الفهرس 
Internet AddictionOnly 14 pages are availabe for public view
 |  | from | 185 |  |  |
| | | from | 185 | | |
Abstract
COMPARING INTERNET ADDICTION &
INTERNET GAMING DISorder AND
ASSOCIATED SLEEP DISORDERS
among first year university students
Thesis
Submitted for Partial Fulfillment of master Degree
in Psychiatry
By
Sarah Baiumy Mohamed
M.B.B.Ch.
Supervised By
Professor/ Eman Ibrahim Abo El-Ella
Professor of Psychiatry
Faculty of Medicine, Ain Shams University
Professor/ Doaa Hamed Hewedi
Professor of Psychiatry
Faculty of Medicine, Ain Shams University
Doctor/ Hussien Ahmed Elkholy
Lecturer in Psychiatry
Faculty of Medicine, Ain Shams University
Faculty of Medicine
Ain Shams University
Cairo 2017
Acknowledgment
First of all, thank you God for blessing me much more
than I deserve.
I would like to express my feelings of gratitude to my
dearly Professor. Eman Abo El-Ella, Professor of
Psychiatry, Ain Shams University, who was a great
supportive teacher who taught me responsibility and
importance of knowledge. I will always be grateful to
her.
I would like to express my deep thanks and gratitude to
Professor. Doaa Hamed, Professor of Psychiatry, Ain
Shams University, for her precious advice and
constructive guidance that helped me in this work
formulation. I will always appreciate her precious effort.
I am deeply indebted to Doctor. Hussien Elkholy,
Lecturer in Psychiatry, Ain Shams University, for his
unfailing support and enthusiasm. He guided me through
the finest details to the most complicated ones to finish
this work in its final form. His precious time, effort and
encouragement will be forever appreciated.
I would like to thank all my Professor and Colleagues
who helped, encouraged and supported me.
Finally, I wish to thank all the university students and
authorities who shared and helped me to bring this work
into its present form.
Sarah Baiumy
Index
List of Figures………………………………………………………………………………..………..i
List of Tables…………………………………………………………………………………………..ii
List of Abbreviations……………………………………………………………………………..iv
INTRODUCTION ………………………………………………………………………………..1
AIM OF THE STUDY ……….………………………………………………………………11
REVIEW OF LITERATURE
Chapter 1
Internet Addiction
2.1 Definition and Terminology…………….………………………………………….……..12
2.2 History and development ….………….………………………………………….….…..13
2.3 Prevalence……………………….…………….………………………………………….………14
2.4 Risk factors of internet addiction……………………………………………….……..15
2.5 Diagnosis of internet addiction.……..………..……………………………….….……19
2.6 Consequences of internet addiction ………………………………………….……..24
2.7 Management of internet addiction...………………………………………….……..27
Chapter 2
Internet Gaming Disorder
3.1 Definition……………………………………….………………………………………….……..33
3.2 History & development of internet gaming……………….……………….……..33
3.3 Prevalence……………………….…………….………………………………………….………35
3.4 Effects of video games……………..…….………………………………………….……..35
3.5 Diagnosis and DSM-5 proposed Criteria…………….……………………….……..40
3.6 Management of IGD….………….………….……………….……………………….……..42
Chapter 3
Sleep
3.1 Anatomy of sleep………………………….…………………………………………………..43
3.2 Sleep cycle…………………………………….……………………………………………….….44
3.3 Sleep Regulation…………………………….…………………………..……………….……47
3.4 Sleep Functions………………………………………………………….……………………..48
3.5 Sleep requirements………………………….…………..…………………………………..49
3.6 Sleep Disorders………………………………….………………………………………………50
3.7 Sleep and Interne………………..………….………………………………………….……..54
METHODOLOGY
1.Design ………………………………………………….………………………………………………56
2.Participants…………………………………………….…………………………………..……….56
3.Tools……………………………………………………….…………………………..……………….57
4.Procedure…………………………………………………………………………………………….59
RESULTS …..………………………………………………………………………………………...64
DISCUSSION……………………………………………………………………………………...86
LIMITATIONS AND STRENGTHS….…….……………………………………….97
CONCLUSION…………………………………………………………………………………….98
RECOMMENDATIONS…..……………………………………………………………….99
SUMMARY……………………………………………………………………………………….100
REFERENCES…………………………………………………………………………..………..109
APPENDICES
Appendix (1): Internet Addiction Test (IAT)…….…………………………….….....143
Appendix (2): Internet Gaming Disorder-Scale..……………………….……………145
Appendix (3): PSQI………………………………………………………………………………….146
Appendix (4): SES-Scale…………………………….…………………………………………..148
ARABIC SUMMARY………………………………………………………………………--
i
List of Figures
Figure 1: Severity of IGD……………..……………………………………….….……..4
Figure 2: Risk factors of Internet Addiction………………….……….….……15
Figure 3: Subtypes of Internet Addiction……………………..……….….……23
Figure 4: Internet Addiction side effects……………………………….….……25
Figure 5: Management of Internet Addiction……………………….….……28
Figure 6: Effects of Internet Games……..……………………………….….……36
Figure 7: Internet Addiction prevalence……………………………….….……68
Figure 8: Internet Gaming Disorder prevalence…………………….….……69
Figure 9: Symptoms and patterns of Internet Addiction …..….….……71
Figure 10: Sleep Quality among participants…………………….….….……72
Figure 11: Relation between IGD and Sleep Quality………….….….……73
Figure 12: High significant correlation between IAT and Grades.…78
Figure 13: High significant correlation between IAT and Sleep
Quality……………………………………………………………………………….….……79
Figure 14: Correlation between IGD and students’ grades….….……81
Figure 15: Correlation between IGD and Sleep Quality……….….……82
Figure 16: Relation between IA and IGD……………………..…….….……84
Figure 17: Relation between IGD and IA ……………………..…….….……84
Figure 18: Correlation between IA and IGD…………………..…….….……85
ii
List of Tables
Table 1: Diagnostic criteria of Internet Addiction………………….….……22
Table 2: Diagnostic criteria of IGD…………………………………….….….……41
Table 3: Main brain rhythms on EEG……………………………….….….……45
Table 4: Range of sleep duration according to different age
groups…………………………………………………………………………….….….……50
Table 5: Validity of the Arabic version of IGD scale……………….….……65
Table 6: Reliability of the Arabic version of IGD scale……….….….……66
Table 7: Personal characteristics of the sample…………………….….……67
Table 8: Prevalence of internet Addiction…..…………………….….….……69
Table 9: Online gaming behavior……………..……………………….….….……70
Table 10: Symptoms and patterns of Internet Addiction…..….….……71
Table 11: Relation between sleep quality and Internet
Addiction……………………………………………….………………………….….….……73
Table 12: Answers of students on Q14 of IAT……………………….….……74
Table 13: Mini-Kid results among the sample ………………….….….……74
Table 14: High significant relation between IA and Psychiatric
disorders …………………………………………………….…………………….…….……75
Table 15: High significant relation between IGD and psychiatric
disorders………………………………………………….…………………………..….……76
Table 16: Relation between Internet Addiction and personal
characteristics ……………..……………………………………………………………….77
iii
Table 17: Correlation between IAT SES, Grades & Sleep quality….…78
Table 18: Relation between Internet Gaming Disorder (IGD) and
personal characteristics……………………………………………………….……..…80
Table 19: Correlation between IGD score and SES-scale, Grades, and
PSQI……………………………………………………………………..…………….….…..…81
Table 20: Relation between Internet Addiction and Internet Gaming
Disorder………………….…………………………………………..…………….….………83
iv
List of Abbreviations
5-HT Serotonin
ADHD Attention Deficit Hyperactivity Disorder
APA American Psychological Association
CBT Cognitive-Behavioral Therapy
CIAR Center for Internet Addiction Recovery
CIU Compulsive Internet Use
CIUS Compulsive Internet Use Scale
DA Dopamine
DSM-4 4th edition of the Diagnostic and Statistical Manual of
Mental Disorders
DSM-5 5th edition of the Diagnostic and Statistical Manual of
Mental Disorders
EDS Excessive Daytime Sleepiness
EEG Electroencephalography
Fig. Figure
GABA -aminobutyric acid
GIA Generalized Internet addiction
IA Internet Addiction
IAD Internet Addiction Disorder
IAT Internet Addiction Test
ICD International Classification of Diseases
ICSD3 International Classification of Sleep Disorders third
edition
IGD Internet Gaming Disorder
INP Institute of National Planning
ITU International Telecommunication Union
v
MINI-KID The Mini International Neuropsychiatric Interview for
children and adolescents
MMORPG Massively Multiplayer Online Role-Playing Games
NREM Non- Rapid Eye Movement
PIU Problematic Internet Use
PIUQ Problematic Internet Use Questionnaire
PTSD Post-Traumatic Stress Disorder
PSQI The Pittsburgh Sleep Quality Index
RDAB Reward-deficient aberrant behavior
REM Rapid Eye Movement
SCN Suprachiasmatic nucleus
SES Socio-Economic Status
SIA Specific Internet addiction
SUD Substance Use Disorder
UNDP United Nations Development Programme
VGP Video game players
Introduction
1
Introduction
Today with more than 40 million internet users in
Egypt (ITU, 2016) and more than 80% of Internet Café
clients in Egypt were young people (UNDP & INP, 2010)
the internet has become an integral part of our society.
The Internet itself is a neutral device originally
designed to facilitate research among academic and military
agencies (Young, 2004). Internet delivers some practical
tools like entertainment, shopping, social sharing
applications which enable accessing knowledge easier and
faster (Young, 1998).However it may cause physical and
psychological harms like tiredness (Akın and Iskender,
2011), depression (Yen et al., 2007), hostility, loneliness
(Çardak, 2013), some educational harms like wasting of
time (Griffiths, 2000), decrease in academic performance
(Aboujaoude, 2010), communication problems with peers
(Gross et al., 2002).
It‟s about how some people use this communication
medium that created a stir among the mental health
community by great discussion of Internet addiction.
Addictive use of the Internet is a rapidly growing
phenomenon (Young, 2004).
Introduction
2
Internet Addiction Disorder (IAD) is sometimes
referred to as problematic Internet use (PIU) (Moreno et
al., 2013) or compulsive Internet use (CIU) (Meerkerk et
al., 2009). Other overlapping terms include Internet
overuse, problematic computer use, or pathological
computer use and even i-Disorder (Rosen, 2012).
Clinical research on behavioral addictions
investigated many models of addiction e.g compulsive
gambling (Mobilia, 1993), overeating (Lesieur & Blume,
1993), and compulsive sexual behavior (Goodman, 1993).
Similar addiction models have been applied to
technological overuse (Griffiths, 1996).
In identifying the Internet addiction the most
frequently used definitions are as follows: Excessive use of
the Internet, uncontrolled and destructive Internet use
(Morahan-Martin and Schumacher, 2000); Excessive
Internet use that causes problems in family, business,
school, social and psychological life of the individuals
(Beard & Wolf, 2001); a new and unidentified clinical
disorder that may affect the individual‟s Internet use,
controlling ability and thus leading to personal,
professional and social problems (Young, 2007).
Introduction
3
Five general subtypes of Internet addiction were
categorized based upon the most problematic types of
online applications, and they include addictions to
Cybersex, Cyber-relationships, online stock trading or
gambling, information surfing, and computer games
(Young et al., 1999).
Recently, Internet gaming disorder (IGD) got listed
in Section III, Conditions for Further Study of the 5th
edition of the Diagnostic and Statistical Manual of Mental
Disorders (DSM–5). This decision was based upon the
large number of studies of this condition and the severity of
its consequences. Also it was referred to as gaming or
internet use disorder, gaming or internet addiction or
dependence, pathological or problematic gaming, etc.
(Petry et al., 2014).
As mentioned in DSM-5, IGD is persistent and
recurrent use of the internet to engage in games, often with
players, leading to clinically significant impairment or
distress in a 12-month period as indicated by five (or more)
of the proposed criteria. as indicated by five (or more) of
the proposed criteria: 1) Preoccupation, 2) Withdrawal
symptoms, 3) Tolerance, 4) Unsuccessful attempts to
control, 5) Loss of interest in previous hobbies and
Introduction
4
Severity of IGD
MilD
Fewer symptoms
and less disruption
Moderate
Severe
More hours spent
on computer and
more severe losses
entertainment, 6) Continued excessive use despite
knowledge of psychosocial problems, 7) Deceiving
regarding the amount of internet gaming, 8) Use internet
games to escape or relieve a negative mood, 9) Jeopardized
or lost a significant relationship, job, or educational or
career opportunity because of participation in internet
games (DSM-5, 2014). Severity of IGD should be specified
according to degree of disturbances in normal life activities
(See Fig. 1).
Fig. 1: Severity of IGD (DSM-5, 2014).
Many studies aimed at detecting risk factors for
developing Internet Addiction. A previous study exposed
that Internet user is higher in younger than adult and mainly
19 to 24 years of age group are considered as a high risk
group for Internet addiction (Koo & Kwon, 2014).
Adolescents in particular are immature both physically and
psychologically and tend to show more negative Internet
Introduction
5
effects than any other age-group (Oh, 2005). They were
more likely to suffer anxiety, depression, loneliness or
social isolation, impulsivity, and feelings of self-effacement
(Cho & Lee, 2004).
Another study reported that university students are at
higher risk of becoming Internet addicts due to more free
time, lack of monitoring on account of being away from
parents and sometimes efforts to become away from
arduous university routines (Soule et al., 2003; Young &
Rogers, 1998; Kandell, 1998).
A meta-analysis done in Korea, revealed that the
magnitude of the effect of the intrapersonal variables (for
example, escape from self, self-identity, Attention problem,
emotional regulation, aggression and negative stress
coping) associated with Internet Addiction (IA) was
significantly higher than that of interpersonal variables
(Koo & Kwon, 2014).
The characteristics of the family are also associated
with the development of Internet addiction among
adolescents (Yen et al. 2007).
Introduction
6
As young people‟s engagement with technology
increases, so does the parents‟ concern with the impact that
it may have on their children‟s lives (Cash et al., 2012).
Generation Gap between parents and their children
increases as parents couldn‟t supervise their children‟s use
of technology. (Van-Doorn et al., 2011) This gap may lead
to inability to establish limits and good control because of
their unfamiliarity with technology (Greydanus and
Greydanus, 2012).
from biological point of view, several studies
suggest the presence of genetic predisposition to addictive
behaviors (Grant et al., 2006). According to this concept,
the lack of adequate number of dopamine receptors or have
an insufficient amount of serotonin/dopamine, as a result
they experience normal level of pleasure in events in which
most people would find rewarding (Beard, 2005). In order
to increase the level of pleasure these types of individuals
are more disposed to search for such type of behaviors like
Internet that make them reward, but simultaneously placing
them at higher risk for addiction (Cash et al., 2012).
