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Wen Ting Tong, Md. Ashraful Islam, Wah Yun Low, Wan Yuen Choo, and Adina Abdullah
63
Prevalence and Determinants of Pathological Internet Use among
Undergraduate Students in a Public University in Malaysia
Wen Ting Tong1, Md. Ashraful Islam2, Wah Yun Low3, Wan Yuen Choo4,
and Adina Abdullah5
Pathological Internet Use (PIU) affects one’s physical and mental health, and university
students are at risk as they are more likely to develop PIU. This study determines the
prevalence of PIU and its associated factors among students in a public university in
Malaysia. This cross-sectional study was conducted among 1023 undergraduate students in
2015. The questionnaire comprised of items from the Young’s Diagnostic Questionnaire to
assess PIU and items related to socio-demography, psychosocial, lifestyle and co-morbidities.
Anonymous paper-based data collection method was adopted. Mean age of the respondents
was 20.73 ± 1.49 years old. The prevalence of pathological Internet user was 28.9% mostly
Chinese (31%), 22 years old and above (31.0%), in Year 1 (31.5%), and those who perceived
themselves to be from family from higher socio-economic status (32.5%). The factors found
statistically significant (p<0.05) with PIU were Internet use for three or more hours for
recreational purpose (OR: 3.89; 95% CI:1.33 – 11.36), past week of Internet use for
pornography purpose (OR: 2.52; 95% CI:1.07 – 5.93), having gambling problem (OR: 3.65;
95% CI:1.64 – 8.12), involvement in drug use in the past 12 months (OR: 6.81; 95% CI:1.42
– 32.77) and having moderate/severe depression (OR: 4.32; 95% CI:1.83 – 10.22). University
authorities need to be aware of the prevalence so that interventions can be developed to
prevent adverse outcomes. Interventions should focus on screening students for PIU, creating
awareness on the negative effects of PIU and promoting healthy and active lifestyle and
restricting students’ access to harmful websites.
Keywords: internet addiction, prevalence, risk factors, tertiary students, Malaysia
In this digital world, the growing Internet use has led to problematic behavior such as
excessive use and several terms have been coined to describe such behavior such as Internet
addiction (IA), Internet dependence, problematic Internet use, compulsive Internet use,
pathological Internet use (PIU), excessive Internet use (Rial Boubeta et al. 2015). PIU is when
a person has excessive or poorly-controlled preoccupations, urges or behaviors related to
Internet use resulting in impairment and distress to their life (Shaw & Black, 2008).
In the 5th edition of the Diagnostic and Statistical Manual of Mental Disorder (DSM-
5), the American Psychiatric Association (APA, 2012) has included Internet Use Disorder as
their clinical diagnosis. In this paper, PIU is used to define someone with Internet problem
with a potentially pathological behavioral problem and does not refer to a clinical diagnosis,
since the instrument used in this study to assess Internet problem is based on a screening tool.
Also PIU is a preferred term as compared to IA where the latter refers to dependency on
psychoactive substances (Davis, 2001).
1 Research Assistant, Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala
Lumpur, Malaysia
2 Research Fellow, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
3 Professor, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia. Email: lowwy@um.edu.my
4 Associate Professor, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya,
Kuala Lumpur, Malaysia
5 Lecturer, Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur,
Malaysia
The Journal of Behavioral Science Copyright © Behavioral Science Research Institute
2019, Vol. 14, Issue 1, 63-83 ISSN: 1906-4675 (Print), 2651-2246 (Online)
63
Prevalence and Determinants of Pathological Internet Use among
Undergraduate Students in a Public University in Malaysia
Wen Ting Tong1, Md. Ashraful Islam2, Wah Yun Low3, Wan Yuen Choo4,
and Adina Abdullah5
Pathological Internet Use (PIU) affects one’s physical and mental health, and university
students are at risk as they are more likely to develop PIU. This study determines the
prevalence of PIU and its associated factors among students in a public university in
Malaysia. This cross-sectional study was conducted among 1023 undergraduate students in
2015. The questionnaire comprised of items from the Young’s Diagnostic Questionnaire to
assess PIU and items related to socio-demography, psychosocial, lifestyle and co-morbidities.
Anonymous paper-based data collection method was adopted. Mean age of the respondents
was 20.73 ± 1.49 years old. The prevalence of pathological Internet user was 28.9% mostly
Chinese (31%), 22 years old and above (31.0%), in Year 1 (31.5%), and those who perceived
themselves to be from family from higher socio-economic status (32.5%). The factors found
statistically significant (p<0.05) with PIU were Internet use for three or more hours for
recreational purpose (OR: 3.89; 95% CI:1.33 – 11.36), past week of Internet use for
pornography purpose (OR: 2.52; 95% CI:1.07 – 5.93), having gambling problem (OR: 3.65;
95% CI:1.64 – 8.12), involvement in drug use in the past 12 months (OR: 6.81; 95% CI:1.42
– 32.77) and having moderate/severe depression (OR: 4.32; 95% CI:1.83 – 10.22). University
authorities need to be aware of the prevalence so that interventions can be developed to
prevent adverse outcomes. Interventions should focus on screening students for PIU, creating
awareness on the negative effects of PIU and promoting healthy and active lifestyle and
restricting students’ access to harmful websites.
