanie Baggio,Katia Iglesias,André Berchtold,Joan-Carles Suris |
| ||Addiction Research & Theory. 2016; : 1 |
|[Pubmed] | [DOI]|
||Differences between specific and generalized problematic Internet uses according to gender, age, time spent online and psychopathological symptoms
| ||Stéphanie Laconi,Nathalie Tricard,Henri Chabrol |
| ||Computers in Human Behavior. 2015; 48: 236 |
|[Pubmed] | [DOI]|
||Nutritional Habits among Internet Users in a Private Malaysian Medical School
| ||Mohammed A. Abdal Qader,Hasanain Faisal Ghazi,Zaleha Md. Isa,Tiba Nezar Hasan,Mustafa Fadil Mohammed |
| ||Pakistan Journal of Nutrition. 2015; 14(7): 409 |
|[Pubmed] | [DOI]|