Many negative consequences of such misuse of
internet were identified. For instance, in South Korea
Introduction
7
Internet Addiction is considered one of its most serious
public health issues (Ahn, 2007). After a series of 10
cardiopulmonary-related deaths in Internet cafés (Choi,
2007) and a game-related murder (Koh, 2007).
Students who spend more time using the Internet have less
sleeping time and feel higher levels of tiredness (Bulck,
2004).
Internet misuse may lead to Excessive Daytime
Sleepiness (EDS) that is considered a risk of drowsy
driving (Masa et al., 2000; Powell et al., 2002), injuries in
the workplace (Melamed and Oksenberg, 2002), and poor
school performance (Gibson et al., 2006). Also internet
addicts are liable to addict drugs, alcohol, tobacco, sex,
chronic overeating etc (Ho et al., 2014).
Researchers have claimed that the uncontrollable
Internet users can generate morphological mutations in the
structure of the brain (Uddin et al., 2016). A study in
Chinese college students exposed that use of computer for
about 10 hrs a day and for 6 days a week, showed decreases
in the dimensions of the dorsolateral prefrontal cortex,
rostral anterior cingulate cortex, supplementary motor area
and parts of the cerebellum compared to control students
(Yuan et al., 2011). It has been hypothesized that these
Introduction
8
variations reveal learning-type cognitive optimizations for
using computers more competently, but correspondingly
diminished temporary memory and decision-making
capabilities as well as increase the pleasure to remain
online rather than the actual world (Mosher, 2011).
The CIAR (Center for Internet Addiction Recovery)
stated that Internet addicts suffer from emotional problems,
including depression and anxiety- associated disorders and
frequently use the fabulous world of the Internet to
psychologically escape unpleasant feelings or stressful
situations (Young, 2009).
Many researchers and clinicians have marked that a
diversity of psychological disorders occurs together with
IAD. For instance, a previous study showed that
depression, anxiety, hostility, interpersonal sensitivity and
psychoticism were consequences of IAD (Cheng & Li,
2014) Other studies showed that people who are suffering
from depression are more likely to develop Internet
addiction (Ha et al., 2006; Young & Rogers, 1998; Kim
et al., 2006). It is controversy, which came first, the
addiction or the co-occurring disorders (Cheng & Li,
2014; Kratzer & Hegerl, 2008).
Introduction
9
Although Internet Addiction yet not listed as one of
the psychiatric disorders in DSM-5, many centers around
the world were developed for prevention and management
of Internet Addiction.
Some mental health professionals suggest that family
should be the focus of prevention strategies. Many
researchers suggest a family-centered approach to
prevention, similar to the one used in interventions for the
prevention of drug addiction (Yen et al., 2007).
This kind of approach entails parental education and
aims at helping parents to improve their communication
skills with their children, promote healthy interaction
within the family and to reduce maladaptive family
behaviors (Yen et al., 2007).
Also researchers suggest that teenagers should be
allowed to use the Internet only during specific hours of the
week so that the development of Internet addiction is
prevented and they are encouraged to participate in real life
and not in cyberspace activities (Ko et al., 2007).
We need to identify the factors contributing to
Internet Addiction and to develop appropriate preventive
interventions for individuals at risk of Internet addiction.
Introduction
10
So the aims of this study were to examine and
compare the prevalence of Internet Addiction and Internet
Gaming Disorder with examination of related sleep
problems and other psychiatric symptoms. Also identifying
risk factors associated with them is one of our aims to help
risk-focused preventive measures of Internet Addiction.
Aims of the Study
11
Aims of the Study
1. Comparing the Prevalence of internet addiction &
internet gaming disorder among the selected
population.
2. Assessing essential risk factors (Sex, Socio-economic
class, Role of theoretical & practical faculties) of both
internet addiction and internet gaming disorder.
3. Exploring the associated sleep disorders among the
pathological users.
4. Measuring its impact on the academic achievement.
Internet Addiction
12
Chapter 1
Internet Addiction
Internet Addiction is a global phenomenon that has
been a topic of increasing interest to clinicians, researchers
and stakeholders such as teachers, parents and community
groups. Clinical research on behavioral addictions
investigated many models of addiction: compulsive
gambling (Mobilia, 1993), overeating (Lesieur & Blume,
1993), and compulsive sexual behavior (Goodman, 1993).
Similar addiction models have been applied to
technological overuse (Griffiths, 1996).
1.1 Definition and Terminology:
Generally speaking, IA has been characterized by
excessive or poorly controlled preoccupation, urges, and/or
behaviors regarding Internet use that lead to impairment or
distress in several life domains (Weinstein et al., 2014).
It is a global social issue, can be broadly
conceptualized as an inability to control one’s use of the
Internet which leads to negative consequences in daily life
(Spada, 2014). Internet addiction also known as
problematic internet use (PIU) (Moreno et al., 2013),
compulsive Internet use (CIU) (Meerkerk et al., 2009).
Internet Addiction
13
1.2 History and development:
In 1995, Dr. Goldberg introduced the concept of
Internet Addiction Disorder (IAD) in an effort to parody
the way the American Psychiatric Association‟s hugely
“medicalizes” every excessive behavior (Beato, 2010).
The symptoms he included were “important social or
occupational activities that are given up or reduced because
of the internet use”, “Fantasies or dreams about the
internet” and “Voluntary or involuntary typing movements
of the fingers” (Wallis, 1997). He used pathological
gambling as his model for the description of IAD (Beard &
Wolf, 2004). Later on, He redefined Internet Addiction
Disorder (IAD) as a “Pathological Internet use Disorder”
also known as (PIU) to avoid what he started as a joke to be
thought of as an officially diagnosed addiction, such as an
addiction to heroin (Wallis, 1997).
In 1996, Internet addiction was researched for the
first time, and findings were presented at the American
Psychological Association (APA). The study reviewed over
600 cases of heavy internet users who exhibited clinical
signs of addiction as measured through an adapted version
of the 4th edition of the Diagnostic and Statistical Manual
Internet Addiction
14
of Mental Disorders (DSM-4) criteria for pathological
gambling (Young, 2009).
1.3 Prevalence:
In contemporary society approximately 40% of the
world population is online. Furthermore, global Internet
usage has grown nearly six-fold over the last decade (Kuss
et. al., 2014).
The percentage of internet users in Egypt was 21.6%
in 2010, which became 37.82% in 2015 (around 35 million
users) (ITU, 2016) and more than 80% of Internet Café
clients were young people (UNDP & INP, 2010).
Today with the absence of golden standard for
Internet addiction diagnosis and assessment, Researchers
find different prevalence rates across different populations
(Kuss, 2014). For instance, a study in USA to explore the
prevalence and health correlates of problematic Internet use
among high school students, the prevalence of Problematic
Internet Use (PIU) was 4% (Lin et al., 2011). Another
study on adolescents in China reported 8.1% of participants
had PIU (Cao et al., 2011).
Internet Addiction
15
Risk Factors
Social
Poor
Communication
Demographic
factors
Lonleniess social support
Psychological Biological
1.4 Risk factors of internet addiction:
Risk factors of internet addiction can be broadly
categorized into social factors, psychological factors and
biological factors.
Fig. 2: Risk factors of Internet Addiction.
A- Social Factors:
*Interpersonal problems and loneliness can play an active
role in developing an addiction to online communication
and relationships (Montag & Reuter, 2015) as we can see
through:
1- Poor communication skills can cause poor self-esteem,
feelings of isolation and create problems, such as trouble
working in groups, making presentations, or going to social
engagements. Virtual relationships are a way of engaging
with others while having the safety of avoiding rejection or
Internet Addiction
16
the anxiety of making physical contact with others.
(Montag & Reuter, 2015)
2- Loneliness is associated with the development of
Internet addiction (Hardie & Tee 2007). Loneliness as a
risk factor is consistent with findings that suggest social
relationships are a key component in the development of
Internet addiction (Montag & Reuter, 2015).
3- Online Affairs which is a romantic or sexual relationship
initiated via online contact and maintained predominantly
through electronic conversations that occurs through email,
chat rooms, or online communities (Atwood and
Schwartz, 2002) may affect marital status. Although it
isn‟t well defined whether marital problems lead to
development of such affairs or those online relationships
that cause marital problems!
*Also low social support (Yates et al., 2012), lack of
family love (Huang et al., 2009) may play a role in
provoking internet addiction.
*University students are more at risk of becoming Internet
addicts due to more free time, lack of monitoring on
account of being away from parents and sometimes efforts
Internet Addiction
17
to become away from arduous university routines (Soule et
al., 2003; Kandell, 1998).
*Access to the Internet is increasingly easy due to advances
in mobile technology and the prevalence of smart phones. It
is likely that with the enhanced accessibility of social
networking sites via smart phones, susceptibility to
addiction may also be on the rise. To these days, social
networks sites such as Facebook can be easily accessed by
not only default Internet browsers on smart phones but also
some free smart phone applications (WU et al., 2013).
* In a previous study, there was a negative correlation
between the frequency of book reading and the
development of Internet addiction. Thus, reading no books
at all and reading less than one book per month were shown
to be independent risk factors of Internet addiction
(Sasmaz et al., 2013).
* With regards to sociodemographic variables, Male gender
(Cuhadar, 2012; Lin et al., 2011; Kheirkhah et al.,
2010), Younger age (Morrison & Gore, 2010), City
residence (Ni et al., 2009) and University level education
(Bakken et al., 2009) were reported as at higher risk of
internet addiction.
Internet Addiction
18
B- Psychological Factors:
There are many psychological variables related to
internet addiction, including impulsivity (Lin et al., 2011),
neuroticism (Kuss et al., 2013; Tsai et al., 2009) low
agreeableness (Kuss et al., 2013), low self-concept (Yates
et al., 2012), fun-seeking (Yen et al., 2009) and negative
emotion avoidance (Beutel et al., 2011). Some researches
classified internet addicts into two types. The Dual
Diagnosed Internet Addict suffers from prior psychological
problems such as to depression, anxiety, obsessivecompulsive
disorder, or substance abuse, to name a few
syndromes associated with the disorder. Other addicts,
referred to as New Internet Addicts, have no prior history
of psychiatric illness or addiction, and their addiction to the
Internet is an entirely new problem (Montag and Reuter,
2015).
c- Biological Factors:
Over the last 15 years, studies have emerged to study
relevant brain processes, activities, and brain structures
associated with both gaming and Internet Addiction.
Neuro-imaging studies allow for objective assessment of
Internet Addiction (IA) by investigating effects of brain
changes on human behavior. Studies reported that extended
Internet Addiction
19
engagement in the addictive behavior leads to dopamine
release in the dopaminergic pathways.
As a consequence, the individual becomes less
sensitive to natural rewards, such as food and sex, and
instead seeks the addictive behavior, ultimately changing
brain chemistry and leading to craving and tolerance.
In periods of abstinence, the lack of dopamine
release in the brain leads to withdrawal symptoms that can
only be alleviated via reinstatement of the addictive
behavior.
Research also suggests that engaging in addictive
behaviors may result in brain dysfunction, including in
prefrontal brain regions, i.e., the orbitofrontal cortex and
cingulate gyrus, which are commonly associated with
decision-making.
Emerging research suggests that similar brain
activation and changes occur for behavioral addictions,
including IA” (Pontes et al., 2015).
1.5 Diagnosis of internet addiction:
Diagnosis of Internet addiction (IA) is often
complex. It is not listed in the latest Diagnostic Statistical
Manual (DSM-5, 2014).
Internet Addiction
20
The lack of formal diagnostic criteria makes it
challenging to diagnose internet addiction, so researchers
are systematically adopting modified criteria for
pathological gambling to investigate it (Winkler et al.,
2013).
Additionally, there is debate about whether Internet
addiction is a distinct disorder or a behavioral problem
secondary to other disorders (Ko et al., 2009; Shaffer et
al., 2000). Some researchers and mental health practitioners
see excessive Internet use as a symptom of another disorder
such as anxiety or depression rather than a separate entity
(Kratzer & Hegerl, 2008). Others considered it an Impulse
control disorder (not otherwise specified), yet there is a
growing consensus that this constellation of symptoms is an
addiction (Cash et al., 2012).
This behavior characterized by many hours spent in
non-work technology-related computer/Internet/video game
activities (Czincz & Hechanova, 2009). It is accompanied
by changes in mood, preoccupation with the Internet and
digital media, the inability to control the amount of time
spent interfacing with digital technology, the need for more
time or a new game to achieve a desired mood, withdrawal
symptoms when not engaged, and a continuation of the
Internet Addiction
21
behavior despite family conflict, a diminishing social life
and adverse work or academic consequences (Beard, 2005;
Young, 1998).
Beard recommends diagnostic criteria of Internet
Addiction (See Table 1). Five criteria are needed for
diagnosis between pre-occupation, loss of control, negative
mood if kept offline and negative consequences from
pathological internet usage.
There has been a variety of assessment tools used in
evaluation. Young‟s Internet Addiction Test (IAT) (young,
1998), the Problematic Internet Use Questionnaire (PIUQ)
developed by Demetrovics, Szeredi, and Pozsa
(Demetrovics Z et al., 2008) and the Compulsive Internet
Use Scale (CIUS) (Meerkerk G et al., 2009).
Internet Addiction
22
Table 1: Beard recommended diagnostic criteria of IA.
(Beard, 2005)
Five diagnostic criteria are required for a diagnosis of Internet
addiction:
1) Is preoccupied with the Internet (thinks about previous online
activity or anticipate next online session).
(2) Needs to use the Internet with increased amounts of time in order
to achieve satisfaction.
(3) Has made unsuccessful efforts to control, cut back, or stop
Internet use.
(4) Is restless, moody, depressed, or irritable when attempting to cut
down or stop Internet use.
(5) Has stayed online longer than originally intended.
>> Additionally, at least one of the following must be present:
(6) Has jeopardized or risked the loss of a significant relationship,
job, educational or career opportunity because of the Internet.
(7) Has lied to family members, therapist, or others to conceal the
extent of involvement with the Internet.
(8) Uses the Internet as a way of escaping from problems or of
relieving a dysphoric mood (e.g., feelings of helplessness, guilt,
anxiety, depression)
Internet Addiction
23
Subtypes of internet addiction:
Fig. 3: Subtypes of Internet Addiction.