Keywords: internet addiction, prevalence, risk factors, tertiary students, Malaysia
In this digital world, the growing Internet use has led to problematic behavior such as
excessive use and several terms have been coined to describe such behavior such as Internet
addiction (IA), Internet dependence, problematic Internet use, compulsive Internet use,
pathological Internet use (PIU), excessive Internet use (Rial Boubeta et al. 2015). PIU is when
a person has excessive or poorly-controlled preoccupations, urges or behaviors related to
Internet use resulting in impairment and distress to their life (Shaw & Black, 2008).
In the 5th edition of the Diagnostic and Statistical Manual of Mental Disorder (DSM-
5), the American Psychiatric Association (APA, 2012) has included Internet Use Disorder as
their clinical diagnosis. In this paper, PIU is used to define someone with Internet problem
with a potentially pathological behavioral problem and does not refer to a clinical diagnosis,
since the instrument used in this study to assess Internet problem is based on a screening tool.
Also PIU is a preferred term as compared to IA where the latter refers to dependency on
psychoactive substances (Davis, 2001).
1 Research Assistant, Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala
Lumpur, Malaysia
2 Research Fellow, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
3 Professor, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia. Email: lowwy@um.edu.my
4 Associate Professor, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya,
Kuala Lumpur, Malaysia
5 Lecturer, Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur,
Malaysia
The Journal of Behavioral Science Copyright © Behavioral Science Research Institute
2019, Vol. 14, Issue 1, 63-83 ISSN: 1906-4675 (Print), 2651-2246 (Online)
Pathological Internet Use among Undergraduate Students
64
Currently, most studies on PIU have been focused largely in Europe and US where
PIU has been a prominent issue in the adolescent health literature. Internet addiction has
become more prevalent in Asia than in other parts of the world (Yen, Yen, & Ko, 2010). In a
meta-analysis of 31 nations across seven world regions, the global prevalence of Internet
addiction was 6.0% (Cheng & Li, 2014). In East Asian countries, most studies were from
Taiwan, China, Korea and Singapore, however, literature are scarce in other Asian
counterparts (Kuss, Griffiths, & Binder, 2013; Lam, 2014). In Asia, there is higher variation
in prevalence among young people and adolescents, ranging from 8% to 50.9% (Kim et al.,
2006; Mak et al., 2014). In China, the rates ranged from 6% to 26.5% (Cao et al., 2011, Lai et
al., 2013, Wu et al., 2013, Chi, Lin & Zhang, 2016; Xin et al., 2018). In one study among
adolescents in six Asian countries, namely China, Hong Kong, Japan, South Korea, Malaysia
and the Philippines, there were variations in internet behaviors and addiction across these
countries (Mak et al., 2014). Further, the study found that the prevalence of addictive Internet
use ranges from 1% in South Korea to 5% in the Philippines, and the prevalence of
problematic Internet use ranges from 13% in South Korea to 46% in the Philippines, as
measured by the Internet Addiction Test (IAT). Further, based on the Revised Chen Internet
Addiction Scale (CTAS-R), the prevalence of addictive Internet use in the six countries are as
follows: Philippines (21%), Hong Kong (16%), Malaysia (14%), South Korea (10%), China
(10%) and Japan (6%) (Mak et al., 2014). Elsewhere, a cross-sectional study in five ASEAN
countries (Indonesia, Malaysia, Myanmar, Thailand and Vietnam), the overall prevalence of
pathological Internet use was 35.9% (ranging from 16.1% in Myanmar to 52.4% in Thailand),
maladaptive use 34.8% and adjusted Internet users 29.9% (Turnbull et al., 2018). Among
these five ASEAN countries, the highest prevalence of pathological Internet use is Thailand
(52.4%) followed by Indonesia (38.5%), Vietnam (37.5%), Malaysia (28.9%), and Myanmar
(16.1%) (Turnbull et al., 2018). Other parts of Asia, in Nepal among undergraduate students,
the prevalence rate of Internet addiction was 35.4% (Bhandari et al., 2017). In Japan, among
junior and high school students, the prevalence of Internet addiction was 8.1% (Marioka et al.,
2017), in South India, among 2776 University students, the prevalence was 29.9% for mild
Internet addiction, 16.4% for moderate addictive use and 0.5% for severe Internet addiction
(Anand et al., 2018). In Asia, Internet use is indeed a problematic issue, and a public health
concern.
Malaysia, a multiethnic country located in Southeast Asia, is not without its negative
consequences of technological advancement. As the country becomes more advanced,
developed, and technologically savvy, this comes with a price. Based on the Malaysian
Communication and Multimedia Commission (MCMC), Internet addiction among Malaysians
has reached an alarming rate. According to the MCMC (2017), smartphones are the most
common device to access the Internet (89.4%), with 57.4% of users being male, and 67.2%
being from urban areas. Additionally, 83.2% of children aged 5 – 17 use the internet. In the
Malaysian Internet User survey (MCMC, 2015), university/college students comprised of
62.5% of internet users who were schooling. 80% accessed the web for social media usage
with the average usage period being over four hours a day, and 89% were found to be
addicted to the Internet. Further, 60% of the respondents showed elevated levels of anxiety
and 32% suffered from major depression. These findings are a cause for concern with
negative implications for the individual, family and the community.