Davis (2001) introduced a theoretical cognitive–
behavioral model on pathological or problematic Internet
use and differentiates between a generalized pathological
Internet use, which called generalized Internet addiction
(GIA), and a specific pathological Internet use, for which
used the term specific Internet addiction (SIA). Davis
argues that GIA is frequently linked to communicative
applications of the Internet and that a lack of social support
in real life and feelings of social isolation or loneliness are
main factors contributing to the development of GIA.
Maladaptive cognitions about the world in general and the
own Internet use in particular may then intensify the
Subtypes
Cyber-sexual
addiction
compulsive use of adult
websites for cybersex and
cyberporn
Cyber-relationship
addiction
Over-involvement in online
relationships.
Net compulsions Obsessive online gambling,
shopping or day-trading
Information overload Compulsive web surfing or
database searches
Computer addiction
Obsessive computer game
playing.
Internet Addiction
24
overuse of the Internet to distract from problems and
negative mood. In contrast, for the overuse of certain
Internet applications, for example, gambling sites or
pornography, a specific individual predisposition is the
main factor, Davis argues. Consequently, it is assumed that
GIA is directly linked to the options the Internet itself
provides, while SIA can also be developed outside the
Internet, but is aggravated by the enormous functions
offered by the Internet applications (Brand et al., 2014).
1.6 Consequences of internet addiction:
Unlike chemical dependency and substance abuse,
the Internet offers several direct benefits as a technological
advancement in our society and not a device to be criticized
as addictive (Montag & Reuter, 2015).
The hallmark consequence of substance dependence
is the medical implication involved, such as cirrhosis of the
liver due to alcoholism, or increased risk of stroke due to
cocaine use. While the physical side effects of utilizing the
Internet are mild compared to chemical dependency,
addictive use of the Internet will result in similar familial,
academic, and occupational impairment (Young, 1999).
Internet Addiction
25
Internet
Addiction
Academic
problems
Physical
Problems
Family
Problems
Occupational
problems
Fig. 4: Internet Addiction side effects.
*Physical problems: Disturbed sleep pattern, due to late
night log-ins, causes excessive fatigue often making
academic or occupational functioning impaired and m ay
decrease one‟s immune system, leaving the patient
vulnerable to disease.
The sedentary act of prolonged computer use may result in
a lack of proper exercise and lead to an increased risk for
carpal tunnel syndrome, back strain, or eyestrain (Young,
1999).
Eating irregularities, such as skipping meals, may affect
proper development especially in younger groups (Rosen
et al., 2014).
Internet Addiction
26
*Also to be mentioned that South Korea considers Internet
addiction one of its most serious public health issues (Ahn,
2007). After a series of 10 cardiopulmonary-related deaths
in Internet cafés (Choi, 2007) and a game-related murder
(Koh, 2007).
*Family problems: Although family problems may be a
trigger to internet addiction, it could be a result of such a
problematic behavior. Marriages, dating relationships,
parent-child relationships, and close friendships have been
noted to be seriously disrupted by ”net binges.” Patients
will gradually spend less time with people in their lives in
exchange for solitary time in front of a computer (Young,
1999).
*Academic problems: The Internet has been touted as a
premiere educational tool driving schools to integrate
Internet services among their classroom environments.
However, a study by young (1998) found that 58 % of
those identified as excessive users also received poor
grades. Similarly, Shields and Kane (2011) found that
students‟ grades were negatively associated with time spent
online. Another study has shown a relationship between
problematic Internet use and poor motivation to study,
Internet Addiction
27
especially in self-generated motivational domains (Reed &
Reay, 2015).
*Occupational problems: Any misuse of time in the
workplace creates a problem for managers, especially as
corporations are providing employees with a tool that can
easily be misused (Young, 1999).
1.7 Management of internet addiction:
The concern of Internet Addiction and its negative
consequences increased. However, no standard protocols
for clinical treatment of IA exist.
Traditional abstinence models are not practical
interventions when they prescribe banned Internet use as
the use of internet is legitimate in business and home
practice. The focus of treatment should consist of
moderation and controlled use. (Young, 1999) While
moderated Internet use is the primary goal of treatment,
abstinence of problematic applications is often necessary.
Specific applications such as a particular game, a particular
gambling site, or a particular sex site will trigger net
binges. Abstinence of the „trigger‟ application is essential
to help the client recover from the problematic
Internet Addiction
28
Management
Pharmacotherapy
Mood Stabilizers
Opioid Receptor
Antagonists
Anti-Depressant
Psychotherapy
CBT
Motivational
Interview
application(s) while retaining controlled use over legitimate
business Internet use (Montag & Reuter, 2015).
Fig. 5: Management of Internet Addiction.
Psychotherapy approach:
It includes a variety of interventions and a mix of
psychotherapy theories to treat the behavior and address
underlying psychosocial issues that are often co-existent
with this addiction (e.g., social phobia, mood disorders,
sleep disorders, marital dissatisfaction, or job burnout). The
most commonly discussed therapies are Cognitive-
Behavioral Therapy (CBT) and Motivational Interviewing
(Montag and Reuter, 2015).
Internet Addiction
29
A- Cognitive Behavioral therapy:
CBT is a familiar treatment based on the premise that
thoughts determine feelings. In general, clients are taught to
monitor their thoughts and identify those that trigger
addictive feelings and actions while learning new coping
skills and ways to prevent a relapse. CBT usually requires 3
months of treatment or approximately 12 weekly sessions.
With Internet addicts, it has been suggested that the early
stage of therapy should be behavioral, focusing on specific
behaviors and situations where the impulse control disorder
causes the greatest difficulty. Cognitive therapy is also used
to deal with maladaptive thoughts often associated with
addictive or compulsive behavior (Young, 2011).
Young outlined in her article in 2011, the three phases of
CBT-IA:
1- In the first phase, behavior modification is used to
gradually decrease the amount of time the addict spends
online.
2- In the second phase, cognitive therapy is used to address
denial that is often present among Internet addicts and
to combat the rationalizations that justify excessive
online use.
Internet Addiction
30
3- The third phase uses Harm Reduction Therapy (HRT)
for continued recovery and relapse prevention.
B- Motivational Interviewing:
Motivational interviewing is a goal-directed style of
counseling for eliciting behavior change by helping clients
to explore and resolve ambivalence. Motivational
interviewing involves asking open-ended questions, giving
affirmations, and reflective listening.
Questions about hours spent on using the internet,
preferable sites, consequences of internet usage, any
complain from family or friends, and about client‟s feeling
while offline.. etc. It is helpful for the client to gain a sense
of responsibility for his or her behavior. By allowing the
client to resolve their ambivalence in a manner that gently
pushes them, helps the client to be more inclined to
acknowledge the consequences of their excessive online
use and engage in treatment. Generally, the style is quiet
and eliciting rather than aggressive, confrontational, or
argumentative (Montag & Reuter, 2015).
Pharmacotherapy approach:
The literature provides small, but convincing
evidence for a link between biological brain abnormalities
Internet Addiction
31
in patients addicted to substances and similar brain
abnormalities in patients with IA (Camardese et al., 2015).
Activation of dopaminergic system results in feelings
of reward and pleasure, while hypo-dopaminergic function
stimulates cravings, which in turn affects attention to goals,
maintenance of cognitive control, and ability to make
action plans and then monitor action (Tanji & Hoshi,
2008).
Abnormal dopaminergic functions in nucleus
accumbens which linked to reward-deficient aberrant
behavior (RDAB) can be associated with both substanceuse
disorders, and also in uncontrolled internet gaming, and
other related behavioral addictions e.g. gambling and sex
addiction (Blum et al., 2012).
Depending on previous studies, the psychopathology
of Internet Addiction (impulsivity (Lin et al., 2011),
compulsivity (Meerkerk et al., 2009), craving (Cash et
al., 2012), and on the commonly associated comorbid
conditions, we could mention the following classes of
psychotropic medications for IA treatment.
1-Antidepressants in particular SSRI play a role in IA
treatment because of their ability to improve the resistance
Internet Addiction
32
to the urge and the control of compulsive repetition
(Camardese et al., 2015). Some Studies show close
relationship between serotonergic dys-regulation,
impulsivity, and symptoms of the obsessive-compulsive
spectrum, for which serotonergic drugs are known to be
effective (Goddard et al., 2008). Also the prevalence of
co-morbid depression among internet addicts (Lee YS. et.
al., 2008) increase the role of SSRI.
2- Opioid Receptor Antagonists, e.g. naltrexone and
nalmefene, inhibit dopamine release in the nucleus
accumbens and ventral pallidum and have been considered
for use in some behavioral addictions. A case study
reported that about using naltrexone for treatment of
cybersex male addict that resulted in 3 years of remission
(Bostwick & Bucci, 2008).
3- Mood Stabilizers usage among internet addicts aren‟t
investigated yet, but it could be promising group of
medication in such area as Lithium & mood stabilizing
anticonvulsants could be used in impulse control disorders.
Also valproate appears to be a fruitful medication to study
due to preliminary evidence demonstrating its anti-craving
efficacy (Maremmani et al., 2010).
Internet Gaming Disorder
33
Chapter 2
Internet Gaming Disorder
Internet gaming is one of the most popular internet
activities. It can be pleasurable and rewarding but some
individuals develop pathological manner of usage of such
activity. Through the past few years, researchers‟ interest
towards this pathological behavior increased and Internet
Gaming Disorder (IGD) was listed in Diagnostic and
Statistical Manual of Mental Disorders (DSM–5).
2.1 Definition:
As mentioned in DSM-5 (2014), IGD is persistent
and recurrent use of the internet to engage in games, often
with other players, leading to clinically significant
impairment or distress.
2.2 History & development of internet gaming:
In the 1980s, games such as Space Invaders, Pac
Man, and Donkey Kong were popularized. These were
single-player games against the machine and getting good
at the game only meant a high score and improvement of
the gamers‟ eye-hand coordination. By the 1990s, gamers
became immersed in a virtual world that they helped to
Internet Gaming Disorder
34
create instead of just playing a single player game. Games
such as Doom and Quake were introduced that allowed
players to create new rooms, customize their characters,
and specify the kinds of weapons used. By the late 1990s,
the gaming industry exploded. Manufactures such as Sony
and Microsoft have developed more sophisticated and
interactive features into their games and the technology has
become much more portable and mobile making online
games accessible anytime and anywhere (Young, 2009).
Players can select more detailed representations for
their characters. For example, human characters, players
can select skin color, hair color, height, weight, and gender.
They also can decide on a character‟s profession, ranging
from a banker, lawyer, dancer, engineer, thief, elf, or
gnome, depending on the game. Each player must choose a
name for the character. Some take great care in determining
just the right name. They spend hours living as this “other
person” and begin to identify with the character that feels
more real and less fictional the longer they play (Yee,
2006).
Researchers show interest with massively
multiplayer online role-playing games (MMORPG).
MMORPG are games where one creates an avatar in a
Internet Gaming Disorder
35
virtual fantasy world and interacts with other online players
to complete missions and journeys (Taneli et al., 2015).
Some reports reveal that MMORPG players have a high
rate of IGD (Billieux et al., 2015; Achab et al., 2011).
2.3 Prevalence:
Information about prevalence of IGD is inconclusive
because of different criteria used for diagnosis, also cross
cultural variations could play role. Studies show different
prevalence rates for example a study in Singapore reported
prevalence of 8.7% among youth (Choo et al., 2010) &
Hong Kong another study showed 15.6% were identified to
have gaming addiction (Wang, 2014). Only 1.6% of
adolescents in a European study met full criteria for IGD
(Mu¨ller KW. et. al., 2015) study in Oxford University
showed a very small proportion of the general population,
between 0.3%and 1.0%, that might qualify for IGD
diagnosis (Przybylski et al., 2016).
2.4 Effects of video games:
Video games have beneficial impacts in domains like
cognitive, emotional, motivational, social (Granic et al.,
2014). However, several studies showed its negative
impacts e.g. addiction possibility, exposure to graphic
Internet Gaming Disorder
36
Cognition
*Improve
selective
attention.
*Train
Spatial
memory
*May cause
desensetizat
ion
Emotion
*Improve
the Mood
*promote
relaxation
*Maladaptive
svoidant
strategy.
*May
increase
aggression
and
decrease
empathy.
Social
*May Help
learning
Social Skills.
BUT
*May
Interfere
with Real
life and
negatively
affect it.
Motivation
*Improve the
intelligence
through
intermettie-nt
reinforcement
(Failure used
as Motive)
violence, contribution to obesity, cardio-metabolic
problems (Palaus et al., 2017).
Fig. 6: Effects of Internet Games.
Playing Function: Erikson in 1977 proposed that play
contexts allow children to test social experiences and
simulate alternative emotional consequences, which can
then achieve resolution outside the play context. Also in
1962 Piaget theorized that made-believe play helps children
to reproduce real-life conflicts, to find out ideal resolutions
for their own pleasure, and to improve negative feelings
(Granic et al., 2014).
Internet Gaming Disorder
37
1- Cognition: Action game players appear to have better
selective attention than non-action players e.g. role playing
gamers (Krishnan et al., 2013). Comparing video game
players (VGPs) & non-VGPs, a study reported that habitual
gamers have more efficient top down attention (sustained
attention) through their better ability to allocate their
attention resources more efficiently and filter out irrelevant
information more effectively (Bavelier et al., 2012). Also
spatial skills can be trained by using video games in a
relatively brief period and those benefits last over an
extended period of time, and can be transferred to other
spatial tasks outside the video game context (Uttal et al.,
2013).
2-Motivation: Video games use failure as motivational
tools and provide only intermittent chances for success. In
1974, Kidell reported that intermittent reinforcement
models are the most effective in training new behavior.
Such games give players a lesson about Persistence in the
face of failure to gain rewards (Ventura et al., 2013). Two
theories of intelligence were proposed, entity theory of
intelligence (which maintains that intelligence is an innate
trait, something that is fixed and cannot be improved) and
incremental theory of intelligence (in which intelligence is
Internet Gaming Disorder
38
malleable, something that can be cultivated through effort
and time) (Dweck & Molden, 2005). Granic & her team
(2014) considered video games as an ideal training ground
for acquiring an incremental theory of intelligence because
they provide players concrete, immediate feedback (e.g.,
points, coins, dead ends in puzzles) regarding specific
efforts players have made.
3-Emotions: No doubt, games are fun and they can bring
positive emotions to players. For example, some studies
reported that playing games with minimal interfaces, shortterm
commitments, and a high degree of accessibility (e.g.
Puzzle video games) can improve players‟ moods, promote
relaxation, and prevent anxiety (Russoniello et al., 2009).
However, it is important to study the extent to which
turning to video games to feel better is adaptive and at what
point using games becomes an avoidant strategy that leads
to more negative outcomes. Other studies suggest that
exposure to violent video games is a causal risk factor for
increased aggressive affect and for decreased empathy
(Anderson et al., 2010). It could occur via changes in
cognitive and personality factors associated with
desensitization (Bartholomew et al., 2005). It is unclear
whether playing a violent video game for a brief period of
Internet Gaming Disorder
39
time would affect measures of desensitization to violence
or of empathy for violence victims. Systematic
desensitization therapies suggest that repeated exposures to
gory scenes of violence and to pain and suffering of others
will have some impact on a person‟s physiological
reactions to new scenes of violence (desensitization) and on
empathetic responses to victims (Anderson et al., 2010).
4-Social: Today, video games became of social nature
unlike those in previous decades. Over 70% of gamers play
their games with a friend, either cooperatively or
competitively (Entertainment Software Association,
2012). Some studies propose that gamers are rapidly
learning social skills and prosocial behavior that might
generalize to their peer and family relations outside the
gaming environment (Granic et al., 2014; Gentile &
Gentile, 2008). A number of studies focused on the relation
between civic engagement and gaming, they reported that
adolescents who played games with civic experiences (e.g.,
massive multiplayer online role-playing game
(MMORPG)) were more likely to be engaged in social and
civic movements in their everyday lives (e.g., volunteering
and raising money for charity.. etc.) (Lenhart et al., 2008).
However, in a study participants were divided into groups
Internet Gaming Disorder
40
depending on the type of game played. The MMORPG
group differed significantly from other groups after 1
month, reporting more hours spent playing, worse health,
worse sleep quality, and greater interference in “real-life”
socializing and academic work (Smyth, 2007).
Other studies reported that temporary increase in
aggressive cognition and affect, caused by exposure to
violent video games, might interfere with empathic
thoughts and emotions that frequently underlie helping
behavior (Anderson et al., 2010). Some types of video
game like those aiming at saving the princess and killing
enemies, might prime a type of “hero” script and thereby
lead to an increased likelihood of certain limited types of
helping behavior.
2.5 Diagnosis and DSM-5 proposed Criteria:
Internet gaming disorder (IGD) listed in Section III,
Conditions for Further Study of the 5th edition of the
Diagnostic and Statistical Manual of Mental Disorders
(DSM–5). (DSM-5, 2014) See Table 3
In DSM-5, they stressed on only non-gambling
internet games can be included in this disorder. Also other
purposes for internet usage like social, professional, or
sexual sites are excluded.
Internet Gaming Disorder
41
•Thinking about previous gaming or
anticipate the next game. It becames the
dominante activity.
Preoccupation
• When internet gaming is away, individual
suffers irritability, anxiety, or sadness.
Withdrawal
•Need to spend increasing amounts of time
emgaged in inernet games.
Tolerance
•Unsuccessful attempts to reduce, control
or stop internet games.
Loss of control
•Loss of interests in previous hobies and
entertainment as a result of, and with the
exception of, internet games.
Give up other activities
•continued excessive use of internet games
despite knowledge of negative psychosocial
problems.
Continue despite problems
•Deceiving family members, therapists, or
others regarding the amount of internet
gaming.
Deception
•Use internet games to escape or relieve a
negative mood.
Escape adverse mood
•Has jeopardized or lost a significant
relationship, job, or educational or career
opportunity because of internet gaming.
Loses
To some authors IGD is a subtype of video game
addiction and they don‟t differentiate between problematic
video game use and problematic online game use (Porter
et al., 2010). They consider the internet as a medium that
could enhance a problematic or addictive behavior
(Griffiths & Pontes, 2014).
Table 2: DSM-5 proposed criteria of IGD.
Internet Gaming Disorder
42
2.6 Management of IGD:
Treatment services of Internet Gaming Disorder are
increasing worldwide, especially in east Asia.
Many studies were done to assess treatment trials
even prior to inclusion of IGD in DSM-5 (King et al.,
2011). But there is in sufficient treatment literature with
long term therapeutic benefit (King and Delfabbro, 2014).
Trials were similar to those mentioned in the
previous chapter “Internet Addiction”, with a larger
evidence base of cognitive-behavioral therapy than other
therapies (King et al., 2017).
Sleep
43
Chapter 3
Sleep
Sleep is one of our daily routines. Everyone needs
sleep, although its biological purpose is a mystery.
It is a homeostatically regulated body process, and
prolonged sleep deprivation is fatal. (Assefa et. al., 2015)
According to the National Institute of Mental Health, sleep
is endogenous, recurring, behavioral states that reflect
coordinated changes in the dynamic functional organization
of the brain and that optimize physiology, behavior, and
health. Homeostatic and circadian processes regulate the
propensity for wakefulness and sleep (Assefa et al., 2015).
Broadly it is divided into two types, rapid eye movement
(REM) sleep and non-rapid eye movement (NREM) sleep
(Walker, 2005).
3.1 Anatomy of sleep:
Several structures in the brain play role in sleep-wake
cycle.
1- Hypothalamus: Within the hypothalamus is
the suprachiasmatic nucleus (SCN) – clusters of thousands
of cells, called the Biological clock. (French &
Muthusamy, 2016) It is responsible for the circadian
Sleep
44
rhythm which is is tightly regulated by changes in the
lighting cycle (Correa et al., 2017).
2- Brain stem: communicate with the hypothalamus to
regulate sleep and wake. The reticular activating system
modulate our sleep–wake states, also affects our response
to the world around us through its projections to the
thalamus and then the cortex (Garcia-Rill et al., 2013).
3- Thalamus: During transition from wakefulness to
NREM sleep, the thalamo-cortical neurons‟ membrane
shows electrical changes that lead to inhibition of the
incoming messages & deprivation of signals from the
outside world (Steriade, 2003).
4- Pineal gland: as a neuro-endocrine gland that can
produce melatonin, which helps to put us to sleep, in
response to light-dark cycle (Axelrod, 1983).
5- Amygdala: involved in emotional processing. It shows
strongest activity during REM sleep (Genzel et al., 2015).
3.2 Sleep cycle:
It seems simple mechanism, sleeping when we are
tired at night and waking up refreshed at morning but it
carries more complicated details. Electroencephalography
(EEG) helped researchers to relate sleep to brain activity
Sleep
45
and smashed the concept that the brain has no activity
during sleep (Kanda et al., 2016).
In EEG, there are four main rhythms in brain activity
(Estrada et al., 2004) as shown in table 3.
Table 3: Main brain rhythms on EEG
Rhythm Voltage Frequency Shape
Beta
Waves
Low voltage
(around 5
μV)
14 to 30 Hz
Alpha
Waves
Higher than
Beta
8 to 13 Hz
Theta
waves
Greater than
alpha
4 and 7 HZ
Delta
waves
Greatest
amplitude
3 Hz or Less
Stages of Sleep:
Stage 0 of sleep: (Awake) Eyes are opened, with rapidly
changing EEG and prominent Beta and Alpha waves
(Acharya et al., 2005).
Stage 1 of sleep: considered the midway between sleep and
wakefulness. It lasts for a short time about 5 – 10 minutes
(Asaad, 2013) with Alpha and Theta waves on EEG
(Estrada et al., 2004).
Sleep
46
Stage 2 of Sleep: in which, the brain produces the low
voltage waves of stage one plus a sharp, high voltage
transient wave known as K-Complex and bursts of waves
having a frequency of 12 to 15 Hz called sleep spindles
(Estrada et al., 2004). It lasts for about 20 minutes
(Asaad, 2013).
Stage 3 of Sleep: is considered a transitional zone between
light and deep sleep. Delta waves started to emerge in the
background (Acharya et al., 2005).
Stage 4 of Sleep: stage 4 and 3 called Deep Sleep stage
lasts for around 30 minutes (Estrada et al., 2004). It is
associated with high amplitude waves but with sloe
frequency less than 2 HZ (Delta waves) (Acharya et al.,
2005).
Stage 5 of Sleep / REM stage/ Paradoxical sleep: is
characterized by rapid eye movements along with the
occasional muscular twitches, increased respiratory rate,
dreaming, and increased brain blood flow (Asaad, 2013).
The brain activity is reversed from Stage 4 to a pattern
similar to stage 1 (Estrada et al., 2004).
Sleep
47
3.3 Sleep Regulation:
Many researchers think that there is more than one
center controlling sleep, which activate and inhibit each
other. Others reported that sleep regulation results from
interaction between homeostatic process (Process S) and
process controlled by circadian pacemaker (Process C)
(Borbély et al., 2016).
Process S is driven by the depletion of glycogen and
accumulation of adenosine in the forebrain that disinhibits
the Ventrolateral preoptic nucleus, leading to inhibition of
the ascending reticular activating system. (Schwartz &
Roth, 2017) Process C is mainly derived by the
suprachiasmatic nucleus (SCN) to keep an internal daynight
rhythm. Brain stem, Hypothalamus, and basal
forebrain are responsible for arousing the thalamus and
cortex. Also they are inhibited during sleep by ȣaminobutyric
acid (GABA) containing neurons (Saper,
2005). On the other hand, serotonin (5-HT) and dopamine
(DA) function to promote waking and to inhibit slow wave
sleep and/or rapid-eye-movement sleep (Monti & Jantos,
2008). However, in the 11th edition of synopsis (2015), it
was mentioned that prevention of serotonin synthesis or
destruction of dorsal raphe nucleus, that contains nearly all
Sleep
48
brain‟s serotonergic cells, reduces sleep for considerable
time. Melatonin is considered as an internal sleep
„facilitator‟ in humans, so it could be used for treatment of
insomnia and the readjustment of circadian rhythms
(Cajochen et al., 2003).
3.4 Sleep Functions:
The Exact Function of sleep is still a biological
mystery.
*Emotions: Neuroimaging studies reveal significant
activity increases during REM sleep in emotion-related
regions both sub-cortically, in the amygdala, striatum and
hippocampus, and cortically, in the insula and medial
prefrontal cortex (Dang-Vu et al., 2010). Depressed
persons have marked disruptions of the REM sleep patterns
in the form of shortened REM latency (60 minutes or less),
increased percentage of REM sleep, and shift of REM sleep
from last half of night to first half (Sadock et al., 2015).
Also accumulated sleep loss leads to an amplification of
negative emotions in response to disruptive daytime
experiences, while blunting the affective benefit associated
with goal-enhancing activities (Zohar et al., 2005).
Sleep
49
*Memory: Sleep support system consolidation and
synaptic consolidation of memories and coordinate the reactivation
and redistribution of hippocampus-dependent
memories to neocortical sites (Diekelmann and Born,
2010).
*Metabolism: Decreased sleep amount increases energy
and fat intakes. If sustained and not compensated by
increased energy expenditure, it may lead to obesity (St-
Onge et al., 2011). One of the studies reported that
prolonged sleep restriction decreases resting metabolic rate,
and increases postprandial plasma increasing the risk of
obesity & diabetes. It could be normalized within 9 days of
recovery sleep and stable circadian re-entrainment (Buxton
et al., 2012).
*Homeostasis: It‟s known that sleep has a restorative
function and have a role in thermoregulation and other
homeostatic functions (Sadock et al., 2015).
3.5 Sleep requirements:
Sufficient sleep duration is variable across the life
span and from person to person. The following table (Table
4) is going to show the recommended hours of sleep to
different age groups. It‟s to be mentioned that sleep
Sleep
50
duration outside the recommended range could be normal
but extreme deviation would affect individual‟s health and
well-being (Hirshkowitz et al., 2015)
Table 4: The normal range of sleep duration according to
different age groups
Age Sleep hours needed
Infants 12 - 15
Pre-scholar 10 - 13
School aged children 9 - 11
Adults 7 - 9
Older adults (> 64yr) 7-8
Sleep needs increase with increased physical work,
pregnancy, illness, and increased mental activity (Sadock
et al., 2015).
3.6 Sleep Disorders:
There are two major classifications of sleep disorders
including the Diagnostic and Statistical Manual of Mental
Disorders, fifth edition (DSM-5) and International
Classification of Sleep Disorders third edition (ICSD3).
In DSM-5, the sleep-wake disorders includes 10 disorders
or disorder groups: Insomnia disorder, Hypersomnolence
disorder, Narcolepsy, Breathing-related sleep disorders,
Circadian rhythm sleep-wake disorders, non-rapid eye
Sleep
51
movement (NREM) sleep arousal disorders, nightmare
disorder, rapid eye movement (REM) sleep behavior
disorder, restless legs syndrome, and substance/medicationinduced
sleep disorder (DSM-5, 2014). In ICSD3, seven
major categories of sleep disorders were identified,
including insomnia disorders, sleep-related breathing
disorders, central disorders of hypersomnolence, circadian
rhythm sleep-wake disorders, sleep-related movement
disorders, parasomnias, and other sleep disorders (Sateia,
2014).
In this part of the chapter we won‟t give much details
about the whole sleep problems but we would like to stress
on those hypothesised to have relation to internet addiction
and internet gaming disorders e.g. insomnia, short sleep
duration and poor quality of sleep (Lam, 2014).
Insomnia:
As mentioned in DSM-5 (2014), a prominent complaint of
dissatisfaction with sleep quantity or quality with one (or
more) of the following symptoms:
1- Difficulty initiating sleep.
2- Difficulty maintaining sleep. e.g. frequent awakening
or difficulty falling asleep after awakening.
Sleep
52
3- Early morning awakening with inability to return to
sleep.
The previous symptoms should occur at least 3
nights per week and to persist for at least 3 months. Also it
should lead to significant distress or impairment of any of
the areas of functioning and not caused by any other illness
or substance effect.
Insomnia leads to many negative consequences as
irritability, poor memory, fatigue and lack of energy that
affect work sufficiency. Also it would lead to accidents and
sleepiness while driving (Cunnington et al., 2013).
Poor Sleep Quality:
Buysse proposed definition of Sleep Health as a
multidimensional pattern of sleep-wake-fullness, adapted to
individual, social, and environmental demands, promoting
physical and mental well-being. And he reported that good
sleep health associated with individual satisfaction,
appropriate timing, adequate duration, high efficiency, and
sustained alertness during waking hours (Buysse, 2014).
In sufficient sleep or poor quality sleep may lead to
impairment in cognitive regulation and reward-related brain
function and to increase in health compromising behaviors
such as substance use especially in adolescents (Hasler et
Sleep
53
al., 2012; McKnight-Eily et al., 2011) Telzer & et al.