64
Currently, most studies on PIU have been focused largely in Europe and US where
PIU has been a prominent issue in the adolescent health literature. Internet addiction has
become more prevalent in Asia than in other parts of the world (Yen, Yen, & Ko, 2010). In a
meta-analysis of 31 nations across seven world regions, the global prevalence of Internet
addiction was 6.0% (Cheng & Li, 2014). In East Asian countries, most studies were from
Taiwan, China, Korea and Singapore, however, literature are scarce in other Asian
counterparts (Kuss, Griffiths, & Binder, 2013; Lam, 2014). In Asia, there is higher variation
in prevalence among young people and adolescents, ranging from 8% to 50.9% (Kim et al.,
2006; Mak et al., 2014). In China, the rates ranged from 6% to 26.5% (Cao et al., 2011, Lai et
al., 2013, Wu et al., 2013, Chi, Lin & Zhang, 2016; Xin et al., 2018). In one study among
adolescents in six Asian countries, namely China, Hong Kong, Japan, South Korea, Malaysia
and the Philippines, there were variations in internet behaviors and addiction across these
countries (Mak et al., 2014). Further, the study found that the prevalence of addictive Internet
use ranges from 1% in South Korea to 5% in the Philippines, and the prevalence of
problematic Internet use ranges from 13% in South Korea to 46% in the Philippines, as
measured by the Internet Addiction Test (IAT). Further, based on the Revised Chen Internet
Addiction Scale (CTAS-R), the prevalence of addictive Internet use in the six countries are as
follows: Philippines (21%), Hong Kong (16%), Malaysia (14%), South Korea (10%), China
(10%) and Japan (6%) (Mak et al., 2014). Elsewhere, a cross-sectional study in five ASEAN
countries (Indonesia, Malaysia, Myanmar, Thailand and Vietnam), the overall prevalence of
pathological Internet use was 35.9% (ranging from 16.1% in Myanmar to 52.4% in Thailand),
maladaptive use 34.8% and adjusted Internet users 29.9% (Turnbull et al., 2018). Among
these five ASEAN countries, the highest prevalence of pathological Internet use is Thailand
(52.4%) followed by Indonesia (38.5%), Vietnam (37.5%), Malaysia (28.9%), and Myanmar
(16.1%) (Turnbull et al., 2018). Other parts of Asia, in Nepal among undergraduate students,
the prevalence rate of Internet addiction was 35.4% (Bhandari et al., 2017). In Japan, among
junior and high school students, the prevalence of Internet addiction was 8.1% (Marioka et al.,
2017), in South India, among 2776 University students, the prevalence was 29.9% for mild
Internet addiction, 16.4% for moderate addictive use and 0.5% for severe Internet addiction
(Anand et al., 2018). In Asia, Internet use is indeed a problematic issue, and a public health
concern.
Malaysia, a multiethnic country located in Southeast Asia, is not without its negative
consequences of technological advancement. As the country becomes more advanced,
developed, and technologically savvy, this comes with a price. Based on the Malaysian
Communication and Multimedia Commission (MCMC), Internet addiction among Malaysians
has reached an alarming rate. According to the MCMC (2017), smartphones are the most
common device to access the Internet (89.4%), with 57.4% of users being male, and 67.2%
being from urban areas. Additionally, 83.2% of children aged 5 – 17 use the internet. In the
Malaysian Internet User survey (MCMC, 2015), university/college students comprised of
62.5% of internet users who were schooling. 80% accessed the web for social media usage
with the average usage period being over four hours a day, and 89% were found to be
addicted to the Internet. Further, 60% of the respondents showed elevated levels of anxiety
and 32% suffered from major depression. These findings are a cause for concern with
negative implications for the individual, family and the community.
Wen Ting Tong, Md. Ashraful Islam, Wah Yun Low, Wan Yuen Choo, and Adina Abdullah
65
Other local Malaysian studies also showed a variation in the prevalence rates of
Internet addiction due to the methodology employed. Cheng and Li’s (2014) meta-analysis of
31 nations across seven regions in the world, among 12-18 years old adolescents, 2.4% of
Malaysian adolescents were reported being addicted to Internet, and 35.1% were found
having problematic Internet use. In one study among secondary school students, 28.6% of the
respondents were addicted to the Internet (Mohd Isa, Hashim, Kaur, & Ng, 2016). Yet, in
another study among undergraduate students in a public university, the prevalence of Internet
addiction was 7.8% and 56.5% were problematic Internet users (Rosliza, Ragubathi,
Mohamad Yusoff, & Shaharuddin, 2018). Zainudin, Md Din, & Othman, (2013) also in their
study among undergraduate students, found 30% prevalence of excessive Internet users.