(2013), on their study examining the effects of poor sleep
quality on adolescents, reported that poorer sleep
individuals may be more apathetic, less confident, greater
likelihood of engaging in risk taking and less caring during
decision making. This suggests that they suffer from less
efficient cognitive control brain function. Another study
using MRI to detect cortical atrophy, they found that poor
sleep quality is associated with longitudinal cortical
atrophy (Sexton et al., 2014). In animal study, it was found
that prolonged restriction or disruption of sleep may lead to
reduced cell proliferation, cell survival, and neurogenesis
within the hippocampus (Meerlo et al., 2009). One of the
scary negative consequences of poor sleep quality, is the
increased risk for suicidal especially among those suffering
from difficulty falling asleep and non-restorative sleep
(Bernert et al., 2014).
Disturbed Circadian Rhythm:
During nocturnal sleep, pro-inflammatory hormones
and cytokines are synchronized to facilitate the initiation of
adaptive immune responses in lymph nodes while during
daytime activity, anti-inflammatory signals, hormones, and
cytokines appear to support immediate effector functions
Sleep
54
(Levi et al., 1991). So when this intrinsic 24-h sleep-wake
rhythm is disrupted, health would be compromised
(Hastings et al., 2008).
5.7. Sleep and Internet
No doubt that the internet became an integral part of
our life. Also sleep maintains our homeostatic function.
We need both of them but how they could affect each other
is one of the important issues that recent studies started to
demonstrate.
Excessive internet use leads to irregular sleep
patterns due to an irregular bedtime schedule (Kamal
& Mosallem, 2013; Kim et al., 2010) And this could lead
to disturbed circadian rhythm (Chen & Gau, 2016).
A Korean study showed that the deeper the addiction
of smart phones the lower the sleep quality (Heo et al.,
2015).
Also among those suffering from Internet Gaming
Disorder (IGD), Sleep problems were detected (Satghare
et al., 2016; Rehbein et al., 2015).
Sleep
55
Some researchers found that not only internet
addiction may affect sleep but also people who suffer from
decreased ability to fall asleep at night, would engage in
internet usage more than others keeping them at higher risk
of internet addiction (Chen & Gau, 2016).
Methodology
56
Methodology
1) Design:
Comparative observational Cross sectional study have been
applied during the academic year 2016-2017 in Ain Shams
University.
2) Participants:
Data were collected from 596 students of the first year of
Ain Shams University. from randomly selected 6 different
faculties (3 theoretical & 3 practical faculties).
Operational Definition of adolescents:
* According to the Unicef: the Adolescence period is
divided into two stages: early adolescence (10–14 years)
and late adolescence (15–19 years) (Unicef, 2011)
* According to the American Academy of Pediatrics, it‟s
roughly divided into three stages: early adolescence (11-14
years), middle adolescence (15-17 years) and late
adolescence (18-21years).
*Inclusion Criteria:
- Being a student of first year in Ain Shams University.
- Age range: 17 – 19 years old.
Methodology
57
- Both sexes.
*Exclusion Criteria:
- Being a known psychiatric patient or receiving
psychiatric medications.
- Exceeding the Age limits.
3) Tools:
1-Informative designed questionnaire: containing
personal data (including Age, Sex, faculty, and grade) and
further questions to assess the following:
* Most Frequently used internet activities (Information
search/Online gaming/Online Shopping/Chatting/Social
networks/Pornographic sites/Downloading/Checking email)
* Hours spent online per day.
2-Socio-Economic Status (SES) Scale: it is the updated
form of the scoring system of Fahmy and El-Sherbini for
measurement of socioeconomic status. It has 7 domains
with a total score of 84. The Socioeconomic level was
classified into very low, low, middle and high levels
depending on the quartiles of the score calculated (El-
Gilany et al., 2012).
Methodology
58
3-Young Internet Addiction test (IAT): is a 20-item scale
that measures the presence and severity of Internet
dependency among adults. It measures characteristics and
behaviors associated with compulsive use of the Internet
that include compulsivity, escapism, and dependency.
Questions also assess problems related to addictive use in
personal, occupational, and social functioning. Questions
are randomized and each statement is weighted along a
Likert-scale continuum that ranges from 0 = less extreme
behavior to 5 = most extreme behavior for each item
(Young, 1999).
We used the Arabic version which is the translated &
validated form of the original test (Reda et al., 2012).
4-Internet Gaming Disorder (IGD) Scale: using the short
dichotomous form which is 9-item scale that can discern 3
groups: normal gamers, risky gamers, disordered gamers
(Lemmens et al., 2015).
We used the Arabic version that we adapted.
5-The Pittsburgh Sleep Quality Index (PSQI): is a selfrated
questionnaire which assesses sleep quality and
disturbances over a 1-month time interval. Nineteen
individual items generate seven ”component” scores:
Methodology
59
subjective sleep quality, sleep latency, sleep duration,
habitual sleep efficiency, sleep disturbances, use of
sleeping medication, and daytime dysfunction. The sum of
scores for these seven components yields one global score
(Buysse et al., 1989).
6-The Mini International Neuropsychiatric Interview
for children and adolescents (MINI KID): developed
jointly by psychiatrists and clinicians in the US and Europe,
for Diagnostic and Statistical Manual of Mental Disorders-
IV (DSM-IV) and International Classification of Diseases
(ICD) 10th revision psychiatric disorders. With an
administration time of approximately 15 min, it was
designed to meet the need for a short but accurate
structured psychiatric interview for multicenter clinical
trials and epidemiology studies (Sheehan et al., 1998).
4) Procedure:
1- Adaptation of the internet gaming disorder scale
Firstly, permission from the author had been obtained to
translate & validate the Internet Gaming Disorder (IGD)
scale.
Then the original scale was translated from English
into Arabic by a professional translator then translated back
Methodology
60
into English by psychiatric consultant who is unaware of
the original version of the scale. The original English and
back-translated versions were compared by 3 experts to
ensure consistency of the 2 versions and to reconcile any
problematic items. Secondly, two versions of the IGD scale
(English & Arabic versions) were distributed in two
different sittings among 26 bilingual individuals who use
internet games and the results were compared to make sure
that both versions give the same results.
2- Sampling:
Sampling was conducted in March and April 2017.
Printed copies of the previously mentioned scales
(Informative designed questionnaire, IAT, IGD scale, SES
Scale, PSQI) were distributed among Ain Shams University
first year students after obtaining ethical approvals and
necessary university authorities‟ permission. We randomly
selected 6 faculties, 3 of them practical and the others were
theoretical. The purpose of the test was fully explained to
all participants, who gave informed consent to take part in
the study. Then a randomly selected sample of the
pathological users and non-pathological users has been
subjected to the MINI-Kid to explore associated psychiatric
morbidity.
Methodology
61
The responses were collected and confidentiality of
the participants was ensured by locking away the filled in
surveys in a locker with access limited to the researchers.
3- Data analysis:
=> At first data analysis for translating and validating
Internet Gaming Disorder (IGD) scale:
Three stages of analysis were carried out.
The first was on a sample of 26 bilingual subjects to assess
the agreement between the Arabic and English Versions.
The second was for assessing the internal
consistency of the Arabic scale, using a sample of 204
university students.
The third was to assess the reliability of the Arabic
scale through measuring the agreement among the
test/retest (with 30 days interval). The collected data was
revised, coded, tabulated and introduced to a PC using
Statistical package for Social Science (IBM Corp. Released
2011. IBM SPSS Statistics for Windows, Version 20.0.
Armonk, NY: IBM Corp). Data has been presented and
suitable analysis was done according to the type of data
obtained for each parameter.
Methodology
62
=> Secondly Analysis for the results of the Scales:
Descriptive analyses were used to describe scores of the
scales considering mean, Standard deviation (± SD) and
range for parametric numerical data and frequency and
percentage of non-numerical data.
Student T Test was used to assess the statistical
significance of the difference between two study group
means as in testing the relation between Internet Addiction
Test scores and students‟ sex and sleep quality.
ANOVA test was used to assess the statistical significance
of the difference between more than two study group
means as in testing relation between Internet Addiction and
Internet Gaming disorder.
Chi-Square test was used to examine the relationship
between two qualitative variables and Fisher’s exact test
was used to examine the relationship between two
qualitative variables when the expected count is less than 5
in more than 20% of cells.
Correlation analysis (using Pearson’s method) was used
to assess the strength of association between two
quantitative variables. The correlation coefficient denoted
Methodology
63
symbolically ”r” defines the strength and direction of the
linear relationship between two variables.
All statistical analyses were conducted using the
IBM SPSS Statistics (20.0) software. A 𝑃 value of less than
0.05 was considered statistically significant.
Results
64
Results
The results of the main two steps of data analysis were as
follows:
1- Validation of the internet gaming disorder scale
Arabic version
Stage 1: Questionnaire validity was assessed using Kappa
statistics to compute the measure of agreement between the
Arabic and English version of questionnaires, Kappa values
ranged from 0.351 to 0.920 which indicate fair to almost
perfect agreement. The P value was less than 0.01 for all
questions except for question no. 4 which was the only one
with fair agreement unlike other questions showing higher
results.
Results
65
Table 5: Validity of the Arabic version of the scale through
measuring the Agreement between both versions
Measurement of
Agreement
(Kappa)
Approx. Sig.
Q1/AR*Q1/EN .838 .000
Q2/AR*Q2/EN .920 .000
Q3/AR*Q3/EN .689 .001
Q4/AR*Q4/EN .351 .064
Q5/AR*Q5/EN .595 .003
Q6/AR*Q6/EN .651 .001
Q7/AR*Q7/EN .783 .000
Q8/AR*Q8/EN .848 .000
Q9/AR*Q9/EN .595 .003
AR: Arabic version. EN: English version.
Stage 2: Alpha (Cronbach) was used to assess the internal
consistency of the scale. It was 0.612 which is an
appropriate value.
Stage 3: Questionnaire reliability was assessed using
Kappa statistics, Kappa values ranged from 0.271 to 0.848
which means fair to almost perfect agreement.
Results
66
Table 6: Reliability of the Arabic version through
measuring the agreement between answers in day 1 and
those in day 30
Measurement of
Agreement
(Kappa)
Approx. Sig.
Q1/D1*Q1/D30 .772 .000
Q2/D1*Q2/D30 .674 .000
Q3/D1*Q3/D30 .533 .000
Q4/D1*Q4/D30 .476 .001
Q5/D1*Q5/D30 .327 .025
Q6/D1*Q6/D30 .271 .064
Q7/D1*Q7/D30 .700 .000
Q8/D1*Q8/D30 .848 .000
Q9/D1*Q9/D30 .614 .000
2- Results of the search scales:
A) Personal characteristics of the sample:
Our sample consisted of 596 student of first year of
Ain Shams University. The mean of their age was 18.5
years, and 55.5% of the sample was females.
Most of the sample was of middle socioeconomic level.
Data was collected from 6 different faculties: Commerce,
Literature, Low, Medicine, Science, and Engineering.
Grades of the students were divided into Excellent, Very
Good, Good, Accepted, Failed. All of those characteristics
are displayed in table (7).
Results
67
Table 7: Personal characteristics of the sample:
Mean ±SD
Age (Yr) 18.6 .5
Sex (n %) Male 265 44.5%
Female 331 55.5%
SES (n %) Low 31 5.2%
Middle 363 60.9%
High 202 33.9%
Faculty (n %) Commerce 106 17.8%
Law 101 16.9%
Literature 100 16.8%
Medicine 100 16.8%
Science 99 16.6%
Engineering 90 15.1%
Grades (n %) Excellent 52 8.7%
V. Good 138 23.2%
Good 217 36.4%
Accepted 142 23.8%
Failed 47 7.9%
Results
68
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Percent
Negative Mild Moderate Severe
Internet Addiction
B) Internet behavior among the sample:
Students used the internet for about 2-8 hours/day
and most of them using it for more than 1 year. According
to the results of the Internet Addiction Test (IAT), 46.6% of
the sample had moderate internet addiction. Through
asking the students about the most visited sites we found
that 89.6% from them were using the social media. See
table (8).
Fig. 7: Internet Addiction prevalence among the sample.
Results
69
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Percent
Normal Risky Disordered
Internet Gaming Disorder
Table 8: Prevalence of internet addiction among selected
sample, and the most visited sites
Mean ±SD
Internet Addiction
(n %)
Normal 54 9.1%
Mild 242 40.6%
Moderate 278 46.6%
Severe 22 3.7%
Most visited site
(n %)
Social Media 517 89.6%
Information Surfing 43 7.5%
Internet Gaming 13 2.3%
Pornographic Sites 4 .7%
Hrs. online/day 5.74 3.82
Results showed that 63.4% from the students were
not using online games. Among the rest of the sample 48%
were at risk to develop Internet Gaming Disorder (IGD).
Fig. 8: Internet Gaming Disorder prevalence.
Results
70
Also we found that 46.7% from game players use
Massive Multiplayer Online (MMO) games. See table 9.
Table 9: Online gaming behavior (number of users of
online games, type of the game they play and prevalence of
IGD among players)
N %
Playing online Games No 378 63.4%
Yes 218 36.6%
Type of online Games MMO 85 46.7%
Not MMO 97 53.3%
Internet Gaming Disorder (Among players
only)
Normal 77 35.3%
Risky 106 48.6%
Disordered 35 16.1%
Through further analysis of the Internet Addiction
Test (IAT) we found that the most prominent symptom
among participants is lack of control followed by
anticipation. See table 10.
Results
71
0%
10%
20%
30%
40%
50%
60%
70%
Mean Percent
Salience
Excessive usage
Work negligence
Anticipation
Lack of control
Social life negligence
Score percentage
Table 10: Symptoms and patterns of Internet Addiction
Mean ±SD
Salience score percentage 42% 17%
Excessive usage score percentage 52% 18%
Work negligence score percentage 44% 21%
Anticipation score percentage 55% 25%
Lack of control score percentage 61% 22%
Social life negligence score percentage 48% 24%
Fig. 9: Symptoms and patterns of Internet Addiction.
Results
72
Sleep Quality
37.6%
62.4%
Good Sleep Quality Bad Sleep Quality
C) Sleep Quality of the students:
According to Pittsburgh Sleep Quality Index (PSQI),
62.4% of the participants were suffering from poor sleep
Quality.
Fig. 10: Sleep Quality among participants.
We found high significant relation between sleep quality
and Internet Addiction, as we found that around 72% from
severe internet addict suffer from poor sleep quality in
comparison with 40% of non-addicts. See table 11.
Also a high significant relation (with P value less
than 0.001) between sleep quality and IGD was founded.
As in the up-coming figure we could find that 68% from
disordered players and 67% from risky players suffer from
poor sleep quality.
Results
73
0%
10%
20%
30%
40%
50%
60%
70%
Normal Risky Disordered
Percent
Internet gaming disorder
Good Sleep Quality Bad Sleep Quality
Table 11: Relation between sleep quality and Internet
Addiction
Internet Addiction
P Sig
Normal Mild Moderate Severe
N % N % N % N %
Sleep
Quality
Good Sleep
Quality
32 59.3% 107 44.2% 79 28.4% 6 27.3%
0.001* HS
Bad Sleep
Quality
22 40.7% 135 55.8% 199 71.6% 16 72.7%
Fig. 11: Relation between IGD and Sleep Quality.
Results of answers of Q14 in IAT asking about how
often students lose sleep due to being online revealed that
most of them did so. (25% always losing sleep time to stay
online). Table 12
Results
74
Table 12: Answers of students on Q14 of IAT.
Mean ±SD
Affection of sleep because of Internet
(n %)
Never 16 2.7%
Rarely 63 10.6%
Occasionally 119 20.0%
Frequently 145 24.3%
Often 103 17.3%
Always 150 25.2%
D) Mini Kid:
106 students from the total sample were subjected to
the Mini-Kid to explore psychiatric disorders among the
sample. Around 52% from those subjected to this test had
no psychiatric disorder.
Table 13: Mini-Kid results among the sample
Mean ±SD
MINI-Kid (among tested
subjects)
(n %)
Normal 56 52.8%
SUD 10 9.4%
ADHD 9 8.5%
Generalized Anxiety
Disorder
8 7.5%
Major depression disorder 7 6.6%
Adjustment Disorder 6 5.7%
Social phobia 4 3.8%
PTS 2 1.9%
Obsessive Compulsive
disorder
2 1.9%
Agoraphobia 2 1.9%
90% from Substance abusers and 100% from those
suffering from Adjustment disorder were moderate internet
addicts. Also a high significant relation with Post traumatic
stress disorder and agoraphobia was revealed. See table 14.
Results
75
Also significant relation between Internet Gaming
Disorder (IGD) and some psychiatric disorders was noticed
e.g. Substance abuse, Social phobia, Adjustment, and Post-
Traumatic Stress Disorder. See table 15.
Table 14: High significant relation between IA and
Psychiatric disorders
INTERNET ADDICTION P Sig
Normal Mild Moderate Severe
N % N % N % N %
MINIKid
Normal 1 1.8% 22 39.3% 3
2
57.1% 1 1.8% 0
.
0
0
1
HS
SUD 1 10.0% 0 .0% 9 90.0% 0 .0%
ADHD 0 .0% 5 55.6% 4 44.4% 0 .0%
GAD 1 12.5% 3 37.5% 4 50.0% 0 .0%
Major
depression
disorder
0 .0% 5 71.4% 1 14.3% 1 14.3%
Adjustment
Disorder
0 .0% 0 .0% 6 100.0% 0 .0%
Social
phobia
0 .0% 3 75.0% 1 25.0% 0 .0%
PTS 0 .0% 0 .0% 1 50.0% 1 50.0%
OCD 0 .0% 2 100.0
%
0 .0% 0 .0%
Agoraphobia 0 .0% 0 .0% 2 100.0% 0 .0%
Results
76
Table 15: High significant relation between IGD and
psychiatric disorders
Internet Gaming Disorder P Sig.
Normal Risky Disordered
N % N % N %
MIN
I-Kid
Normal 1
7
30.4
%
2
7
48.2
%
1
2
21.4% 0.001 HS
SUD 1 11.1
%
6 66.7
%
22.2%
ADHD 2 25.0
%
5 62.5
%
1 12.5%
GAD 4 50.0
%
3 37.5
%
1 12.5%
Major
depression
disorder
2 28.6
%
5 71.4
%
0 .0%
Adjustment
Disorder
1 16.7
%
4 66.7
%
1 16.7%
Social phobia 0 .0% 2 50.0
%
2 50.0%
PTS 0 .0% 1 50.0
%
1 50.0%
OCD 2 100.
0%
0 .0% 0 .0%
Agoraphobia 1 50.0
%
1 50.0
%
0 .0%
E) Essential relations:
1- Relation between Internet Addiction and personal
characteristics See table 16
In our research we found no significant difference
between male and female regarding results of IAT. But
there was significant relation between faculties and internet
addiction. 62% from students of literature faculty were
moderately addicted in comparison to 36% in faculty of
medicine. Theoretical faculties showed higher mean scores
of IAT than practical faculties. from those who failed in
the first semester 66% were moderately internet addict and
only 2% from them was normal internet users. Also a
significant relation between Internet Addiction and
socioeconomic status existed.
Results
77
Table 16: Relation between Internet Addiction and
personal characteristics
Internet Addiction
Normal Mild Moderate Severe P Sig
N % N % N % N %
Sex
Male 20 7.5% 107 40.4% 130 49.1% 8 3.0% 0.495* NS
Female 34 10.3% 135 40.8% 148 44.7% 14 4.2%
Faculty
Commerce 10 9.4% 50 47.2% 40 37.7% 6 5.7% 0.001** HS
Law 5 5.0% 29 28.7% 63 62.4% 4 4.0%
Literature 0 .0% 35 35.0% 62 62.0% 3 3.0%
Medicine 9 9.0% 50 50.0% 36 36.0% 5 5.0%
Science 21 21.2% 30 30.3% 44 44.4% 4 4.0%
Engineering 9 10.0% 48 53.3% 33 36.7% 0 .0%
Grades
A 9 17.3% 24 46.2% 17 32.7% 2 3.8% 0.002** HS
B 14 10.1% 65 47.1% 54 39.1% 5 3.6%
C 23 10.6% 94 43.3% 95 43.8% 5 2.3%
D 7 4.9% 45 31.7% 81 57.0% 9 6.3%
F 1 2.1% 14 29.8% 31 66.0% 1 2.1%
SES
Low 4 12.9% 19 61.3% 8 25.8% 0 .0% 0.036* S
Middle 32 8.8% 134 36.9% 179 49.3% 18 5.0%
High 18 8.9% 89 44.1% 91 45.0% 4 2.0%
NS: not significant S: significant HS: highly significant
There was a highly significant Correlation between
IAT score and grades and sleep quality otherwise no
significant correlation was detected. Table 17
Results
78
IAT
0 20 40 60 80 100
Grades
5
4
3
2
1
Table 17: Correlation between IAT, SES, Grades & Sleep
quality
SES-scale Grades PSQI
IAT
R* .008 .213 .281
P .852 .0001 0.0001
Sig NS HS HS
Fig. 12: High significant correlation between IAT and Grades.
Results
79
IAT
0 20 40 60 80 100
Pittsburgh Sleep Quality Index
20
15
10
5
0
Fig. 13: High significant correlation between IAT and Sleep Quality.
Results
80
2- Relation between Internet Gaming Disorder (IGD)
and personal characteristics See table 18
A highly significant relation between faculties and
internet gaming was revealed. As we found that 63% of
engineering students was normal internet gamers and 66%
of literature students were risky internet gamers. No
significant variance between male and female regarding
results of IGD scale was found. Also nothing significant
regarding socioeconomic status and internet gaming.
Table 18: Relation between Internet Gaming Disorder
(IGD) and personal characteristics
Internet Gaming Disorder
Normal Risky Disordered P Sig
N % N % N %
Sex
Male 44 31.2% 71 50.4% 26 18.4%
0.167* NS
Female 33 42.9% 35 45.5% 9 11.7%
Faculty
Commerce 12 40.0% 10 33.3% 8 26.7%
0.001* HS
Law 7 17.9% 23 59.0% 9 23.1%
Literature 4 16.7% 16 66.7% 4 16.7%
Medicine 16 27.6% 32 55.2% 10 17.2%
Science 10 43.5% 10 43.5% 3 13.0%
Engineering 28 63.6% 15 34.1% 1 2.3%
Grades
A 10 45.5% 10 45.5% 2 9.1%
0.895** NS
B 24 39.3% 28 45.9% 9 14.8%
C 26 35.1% 35 47.3% 13 17.6%
D 13 27.7% 26 55.3% 8 17.0%
F 4 28.6% 7 50.0% 3 21.4%
SES
Low 4 66.7% 2 33.3% 0 .0%
Middle 44 34.9% 60 47.6% 22 17.5% 0.634** NS
High 29 33.7% 44 51.2% 13 15.1%
NS: not significant S: significant HS: highly significant
Results
81
IGDs
0 2 4 6 8
Grades
5
4
3
2
1
A High significant Correlation was detected between
IGDs score and grades and sleep quality among study
participants. Table 19
Table 19: Correlation between IGD score and SES-scale,
Grades, and PSQI
SES-scale Grades PSQI
IGDs
R* -.026 .175 .200
P 0.703 0.009 0.003
Sig NS HS HS
NS: not significant HS: highly significant
Fig. 14: Correlation between IGD and students‟ grades.
Results
82
IGDs
0 2 4 6 8
Pittsburgh Sleep Quality Index
20
15
10
5
0
Fig. 15: Correlation between IGD and Sleep Quality.
Results
83
3- Relation between Internet Addiction and Internet
Gaming Disorder Table 20
77% from severe internet addicts was suffering from
disordered pattern of internet gaming which means a high
significant relation between both behaviors.
Table 20: Relation between Internet Addiction and Internet
Gaming Disorder
Internet Addiction
Negative Mild Moderate Severe P Sig
N % N % N % N %
Internet
Gaming
Disorder
Normal 9 69.2% 38 42.7% 29 27.1% 1 11.1% 0.001* HS
Risky 4 30.8% 45 50.6% 56 52.3% 1 11.1%
Disordered
0 .0% 6 6.7% 22 20.6% 7 77.8%
HS: highly significant
Regarding scores of IGD scale and IAT, we realized
that those who got higher IAT score was categorized as
disordered internet gamers and vice versa those who had
higher scores regarding IGD-Scale was categorized as
severe internet addicts regarding IAT scores.
Results
84
0
10
20
30
40
50
60
70
No IGD Risky IGD Poitive IGD
Mean IAT score
IAT score
0
1
2
3
4
5
6
No Internet
Addiction
Mild Moderate Severe
Mean
IGDs score
Fig. 16: Relation between IA and IGD.
Fig. 17: Relation between IGD and IA.
Results
85
IAT
0 20 40 60 80 100
IGDs
8
6
4
2
0
Also a high significant correlation between IAT score and
IGD score was revealed.
Fig. 18: Correlation between IA and IGD.
Discussion
86
Discussion
Internet is the global network that provides us with
lots of information, communication facilities, and many
other benefits in work, education, and even leisure time.
However some individual may suffer from harms because
of their pattern of using the internet.
In 1996 at the American Psychological Association,
Dr. Young researched Internet users who exhibited clinical
signs of addiction as measured through an adapted version
of the DSM-IV criteria for pathological gambling (Young,
1996). She defined it as a clinical disorder that may affect
the individual‟s Internet use, controlling ability and thus
leading to personal, professional and social problems
(Young, 2004).
In 2014, Internet gaming disorder (IGD) was listed in
Section III, Conditions for Further Study of the 5th edition
of the Diagnostic and Statistical Manual of Mental
Disorders (DSM–5). It was defined as the persistent and
recurrent usage of the internet games that leads to clinically
significant impairment or distress (DSM-5, 2014).
This research is a cross sectional study in Ain Shams
University on 596 of the first year university students (age
Discussion
87
range 17-19yr), This sample was recruited fulfilling the
definition of late adolescents according to United Nations
Children‟s Fund (UNICEF) (UNICEF, 2011). It was found
that 91% from the selected sample suffer from Internet
Addiction symptoms with different grades (mild, moderate
and severe addiction) according to Internet Addiction Test
(Young, 1999). Such a high result agree with previous
researches that showed a higher risk of Internet Addiction
among adolescents (Cao et al., 2011) specially university
students because of lack of monitoring and presence of
more free time (Soule et al., 2003; Young & Rogers,
1998; Kandell, 1998).
Most of the students in this research, about 89% of
them, selected social media as the most visited sites. A
study in Tanta University reported that 93% of students
were using Face Book (Saied et al., 2016). The higher they
feel gratified while using social networks the more likely
they report Internet Addiction symptoms (Leung, 2014).
Nowadays, social networks sites e.g. Facebook can be
easily accessed by smart phones and free applications (WU
et al., 2013) which increase the risk of Internet Addiction.
As regards severe Internet Addiction, in the current
study, we found that 25% of pornographic sites users are
Discussion
88
severely addicted compared by 4% of social media users
and 0% of other sites‟ users who exhibited milder form of
Internet Addiction.
Internet Addiction Test (IAT) estimates the total
severity of IA. Also it may help us to further examine
pattern of symptoms and complaints. For instance,
Salience, neglect work, excessive use, anticipation, lack of
control, and neglecting social life, all are features that could
be revealed using IAT.
In current study, it was found that the most
prominent symptom among participants is lack of control
followed by anticipation then excessive use. And this
decrease in executive control is consistent with other
behavioral addictions, such as pathological gambling.
(Brand et al., 2014) According to IAT (Young, 1999),
High scores in lack of control-related exam items indicate
that respondents having troubles managing online time and
may spend lots of time on-line more than intended.
Anticipation-related exam items indicate that respondents
mostly thinking about next time being on-line and could be
compelled to use internet while they are offline. Excessive
use items indicate excessive online behavior and a
compulsive pattern of usage. Also high rates of those items
Discussion
89
indicate that respondent more vulnerable to be depressed or
angry if forced to be offline for extended time.
Another popular internet activity is internet games.
Only 36% from our sample were using on-line games and
48% from gamers (about 18% from the whole sample)
were at risk to develop Internet Gaming Disorder (IGD)
while 16% of gamers were disordered (about 6% of the
whole sample). In other studies we could notice different
prevalence rates this may be related to cross cultural
differences and the use of different criteria and tools for
diagnosis. In this study the Internet Gaming Disorder (IGD)
Scale (Lemmens et al., 2015) was used after getting
author‟s permission, translating and validating it.
Researchers show interest with massively
multiplayer online role-playing games (MMORPG).
MMORPG are games where one creates an avatar in a
virtual fantasy world and interacts with other online players
to complete missions and journeys (Taneli et al., 2015). In
this study there was no remarkable difference between
students using internet games as we found that nearly 47%
of the gamers were using MMORPG and 53% were using
other types of games as puzzle, casual and racing.