Among local medical students, a study showed a prevalence of 36.9% Internet addiction
(Ching et al., 2017). As to the impact of Internet addiction on young Malaysian adults, (Alam
et al., 2014) showed those adults using Internet excessively were having problems, such as,
interpersonal, behavioral, physical, psychological and work problems in their daily lives.
Internet addiction in adolescents and young adults has become a public health issue
and has an impact on health education and health promotion. Excessive and inappropriate use
of the Internet can pose serious negative consequences on one’s mental health and quality of
life (Kuss & Griffiths, 2012, Alam et al., 2014). Thus, this paper examined the prevalence of
pathological internal use among university students in Kuala Lumpur and its associated
factors. It is hypothesized that pathological Internet use is associated with socio-demographic
factors, gender, age, life satisfaction, time spent on Internet, Internet use patterns, history of
child abuse and other psychosocial factors. The literature reviewed will further illustrate the
relationship between pathological Internet use and its various associated factors.
Literature Review
There are many factors associated with Internet use, such as sociodemographic
variables, such as gender, time spent online, psychosocial factors, life satisfaction, and history
of child abuse and other comorbid symptoms, such as depression, harmful substance abuse
and sleeping disorder. Socio-demographic factors such as gender, family socio-economic
status, types of residence; duration of Internet use for study or recreational purpose;
psychosocial factors such as low academic achievement, low life satisfaction; and comorbid
symptoms such as alcohol and substance use and depression have been associated with PIU in
adolescents and young people (Kuss, Griffiths, Karila, & Billieux, 2014, Turnbull et al.,
2018). Bozogplan, Demirer, & Sahin, (2013) found that loneliness, self-esteem and life
satisfaction explained 38% of the total variance in Internet addiction.
A number of studies have shown that the male gender is more susceptible to Internet
addiction (Carli et al., 2013; Anand et al., 2018). Anand et al. (2018) in their study among
undergraduate students, aged 18-21 years old in South India found that IA was higher among
male students, i.e. 2.8 times at a higher risk of engaging IA. One study among school
adolescents in China, showed that mild and severe IA was significantly higher in boys than in
girls (Xin et. al., 2018). Among Malaysian medical students, the male students were 1.8 times
more at risk of Internet addiction (Ching, et al., 2017). College and University students are
more susceptible to PIU (Kim, Griffiths, Lau, Fong, & Lam, 2013; Ozcan, & Buzlu, 2007;
Chi, Lin, & Zhang, 2016) due to reasons such as early exposure to the Internet, lack of
parental supervision, the availability and free access to the Internet at the university campus,
65
Other local Malaysian studies also showed a variation in the prevalence rates of
Internet addiction due to the methodology employed. Cheng and Li’s (2014) meta-analysis of
31 nations across seven regions in the world, among 12-18 years old adolescents, 2.4% of
Malaysian adolescents were reported being addicted to Internet, and 35.1% were found
having problematic Internet use. In one study among secondary school students, 28.6% of the
respondents were addicted to the Internet (Mohd Isa, Hashim, Kaur, & Ng, 2016). Yet, in
another study among undergraduate students in a public university, the prevalence of Internet
addiction was 7.8% and 56.5% were problematic Internet users (Rosliza, Ragubathi,
Mohamad Yusoff, & Shaharuddin, 2018). Zainudin, Md Din, & Othman, (2013) also in their
study among undergraduate students, found 30% prevalence of excessive Internet users.
Among local medical students, a study showed a prevalence of 36.9% Internet addiction
(Ching et al., 2017). As to the impact of Internet addiction on young Malaysian adults, (Alam
et al., 2014) showed those adults using Internet excessively were having problems, such as,
interpersonal, behavioral, physical, psychological and work problems in their daily lives.
Internet addiction in adolescents and young adults has become a public health issue
and has an impact on health education and health promotion. Excessive and inappropriate use
of the Internet can pose serious negative consequences on one’s mental health and quality of
life (Kuss & Griffiths, 2012, Alam et al., 2014). Thus, this paper examined the prevalence of
pathological internal use among university students in Kuala Lumpur and its associated
factors. It is hypothesized that pathological Internet use is associated with socio-demographic
factors, gender, age, life satisfaction, time spent on Internet, Internet use patterns, history of
child abuse and other psychosocial factors. The literature reviewed will further illustrate the
relationship between pathological Internet use and its various associated factors.
Literature Review
There are many factors associated with Internet use, such as sociodemographic
variables, such as gender, time spent online, psychosocial factors, life satisfaction, and history
of child abuse and other comorbid symptoms, such as depression, harmful substance abuse
and sleeping disorder. Socio-demographic factors such as gender, family socio-economic
status, types of residence; duration of Internet use for study or recreational purpose;
psychosocial factors such as low academic achievement, low life satisfaction; and comorbid
symptoms such as alcohol and substance use and depression have been associated with PIU in
adolescents and young people (Kuss, Griffiths, Karila, & Billieux, 2014, Turnbull et al.,
2018). Bozogplan, Demirer, & Sahin, (2013) found that loneliness, self-esteem and life
satisfaction explained 38% of the total variance in Internet addiction.