Discussion
90
Risk Factors affecting Internet behavior:
One of the aims of the current study was to test some
factors that may influence internet usage e.g.
socioeconomic status (SES), gender and type of faculty
(theoretical or practical). We randomly selected 3 practical
faculties (Medicine, Engineering and Science) and 3
theoretical faculties (Law, Literature and Commerce) and
found a high significant relation between faculties and
internet behavior. For example, in faculty of literature 66%
from participants were at risk to develop IGD in
comparison to 63% of engineering students were normal
gamers. Also regarding Internet addiction 62% of
participants from faculty of law were moderate internet
addicts but only 36% from faculty of medicine were
moderate addicts. It could be because of more free time
available to students of theoretical faculties that expose
them to higher risk of IA and IGD (Soule et al., 2003)
The socioeconomic status (SES) of students‟ parents
was categorized into high, middle, low and very low status.
60% of the sample was of middle social status. A
significant relation between SES and IA was revealed. As
we found that about 50% from participants of middle SES
was of moderate internet addiction and 45 % of high SES
Discussion
91
and only 25% of low SES were moderate addicts. No
significant relation was revealed between SES and IGD.
Some researchers reported that SES, especially parental
education, is inversely associated with adolescents’
addictive internet use as parents of higher education are
more protective and effectively supervise and guide their
children‟s internet use (Heo et al., 2014). Other study
showed that higher SES of parents is carrying a higher risk
of Internet Addiction because of easily accessible network
(Reda et al., 2012).
In our study, about 45% of participants were males
and 55% were females and no significant relation has been
found between both genders and either Internet Addiction
or IGD. Many studies considered male gender as a risk
factor for internet addiction because of high association
between them (Ak et al., 2013; Tsai et al., 2009; Young,
1998). However, other studies showed no specific
difference between both genders (Reda et al., 2012;
Griffiths, 1996) also young (1996) presented a case of
homemaker female who increased her usage of chatting,
searching for belonging and emotional support. Such a case
would change the schemata of the excessive internet user as
a young male theme. This discrepancy between findings of
Discussion
92
association between gender and Internet Addiction may be
related to factors affecting the selected sample e.g. cultural
factors and problem awareness (Kuss et al., 2014).
Negative consequences of disturbed Internet
behavior:
Misuse of the internet has lots of negative
consequences e.g. disturbed sleep pattern (Young, 1999),
decreased academic achievement (Shields & Kane, 2011),
sedentary life style with its side effects (Choi, 2007). In the
current study we have tried to test some of the negative
effects of misuse of internet.
We tested sleep quality of students using the
Pittsburgh Sleep Quality Index (PSQI). A high significant
relation between sleep quality and Internet Addiction (IA)
was revealed as we found that 72% from severe internet
addicts were suffering from poor sleep quality. Sleep
quality and IA are not only related to each other but also
correlated i.e. the more severe the internet addiction, the
lesser the sleep quality. Nearly the same results were
reported regarding relation between IGD and sleep quality.
As we found that 69% from disordered gamers and 67%
from risky gamers were suffering from poor sleep quality.
Discussion
93
Also a high significant correlation between IGD and sleep
quality was noticed. Such results agree with other studies
which reported that excessive internet use leads to irregular
sleep patterns and poor quality of sleep due to an irregular
bedtime schedule (Kamal & Mosallem, 2013;
Kim et al., 2010) Also this could lead to disturbed
circadian rhythm (Chen & Gau, 2016).
Disturbed sleep pattern, due to late night log-ins,
causes excessive fatigue often making academic or
occupational functioning impaired and may decrease one‟s
immune system, leaving the patient vulnerable to diseases
(Young, 1999). In current study, 97.3% reported losing
sleep, either always, often, frequently, occasionally or
rarely, due to engaging into online activities (25% from
them were “always” doing so). However, Some researchers
found that not only internet addiction may affect sleep but
also people who suffer from decreased ability to fall asleep
at night, would engage in internet usage more than others
keeping them at higher risk of internet addiction (Chen &
Gau, 2016).
Although the internet is one of the educational tools,
many researchers reported a relation between internet
excessive use and decreased academic achievement. For
Discussion
94
example, Young (1998) found that 58 % of those identified
as excessive users also received poor grades. Similarly,
Shields and Kane (2011) found that students‟ grades were
negatively associated with time spent online. In our study,
we found that the mean hours students spent online were
5.74 (±SD 3.82). Another study has shown a relationship
between problematic Internet use and poor motivation to
study, especially in self-generated motivational domains
(Reed & Reay, 2015).
Also we noticed that the grades of students
deteriorate while the severity of IA increases and 98% from
those who failed there exams were internet addicts. Also a
high significant correlation has been found between IGD
and students grades.
Psychiatric disorders and disturbed Internet
behavior:
We have used the Mini International
Neuropsychiatric Interview for children and adolescents
(MINI KID) to establish structured psychiatric interview
with participants.
A previous study reported that IA was related to
generalized anxiety, ADHD, and phobias (Reda et al.,
Discussion
95
2012) and this agree with the significant relation that we
have found between IA and Agoraphobia and ADHD.
Another study showed that IA related to different types of
distresses (Desouky & Ibrahem, 2015). This is in line with
the significant relation that was noticed, in our research,
between IA and Adjustment disorder. In our study, IGD has
showed a relation with social phobia (Social Anxiety
Disorder) this may be related to the idea that those patients
avoid social contact and may be using internet games and
online virtual worlds to avoid distress related to real social
contact. Also Post-Traumatic Stress Disorder was related to
IGD this may augment the concept of using internet games
to escape negative feelings. A study in South Korea
considered substance abuse as a risk of IA (Lee et al.,
2013). Other study reported a positive association of IA
with alcohol abuse (Ho et al., 2014). In current study, 90%
from those substance abusers, engaged in our study, are
moderate internet addicts and 66% from them are at risk to
develop IGD. Those results agree with classifying internet
misuse as an addiction because of the similarities found
between IA and substance addiction. For instance, lack of
control, craving and even structural brain changes including
prefrontal cortex (Brand et al., 2014).
Discussion
96
It is noteworthy, some of the participants during the
interview have showed depressive and anxiety symptoms
that were not severe enough to be diagnosed as disorders.
Limitations and Strengths
97
Limitations
1. Access limitation: not all of the students agreed to give
us their contacts to be further assessed using MINI-KID
in another interview and this led to lesser number of
participants in MINI-KID which may not be
generalized.
2. Culture barrier: some of the students felt ashamed to
admit that they use pornographic sites as the most
visited sites.
3. Self-reported data: We used self-reported scales to
assess internet behavior and sleep quality. To avoid
memory related bias we were asking about the current
pattern not about past experiences. More researches are
needed to compare and examine outcomes.
4. PSQI: Used to measure sleep quality and didn‟t allow
us to assess specific sleep disorders. However, it
allowed us to find significant relation between Internet
& Sleep problems.
Strengths
1- Appropriate number of participants.
2- No much data about Internet Gaming Disorder (IGD) in
Egypt so we tried to highlight this area to be further
researched.
3- Translation and validation of IGD scale to be used in
other researches.
Conclusion
98
Conclusion
Internet, the global network, is a double-edged
sword. It provides us with lots of information,
communication facilities, and many other benefits in work,
education, and even leisure time. However Internet misuse
may disrupt different life aspects in the form of family
problems, education or academic deterioration, even
developmental and physical state may be affected because
of the sedentary life and poor care of self.
The current study has revealed a high prevalence rate
of Internet Addiction (IA) among first year university
students. This prevalence was higher than Internet Gaming
Disorder (IGD) prevalence. Both of IA and IGD are
inversely correlated to sleep quality. Theoretical faculties
are at higher risk for IA and IGD. Middle Socioeconomic
status (SES) of adolescents‟ parents is related to IA but not
related to IGD. Social phobia, Agoraphobia, PTSD,
Substance abuse, ADHD could be related to internet
pathological usage.
Recommendations
99
Recommendations
Research Recommendations:
1- Larger number of participants needed to examine
associated psychiatric disorders. We would like to
advice to set the psychiatric interview at the same time
of taking internet problems scales to avoid missing any
of the participants.
2- Further researches about internet usage and associated
risk factors of its pathological use in our Arabic
countries are needed to measure the extent of the
problem.
3- As the Internet Gaming Disorder was chosen to be
listed in DSM-5 and proposed criteria were mentioned,
Arabic researchers should focus on and measure the
extent of it.
Clinical Recommendations:
1- Centers for treatment of Internet Addiction in our
countries need to be established to help pathological
users.
2- Preventing measures and raising awareness of high
vulnerable groups e.g. adolescents and university
students should be considered.
3- Parental education about how to use technology to be
able to guide and supervise their children.
Summary
100
Summary
Introduction:
Today with more than 40 million internet users in
Egypt (ITU, 2016) and more than 80% of Internet Café
clients in Egypt were young people (UNDP & INP, 2010)
the internet has become an integral part of our society.
As highlighted by ÇARDAK ,Internet delivers some
practical tools like entertainment, shopping, social sharing
applications which enable accessing knowledge easier and
faster (Young, 1998) together with physical and
psychological harms like tiredness (Akın & Iskender,
2011), hostility, depression (Yen et al., 2007), loneliness
(Morahan-Martin & Schumacher, 2000), some
educational harms like wasting of time (Griffiths, 2000),
decrease in academic performance (Aboujaoude, 2010),
communication problems with peers (Gross et al., 2002;
Morahan-Martin & Schumacher, 2000).
Internet Addiction is a global phenomenon that has
been a topic of increasing interest to clinicians, researchers
and stakeholders such as teachers, parents and community
groups.
Summary
101
It‟s also called problematic Internet use (PIU)
(Moreno et al., 2013), compulsive Internet use (CIU)
(Rosen et al., 2012).
Five general subtypes of Internet addiction were
categorized based upon the most problematic types of
online applications, and they include addictions to
Cybersex, Cyber-relationships, online stock trading or
gambling, information surfing, and computer games
(Young, 1999).
In identifying the Internet addiction the most
frequently used definitions are as follows: Excessive use of
the Internet, uncontrolled and destructive Internet use
(Morahan-Martin & Schumacher, 2000); Excessive
Internet use that causes problems in family, business,
school, social and psychological life of the individuals
(Beard & Wolf, 2001); a new and unidentified clinical
disorder that may affect the individual‟s Internet use,
controlling ability and thus leading to personal,
professional and social problems (Young, 2007).
Recently, Internet gaming disorder (IGD) listed in
Section III, Conditions for Further Study of the 5th edition
of the Diagnostic and Statistical Manual of Mental
Summary
102
Disorders (DSM–5). As mentioned in DSM-5, IGD is
persistent and recurrent use of the internet to engage in
games, often with players, leading to clinically significant
impairment or distress in a 12-month period as indicated by
five (or more) of the proposed criteria: 1)Preoccupation,
2)Withdrawal symptoms, 3)Tolerance, 4)Unsuccessful
attempts to control, 5)loss of interest in previous hobbies
and entertainment, 6)Continued excessive use despite
knowledge of psychosocial problems, 7)Deceiving
regarding the amount of internet gaming, 8)use internet
games to escape or relieve a negative mood, 9)jeopardized
or lost a significant relationship, job, or educational or
career opportunity because of participation in internet
games (DSM-5, 2014).
Hypothesis of the Study:
The prevalence of Internet addiction among the
selected population is higher than Internet gaming disorder
and both are related to sleep disorders.
Aims of the Study:
1- Comparing the Prevalence of internet addiction &
internet gaming disorder among the selected
population.
Summary
103
2- Assessing essential risk factors (Sex, Socio-economic
class, Role of theoretical & practical faculties) of both
internet addiction and internet gaming disorder.
3- Exploring the associated sleep disorders among the
pathological users.
4- Measuring its impact on the academic achievement.
Design:
Comparative observational Cross sectional study
have been applied during the academic year 2016-2017 in
Ain Shams University.
Participants:
Data were collected from 596 students of the first
year of Ain Shams University. from randomly selected 6
different faculties (3 theoretical & 3 practical faculties).
According to the Unicef: the Adolescence period is divided
into two stages: early adolescence (10–14 years) and late
adolescence (15–19 years). (Unicef, 2011).
*Inclusion Criteria:
- Being a student of first year in Ain Shams University.
- Age range: 17 – 19 years old.
- Both sexes.
Summary
104
*Exclusion Criteria:
- Being a known psychiatric patient or receiving psychiatric
medications.
- Exceeding the Age limits.
Tools:
1-Informative designed questionnaire.
2-Socio-Economic Status (SES) Scale: (El-Gilany et al.,
2012)
3-Young Internet Addiction test (IAT): (Young, 1999)
We used the Arabic version which is the translated &
validated form of the original test. (Rabie M. et al., 2012)
4-Internet Gaming Disorder (IGD) Scale: (Lemmens et
al., 2015) We used the Arabic version that we adapted.
5-The Pittsburgh Sleep Quality Index (PSQI): (Buysse
et al., 1989)
6-The Mini International Neuropsychiatric Interview
for children and adolescents (MINI KID): (Sheehan et
al., 1998)
Procedure:
1- Adaptation of the internet gaming disorder scale
through its translation and validation.
Summary
105
2- Sampling:
Sampling was conducted in March and April 2017.
Printed copies of the previously mentioned scales were
distributed among Ain Shams University first year students
after obtaining ethical approvals and necessary university
authorities‟ permission. We randomly selected 3 of
practical faculties and 3 theoretical. The purpose of the test
was fully explained to all participants, who gave informed
consent to take part in the study. Then a randomly selected
sample of the pathological users and non-pathological users
has been subjected to the MINI-Kid to explore associated
psychiatric morbidity.
3- Data analysis:
An appropriate statistical analysis was conducted.
Results:
1- Validation of the internet gaming disorder scale
Arabic version: Questionnaire reliability and validity
showed appropriate results.
2- Results of the search scales: During this study, 91% of
the participants were diagnosed with Internet addiction,
but to varying degrees between mild, moderate and
excessive addiction. Most chose social media as the
Summary
106
most visited sites and 25% of the participants who
chose porn sites as the most visited were suffering from
severe internet addiction.
For Internet games, about one-third of the
participants were using it, and about half of the users were
at risk to develop Internet Gaming Disorder.
During our research we tried to study some of the
factors that may affect the use of the Internet. For example,
we studied the social levels of parents of students
participating in the research and were divided into high,
medium, low and very low. We noticed a link between the
social level and the Internet addiction and did not notice a
link between it and Internet Gaming Disorder (IGD).
We also found that theoretical faculties‟ students
have a higher score regarding Internet addiction test and
IGD scale, which indicates that they are more vulnerable to
internet misuse problems.
During our research, we did not notice a gender
difference in their Internet testing results.