A number of studies have shown that the male gender is more susceptible to Internet
addiction (Carli et al., 2013; Anand et al., 2018). Anand et al. (2018) in their study among
undergraduate students, aged 18-21 years old in South India found that IA was higher among
male students, i.e. 2.8 times at a higher risk of engaging IA. One study among school
adolescents in China, showed that mild and severe IA was significantly higher in boys than in
girls (Xin et. al., 2018). Among Malaysian medical students, the male students were 1.8 times
more at risk of Internet addiction (Ching, et al., 2017). College and University students are
more susceptible to PIU (Kim, Griffiths, Lau, Fong, & Lam, 2013; Ozcan, & Buzlu, 2007;
Chi, Lin, & Zhang, 2016) due to reasons such as early exposure to the Internet, lack of
parental supervision, the availability and free access to the Internet at the university campus,
Pathological Internet Use among Undergraduate Students
66
the need to use Internet to perform academic activities (Ko, Yen, Chen, Chen, & Yen, 2008)
to cope with anxiety, depression, and stress of university’s life (Hicks & Heastie, 2008) and,
for social networking (van Rooij, Schoenmakers, van de Eijnden, & van de Mheen, 2010).
The quality of the family environment and parent-child relationships were also shown to be
linked to Internet addiction (Chi, Lin, & Zhang, 2016). The excessive use of the Internet has
also impacted on students’ academic performance and social interaction (Yen, Yen, & Ko,
2010; Durkee et al., 2016; Turnbull et al., 2018).
Internet use pattern is also something to reckon with, as the pattern varies from study
to study. Mak et al., (2014) in their six Asian countries epidemiological study of Internet
behaviors among adolescents aged 12-18 years old, found that emails (66%), instant messages
(50%), blogging (25%), and visiting leisure web sites (20%) are relatively more common in
Japan, whereas social networking (65%), newsgroup/discussion groups/forums (19%), non-
purposive web surfing (27%), online shopping (8%), and downloading (28%) are relatively
more common in Hong Kong. In Malaysia, most common are for social networking (38%),
followed by online gaming (19%), downloading (19%), web surfing (14%), visiting leisure
websites (13%), email (12%), listening to online radio (10%), Instant messenger (9%), and
others (Mak et al., 2014). In another ASEAN study of 5 countries among undergraduate
students (Turnbull et al., 2018), it was found that among those with PIU, overall Internet
usage was more than 5 hours/day, followed by Internet use for recreation purposes (more than
3 hours/per), Internet for pornography, Smartphone use, and Internet use for study purposes.
It is obvious that students use the Internet for a variety of purposes. A Malaysian study on
medical students found that the use of Internet was mainly for entertainment purposes,
followed by education and the mixture of both entertainment and education purposes (Ching,
et al., 2017). Based on the recent Internet Users Survey 2017 (MCMC, 2017), text
communication (96.3%) and visiting social network site (89.3%), were the most common
activities for Internet users as well as getting information online (86.9%).
A study among Hong Kong and Macau university students have reported that having
more liberal sexual attitudes, stronger perception that sex is as an instrument for biological
needs, poor attitudes towards contraception and ever had sexual experience were significantly
associated with PIU (Ding et al., 2016). Childhood trauma particularly physical and
emotional abuse significantly increases risks of developing PIU (Zhang et al., 2009;
Dalbudak, Evren, Aldemir, & Evren, 2014; Turnbull et. al., 2018). Furthermore, adolescents
who had experienced sexual abuse showed lower self-esteem, more depressive symptoms, and
greater problematic Internet use compared to adolescents who have not experienced sexual
abuse (Kim, Park, & Park, 2017). Childhood abuse has also been related to post-traumatic
stress disorder (PTSD) (Ginzburg et al., 2009; Hsieh et al., 2016). People who have
experienced traumatic events may use avoidance as a means to cope with their negative
memories and emotions and one way to do that is to use the Internet as distraction and this
may lead to addiction.
PIU has impact on physical and psychological health due to poorer diet, less regular
exercise, sedentary activities, less sleep (Kim & Chun, 2005, Mak et al. 2014) resulting in
obesity (Mak et al., 2014; Tsitsika et al., 2016), lower self-perceived immune function (Reed,
Vile, Osborne, Romano, & Truzoli, 2015) and health status; as well as mental problems such
as depression, social anxiety, attention-deficit hyperactive disorder and psychosocial well-
being (Ko, Yen, Yen, Chen, & Chen, 2012; Lai et al., 2015; Mak et al 2014; Sung, Noh, Park,
& Ahn 2013). Co-morbid symptoms, such as, gambling problem, harmful use of alcohol,
66
the need to use Internet to perform academic activities (Ko, Yen, Chen, Chen, & Yen, 2008)
to cope with anxiety, depression, and stress of university’s life (Hicks & Heastie, 2008) and,
for social networking (van Rooij, Schoenmakers, van de Eijnden, & van de Mheen, 2010).
The quality of the family environment and parent-child relationships were also shown to be
linked to Internet addiction (Chi, Lin, & Zhang, 2016). The excessive use of the Internet has
also impacted on students’ academic performance and social interaction (Yen, Yen, & Ko,
2010; Durkee et al., 2016; Turnbull et al., 2018).
Internet use pattern is also something to reckon with, as the pattern varies from study
to study. Mak et al., (2014) in their six Asian countries epidemiological study of Internet
behaviors among adolescents aged 12-18 years old, found that emails (66%), instant messages
(50%), blogging (25%), and visiting leisure web sites (20%) are relatively more common in
Japan, whereas social networking (65%), newsgroup/discussion groups/forums (19%), non-
purposive web surfing (27%), online shopping (8%), and downloading (28%) are relatively
more common in Hong Kong. In Malaysia, most common are for social networking (38%),
followed by online gaming (19%), downloading (19%), web surfing (14%), visiting leisure
websites (13%), email (12%), listening to online radio (10%), Instant messenger (9%), and
others (Mak et al., 2014). In another ASEAN study of 5 countries among undergraduate
students (Turnbull et al., 2018), it was found that among those with PIU, overall Internet
usage was more than 5 hours/day, followed by Internet use for recreation purposes (more than
3 hours/per), Internet for pornography, Smartphone use, and Internet use for study purposes.
It is obvious that students use the Internet for a variety of purposes. A Malaysian study on
medical students found that the use of Internet was mainly for entertainment purposes,
followed by education and the mixture of both entertainment and education purposes (Ching,
et al., 2017). Based on the recent Internet Users Survey 2017 (MCMC, 2017), text
communication (96.3%) and visiting social network site (89.3%), were the most common
activities for Internet users as well as getting information online (86.9%).
A study among Hong Kong and Macau university students have reported that having
more liberal sexual attitudes, stronger perception that sex is as an instrument for biological
needs, poor attitudes towards contraception and ever had sexual experience were significantly
associated with PIU (Ding et al., 2016). Childhood trauma particularly physical and
emotional abuse significantly increases risks of developing PIU (Zhang et al., 2009;
Dalbudak, Evren, Aldemir, & Evren, 2014; Turnbull et. al., 2018). Furthermore, adolescents
who had experienced sexual abuse showed lower self-esteem, more depressive symptoms, and
greater problematic Internet use compared to adolescents who have not experienced sexual
abuse (Kim, Park, & Park, 2017). Childhood abuse has also been related to post-traumatic
stress disorder (PTSD) (Ginzburg et al., 2009; Hsieh et al., 2016). People who have
experienced traumatic events may use avoidance as a means to cope with their negative
memories and emotions and one way to do that is to use the Internet as distraction and this
may lead to addiction.
PIU has impact on physical and psychological health due to poorer diet, less regular
exercise, sedentary activities, less sleep (Kim & Chun, 2005, Mak et al. 2014) resulting in
obesity (Mak et al., 2014; Tsitsika et al., 2016), lower self-perceived immune function (Reed,
Vile, Osborne, Romano, & Truzoli, 2015) and health status; as well as mental problems such
as depression, social anxiety, attention-deficit hyperactive disorder and psychosocial well-
being (Ko, Yen, Yen, Chen, & Chen, 2012; Lai et al., 2015; Mak et al 2014; Sung, Noh, Park,
& Ahn 2013). Co-morbid symptoms, such as, gambling problem, harmful use of alcohol,
Wen Ting Tong, Md. Ashraful Islam, Wah Yun Low, Wan Yuen Choo, and Adina Abdullah
67
drug use in the past 12 months, mental distress, e.g. severe depression, PTSD symptoms,
sleeping problems and suicidal attempts, are also related to PIU (Turnbull et al. 2018). Carli et
al., (2013) and Yen et al., (2007) suggest that depression is a leading comorbid disorder with
IA. Individuals with negative self-esteem are at risk of engaging in addictive Internet
behaviors which helps to momentarily free themselves of their negative self-esteem, irrational
cognitive assumptions and associated unpleasant emotions (Griffiths, 2000). Thus, one way
of relieving stress among university students is by interacting with their computers, and this
works as a coping mechanisms for them.
PIU is indeed a very complex issue and can manifest itself in a pathological,
behavioral and emotional way and there are also many theories that explain PIU. Among
others, is the cognitive-behavioral model of pathological Internet use proposed by Davis
(2001) to explain the etiology of this phenomenon. This model emphasizes the individual’s
cognitions (or thoughts) as the main source of abnormal behavior, and these cognitive
symptoms (e.g. feeling of self-consciousness, low self-esteem, low self-worth, social anxiety,
etc.) of PIU often precede and cause the affective or behavioral symptoms. Thus, the
etiological factor must be present or must have occurred in order for the symptoms to occur
(in this case, the Internet use). So, the maladaptive cognitions (e.g. distorted thoughts and the
thought processes) are sufficient to cause the symptoms of PIU, such as, the obsessive
thoughts about Internet usage, or having less time to do other things, etc. (Davis, 2001). This
model is important to explain the role of cognitions in PIU.
In view of the literature above, it is thus important to determine the extent of PIU and
its associated factors so that interventions can be developed to prevent the onset of negative
consequences of PIU among university students who are the future policy makers of a nation.
Therefore, this study begs the research questions as to how serious is PIU in Malaysia among
University students, and how the various socio-demographic factors, psychosocial issues,
Internet use patterns, history of child abuse, and co-morbid conditions are affected by PIU. It
is also hypothesized that the socio-demographic variables together with the various psycho-
social variables are related to PIU.
Objectives
This study aimed (1) to determine the prevalence of PIU using the Young’s Diagnostic
Questionnaire (Young, 1998), where the diagnosis of PIU was established when there is a
score of ≥ 5; and (2) to determine the associated factors pertaining to socio-demography (age,
gender, perceived income status, academic performance), post-traumatic stress disorder,
history of child abuse, Internet use patterns and co-morbid symptoms using logistic
regression.
Methods
Study Design and Sampling
This cross-sectional study was conducted between July to September 2015 among
Malaysian undergraduate students in a public university in Kuala Lumpur. The particular
university in this study was purposely selected because it is the premier university in the
country. University students were chosen as the literature review has shown that this is the
age group that are most vulnerable, being Internet savvy and frequently exposed to
67
drug use in the past 12 months, mental distress, e.g. severe depression, PTSD symptoms,
sleeping problems and suicidal attempts, are also related to PIU (Turnbull et al. 2018). Carli et
al., (2013) and Yen et al., (2007) suggest that depression is a leading comorbid disorder with
IA. Individuals with negative self-esteem are at risk of engaging in addictive Internet
behaviors which helps to momentarily free themselves of their negative self-esteem, irrational
cognitive assumptions and associated unpleasant emotions (Griffiths, 2000). Thus, one way
of relieving stress among university students is by interacting with their computers, and this
works as a coping mechanisms for them.
PIU is indeed a very complex issue and can manifest itself in a pathological,
behavioral and emotional way and there are also many theories that explain PIU. Among
others, is the cognitive-behavioral model of pathological Internet use proposed by Davis
(2001) to explain the etiology of this phenomenon. This model emphasizes the individual’s
cognitions (or thoughts) as the main source of abnormal behavior, and these cognitive
symptoms (e.g. feeling of self-consciousness, low self-esteem, low self-worth, social anxiety,
etc.) of PIU often precede and cause the affective or behavioral symptoms. Thus, the
etiological factor must be present or must have occurred in order for the symptoms to occur
(in this case, the Internet use). So, the maladaptive cognitions (e.g. distorted thoughts and the
thought processes) are sufficient to cause the symptoms of PIU, such as, the obsessive
thoughts about Internet usage, or having less time to do other things, etc. (Davis, 2001). This
model is important to explain the role of cognitions in PIU.
In view of the literature above, it is thus important to determine the extent of PIU and
its associated factors so that interventions can be developed to prevent the onset of negative
consequences of PIU among university students who are the future policy makers of a nation.
Therefore, this study begs the research questions as to how serious is PIU in Malaysia among
University students, and how the various socio-demographic factors, psychosocial issues,
Internet use patterns, history of child abuse, and co-morbid conditions are affected by PIU. It
is also hypothesized that the socio-demographic variables together with the various psycho-
social variables are related to PIU.
Objectives
This study aimed (1) to determine the prevalence of PIU using the Young’s Diagnostic
Questionnaire (Young, 1998), where the diagnosis of PIU was established when there is a
score of ≥ 5; and (2) to determine the associated factors pertaining to socio-demography (age,
gender, perceived income status, academic performance), post-traumatic stress disorder,
history of child abuse, Internet use patterns and co-morbid symptoms using logistic
regression.
Methods
Study Design and Sampling
This cross-sectional study was conducted between July to September 2015 among
Malaysian undergraduate students in a public university in Kuala Lumpur. The particular
university in this study was purposely selected because it is the premier university in the
country. University students were chosen as the literature review has shown that this is the
age group that are most vulnerable, being Internet savvy and frequently exposed to
Pathological Internet Use among Undergraduate Students
68
communications via social media. The total undergraduate student body at the time of study
was 11,908 from 16 faculties, 2 centers and 2 academies. A stratified cluster sampling was
used to draw the sample. All the faculties, centers and academies formed the clusters and were
included in the sampling frame. Within each cluster, the student populations were stratified by
gender in order to obtain equal representation of both males and females. The number of
students selected from each cluster are proportional to size. Undergraduate students from year
1 to 5 form all the clusters, as those who are currently studying at the university were invited
to participate on a voluntary basis.
Measurements
The questionnaire used for this study was a combination of items from the following:
PIU was assessed using the Young’s Diagnostic Questionnaire (YDQ) (Young, 1998).
The YDQ was developed based on the diagnostic criterion of pathological gambling listed in
the DSM-4 (American Psychiatric Association, 1994). The YDQ comprised of 8 “yes” and
“no” items assessing patterns of Internet usage in terms of preoccupation, tolerance, loss of
control, withdrawal, negative consequences, denial, and escapism (scoring 0-8). One point
was given to each “Yes” answer. Diagnosis of PIU was established when there is a score of ≥
5. The Cronbach’s Alpha value was 0.678.
Socio-demographic variables including age, gender, ethnicity, current year of study,
self-perceived economic status and current residence (6 items). The item on self-perceived
economic status had a response options from 1=wealthy (within the highest 25% in your
country in terms of wealth), 2=Quite well-off (within the 50-75% range for your country),
3=Not very well off (within the 25-50% range for your country) and 4=Quite poor (within the
lowest 25% in your country in terms of wealth).
Internet use variables were open ended items on number of hours spent on the Internet
in a day, number of hours spent on the Internet for study purposes and recreational purposes
in a day, number of hours spent on the Internet for pornography in a week and number of
hours using smartphone in a day (5 items).
Psychosocial variables included items from the World Health Organization adverse
childhood experience scale (CDC, 2016; WHO, 2016) to measure child abuse experiences in
terms of emotional (5 items; Cronbach’s Alpha (0.78)), physical (2 items; Cronbach’s Alpha
(0.74)) and sexual abuse (4 items; Cronbach’s Alpha (0.81)).
Self-perceived life satisfaction was measured using one-item: “All things considered,
how satisfied are you with your life as a whole?” adapted from Lucas & Donnellan (2012).
The response options ranged from 1=very satisfied to 5=very dissatisfied.
Self-perceived academic performance was measured using one-item “How would you
rate your academic performance” with response options from 1=excellent to 5=poor.
Co-morbid symptoms measured were: gambling, measured using the item “Have you
felt that you might have a problem with gambling?” with response options from 0=never,
1=sometimes, 2=most of the time, 3=almost always; tobacco use measured using the item
“Do you currently use one or more of the following tobacco products (cigarettes, snuff,
chewing tobacco, cigars, etc.) with response options “yes” and “no” (World Health
Organization, 1998); and drug use measured using the item “How often have you taken drugs
in the past 12 months; other than prescribed by healthcare providers?” with response options
68
communications via social media. The total undergraduate student body at the time of study
was 11,908 from 16 faculties, 2 centers and 2 academies. A stratified cluster sampling was
used to draw the sample. All the faculties, centers and academies formed the clusters and were
included in the sampling frame. Within each cluster, the student populations were stratified by
gender in order to obtain equal representation of both males and females. The number of
students selected from each cluster are proportional to size. Undergraduate students from year
1 to 5 form all the clusters, as those who are currently studying at the university were invited
to participate on a voluntary basis.
Measurements
The questionnaire used for this study was a combination of items from the following:
PIU was assessed using the Young’s Diagnostic Questionnaire (YDQ) (Young, 1998).
The YDQ was developed based on the diagnostic criterion of pathological gambling listed in
the DSM-4 (American Psychiatric Association, 1994). The YDQ comprised of 8 “yes” and
“no” items assessing patterns of Internet usage in terms of preoccupation, tolerance, loss of
control, withdrawal, negative consequences, denial, and escapism (scoring 0-8). One point
was given to each “Yes” answer. Diagnosis of PIU was established when there is a score of ≥
5. The Cronbach’s Alpha value was 0.678.
Socio-demographic variables including age, gender, ethnicity, current year of study,
self-perceived economic status and current residence (6 items). The item on self-perceived
economic status had a response options from 1=wealthy (within the highest 25% in your
country in terms of wealth), 2=Quite well-off (within the 50-75% range for your country),
3=Not very well off (within the 25-50% range for your country) and 4=Quite poor (within the
lowest 25% in your country in terms of wealth).
Internet use variables were open ended items on number of hours spent on the Internet
in a day, number of hours spent on the Internet for study purposes and recreational purposes
in a day, number of hours spent on the Internet for pornography in a week and number of
hours using smartphone in a day (5 items).
Psychosocial variables included items from the World Health Organization adverse
childhood experience scale (CDC, 2016; WHO, 2016) to measure child abuse experiences in
terms of emotional (5 items; Cronbach’s Alpha (0.78)), physical (2 items; Cronbach’s Alpha
(0.74)) and sexual abuse (4 items; Cronbach’s Alpha (0.81)).
Self-perceived life satisfaction was measured using one-item: “All things considered,
how satisfied are you with your life as a whole?” adapted from Lucas & Donnellan (2012).
The response options ranged from 1=very satisfied to 5=very dissatisfied.
Self-perceived academic performance was measured using one-item “How would you
rate your academic performance” with response options from 1=excellent to 5=poor.
Co-morbid symptoms measured were: gambling, measured using the item “Have you
felt that you might have a problem with gambling?” with response options from 0=never,
1=sometimes, 2=most of the time, 3=almost always; tobacco use measured using the item
“Do you currently use one or more of the following tobacco products (cigarettes, snuff,
chewing tobacco, cigars, etc.) with response options “yes” and “no” (World Health
Organization, 1998); and drug use measured using the item “How often have you taken drugs
in the past 12 months; other than prescribed by healthcare providers?” with response options
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