Most students have been found to be suffering from
loss of control, Anticipation and excessive use of the
internet. There is no doubt that the misuse of the Internet
Summary
107
affects the level of function of the students. For example,
we found in our study that the grades of students are getting
worse with the increasing severity of internet addiction and
IGD.
In our study we found that 72% of people with
excessive Internet addiction suffer from poor sleep quality.
We also noticed that the more they addicted to the Internet,
the lesser sleep quality.
Through the psychological interview of the students,
we found a link between the misuse of the Internet
(whether internet addiction or online games disorder) and
other psychiatric disorders such as: social phobia,
Agoraphobia, PTSD, SUD and ADHD. Also some
psychological symptoms such as anxiety and depression,
which may not be enough to be considered as Disorder, but
may be a factor affecting the use of the Internet.
*Limitations
1- Access limitation: not all of the students agreed to
give us their contacts to submit to MINI-KID.
2- Culture barrier: some of the students felt ashamed to
admit that they use pornographic sites as the most
visited sites.
Summary
108
3- Self-reported data: To avoid memory related bias we
were asking about the current pattern not about past
experiences. More researches are needed to compare
and examine outcomes.
4- Further measures are needed to diagnose specific
sleep disorders among students in addition to sleep
quality.
References
109
References
Aboujaoude E. Problematic Internet use: an overview.
World Psychiatry. 2010; 9(2): 85-90.
Achab S, Nicolier M, Mauny F, Monnin J, Trojak B,
Vandel P, Sechter D, Gorwood P, Haffen E.
Massively multiplayer online role-playing games:
comparing characteristics of addict vs non-addict online
recruited gamers in a French adult population. BMC
psychiatry. 2011 Aug 26;11(1):144.
Acharya R, Faust O, Kannathal N, Chua T,
Laxminarayan S. Non-linear analysis of EEG signals
at various sleep stages. Computer methods and
programs in biomedicine. 2005 Oct 31;80(1):37-45.
Ahn DH: Korean policy on treatment and rehabilitation
for adolescents‟ Internet addiction, in 2007
International Symposium on the Counseling and
Treatment of Youth Internet Addiction. Seoul, Korea,
National Youth Commission, 2007, p 49
Ak S, Koruklu N, Yilmaz Y. A study on Turkish
adolescent’s Internet use: Possible predictors of Internet
addiction. CyberPsychology, Behavior and Social
Networking. 2013;16(3):205-9.
References
110
Akın A, Iskender M. Internet Addiction and
Depression, Anxiety and Stress. International Online
Journal of Educational Sciences. 2011; 3(1), 138-148.
Anderson C, Shibuya A, Ihori N, Swing E, Bushman
B, Sakamoto A, Rothstein H, Saleem M. Violent
video game effects on aggression, empathy, and
prosocial behavior in Eastern and Western countries: a
meta-analytic review. Psychological. Bulletin.
2010:136(2): 151–173.
Asaad T. Psychology of Sleep, Behavioral Sciences
and Medical Psychology, 2013;12:83-89.
Assefa S, Diaz-Abad M, Wickwire E and Scharf S.
The Functions of Sleep, Neuroscience. 2015;2(3):155–
171.
Atwood JD, Schwartz L. Cyber-sex: The new affair
treatment considerations. Journal of Couple &
Relationship Therapy. 2002; 1(3): 37-56.
Axelrod J. Regulation of circadian rhythms of indole
amines in the pineal gland. In The Pineal Gland and Its
Endocrine Role. 1983 (pp. 1-13). Springer US.
Bakken I, Wenzel H, Gӧ testam K, Johansson A,
Øren A. Internet addiction among Norwegian adults: A
References
111
stratified probability sample study. Scandinavian
Journal of Psychology. 2009; 50(2):121-127.
Bartholow B, Sestir M, Davis E. Correlates and
Consequences of Exposure to Video Game Violence:
Hostile Personality, Empathy, and Aggressive
Behavior. Personality and Social Psychology Bulletin.
2005;31(11):1573-1586.
Bavelier D, Achtman R, Mani M, Föcker J. Neural
bases of selective attention in action video game
players. Vision Research. 2012;61:132-143.
Beard K, Wolf E. Modification in the Proposed
Diagnostic Criteria for Internet Addiction,
CyberPsychology & Behavior. 2004; 4(3):377-383.
Beard K. Internet addiction: a review of current
assessment techniques and potential assessment
questions. CyberPsychology & Behavior. 2005; 8(1):
7–14.
Beard KW, Wolf EM. Modification in the proposed
diagnostic criteria for Internet addiction.
CyberPsychology & Behavior. 2001 Jun 1;4(3):377-83.
References
112
Beato G. ”Internet Addiction: What Once Was Parody
May Soon Be Diagnosis.” Reason. Reason Foundation.
2010.
Bernert R, Turvey C, Conwell Y and Joiner T.
Association of Poor Subjective Sleep Quality with risk
for Death by Suicide During a 10-Year Period, JAMA
Psychiatry. 2014;71(10):1129-1137.
Beutel M, Braehler E, Glaesmer H, Kuss D,
Woelfling K and Mueller K. Regular and problematic
leisure-time Internet use in the community: Results
from a German population-based survey.
Cyberpsychology, Behavior and Social Networking,
2011; 14(5):291-6.
Billieux J, Deleuze J, Griffiths MD, Kuss DJ. Internet
gaming addiction: The case of massively multiplayer
online role-playing games. In: Textbook of addiction
Treatment: International perspectives 2015 (pp. 1515-
1525). Springer Milan.
Blum K, Gardner E, Oscar-Berman M and Gold M.
“Liking” and “wanting” linked to reward deficiency
syndrome (RDS): hypothesizing differential
References
113
responsivity in brain reward circuitry. Current
Pharmaceutical Design. 2012;18:113–118.
Borbély A, Daan S, Wirz-Justice A and Deboer T.
The two-process model of sleep regulation: a
reappraisal, journal of sleep regulation.
2016;25(2):131–143.
Bostwick J, Bucci J. Internet sex addiction treated with
naltrexone. Mayo Clinic Proceeding. 2008;83(2):226–
230.
Brand M, Young K and Laier C. Prefrontal Control
and Internet Addiction: A Theoretical Model and
Review of Neuropsychological and Neuroimaging
Findings, Frontiers in human neuroscience. 2014.
Bulck JV. Television viewing, computer game playing,
and internet use and self-reported time to bed and time
out of bed in secondary-school children, Sleep. 2004;
27: p.101–104.
Buxton O, Cain S, O’Connor S, Porter J, Duffy
J, Wang W, Czeisler C and Shea S. Metabolic
Consequences in Humans of Prolonged Sleep
Restriction Combined with Circadian Disruption,
Science Translational Medicine. 2012;11:4(129).
References
114
Buysse D, Reynolds C, Monk T, Berman S and
Kupfer D. The Pittsburgh sleep quality index: A new
instrument for psychiatric practice and
research. Psychiatry Research, 1989;28(2):193-213.
Buysse D. Sleep Health: Can We Define It? Does It
Matter? Sleep, 2014;37(1).
Cajochen C, Kra¨uchi K and Wirz-Justice A. Role of
Melatonin in the Regulation of Human Circadian
Rhythms and Sleep, Journal of Neuroendocrinology,
2003;15:432–437.
Camardese G, Leone B, Walstra C, Janiri L and
Guglielmo R. Pharmacological Treatment of Internet
Addiction, Internet Addiction Neuroscientific
Approaches and Therapeutical Interventions,
2015;10:151-166.
Cao H, Sun Y, Wan Y, Hao J and Tao F. Problematic
Internet use in Chinese adolescents and its relation to
psychosomatic symptoms and life satisfaction, BMC
Public Health. 2011; 11: 802.
Çardak M. Psychological well-being and internet
addiction among university students. The Turkish
Online Journal of Educational Technology, 2013;12(3).
References
115
Cash H, Rae C, Steel A and Winkler A. Internet
Addiction: A Brief Summary of Research and Practice.
Current Psychiatry, 2012; 8(4):292–8.
Chen Y and Gau S. Sleep problems and internet
addiction among children and adolescents: a
longitudinal study, journal of sleep research.
2016;25(4):458-465.
Cheng C, Li AY. Internet addiction prevalence and
quality of (real) life: a meta-analysis of 31 nations
across seven world regions. Cyberpsychology,
Behavior, and Social Networking. 2014 Dec
1;17(12):755-60.
Cho Y and Lee H. A study on a model for Internet
addiction of adolescents. Journal of Korean
Academy of Nursing, 2004; 34(1): 102-110.
Choi YH: Advancement of IT and seriousness of youth
Internet addiction, in 2007 International Symposium on
the Counseling and Treatment of Youth Internet
Addiction. Seoul, Korea, National Youth Commission,
2007, p 20
Choo H, Gentile D, Sim T, Li D, Khoo A and Liau
A. Pathological video-gaming among singaporean
References
116
youth, Annals of the Academy of Medicine Singapore,
2010;39(11):822– 829.
Correa D, Aten S, Garcia A, Obrietan K and Hoyt
K. Light-induced Changes in the Suprachiasmatic
Nucleus Transcriptome Regulated by the MAPK/ERK
Pathway, The FASEB Journal. 2017;31(1)813.5.
Cuhadar C. Exploration of problematic Internet use
and social interaction anxiety among Turkish preservice
teachers. Computers & Education, 2012;
59(2):173-81.
Cunnington D, Junge MF, Fernando AT. Insomnia:
prevalence, consequences and effective treatment. Med
J Aust. 2013 Oct 21;199(8):S36-40.
Czincz J and Hechanova R. Internet addiction:
Debating the diagnosis. Journal of Technology in
Human Services. 2009; 27(4):257–72
Dang-Vu T, Schabus M, Desseilles M, Sterpenich V,
Bonjean M and Maquet P. Functional neuroimaging
insights into the physiology of human sleep, Sleep.
2010;33(12).
References
117
Davis R. A cognitive-behavioral model of pathological
Internet use. Computers in Human Behavior. 2001;
17:187–195
Desouky D. and Ibrahem R. Internet Addiction and
Psychological Morbidity among Menoufia University
Students, Egypt, American Journal of Public Health
Research. 2015;3(5):192-198
Diagnostic and statistical manual of mental disorders.
5th ed. (DSM-5) Washington: American Psychiatric
Publishing; 2014.
Diekelmann S and Born J. The memory function of
sleep, Nature Reviews Neuroscience. 2010; 11: 114-
126.
Dweck CS and Molden DC. Self-theories: Their
impact on competence motivation and acquisition. In
Elliot AJ & Dweck CS, ed. by. Handbook of
competence and motivation. 2005. p.122–140.
El-Gilany A, El-Wehady A and El-Wasify M.
Updating and validation of the socioeconomic status
scale for health research in Egypt. Eastern
Mediterranean Health Journal. 2012; 18(9).
References
118
Entertainment Software Association. Essential facts
about the computer and video game industry. 2012.
Retrieved from: www.theesa.com/facts/pdfs/
ESA_EF_2012. pdf
Estrada E, Nazeran H, Nava P, Behbehani K, Burk
J, Lucas E. EEG feature extraction for classification of
sleep stages. In Engineering in Medicine and Biology
Society, 2004. IEMBS’04. 26th Annual International
Conference of the IEEE 2004 Sep 1 (Vol. 1, pp. 196-
199). IEEE.
French IT and Muthusamy KA. A Review of Sleep
and Its Disorders in Patients with Parkinson’s Disease
in Relation to Various Brain Structures, Frontiers in
Aging Neuroscience. 2016; 8:114.
Garcia-Rill E, Kezunovic N, Hyde J, Simon C, Beck
P and Urbano FJ. Coherence and frequency in the
reticular activating system (RAS), Sleep
medicine reviews. 2013; 17(3): 227–238.
Gentile DA and Gentile JR. Violent video games as
exemplary teachers: A conceptual analysis. Journal of
Youth and Adolescence, 2008; 9: 127–141.
References
119
Genzel L, Spoormaker VI, Konrad BN and Dresler
M. The role of rapid eye movement sleep for amygdalarelated
memory processing, Neurobiology of Learning
and Memory, 2015; 122: 110–121.
Gibson E, Powles A, Thabane L, O’Brien S, Molnar
D, Trajanovic N, Ogilvie R, Shapiro C, Yan M and
Chilcott-Tanser L. ”Sleepiness” is serious in
adolescence: Two surveys of 3235 Canadian
students. BMC Public Health. 2006; 6(1).
Goddard AW, Shekhar A, Whiteman AF and
McDougle CJ. Serotoninergic mechanisms in the
treatment of obsessive-compulsive disorder. Drug
Discov Today, 2008; 13: 325–332
Goodman A. Diagnosis and treatment of sexual
addiction, Journal of Sex & Marital Therapy, 1993;
19(3): 225-251.
Granic I, Lobel A and Engels RC. The benefits of
playing video games, American Psychologist, 2014;
69(1): 66-78
Grant JE, Brewer JA, Potenza MN. The
neurobiology of substance and behavioral addictions.
CNS spectrums. 2006 Dec;11(12):924-30.
References
120
Greydanus DE and Greydanus MM. Internet use,
misuse, and addiction in adolescents: current issues and
challenges. International Journal of Adolescent
Medicine and Health, 2012; 24(4): 283-89.
Griffiths MD and Pontes HM. Internet Addiction
Disorder and Internet Gaming Disorder are Not the
Same, Addiction Research & Therapy, 2014, 5(4).
Grıffıths MD. Does Internet and computer ”addiction”
exist? Some case study evidence. CyberPsychology and
Behavior, 2000; 3(2): 211-218.
Griffiths MD. Internet addiction: An issue for clinical
psychology, Clinical Psychology Forum. 1996; 97: 32-
6.
Gross EF, Juvonen J and Gable SL. Internet use and
well-being in adolescence. Journal of Social Issues.
2002; 58: p.75–90.
Ha JH, Yoo HJ, Cho IH, Chin B, Shin D, Kim JH.
Psychiatric comorbidity assessed in Korean children
and adolescents who screen positive for Internet
addiction. Journal of Clinical Psychiatry2006 May;
67(5): 821-6.
References
121
Hardie E, Tee MY. Excessive Internet Use: The Role
of Personality, Loneliness and Social Support Networks
in Internet Addiction, Australian Journal of Emerging
Technologies & Society. 2007; 5(1): p34-47.
Hasler BP, Dahl RE, Holm SM, Jakubcak JL, Ryan
ND, Silk JS, Phillips ML and Forbes EE. Weekendweekday
advances in sleep timing are associated with
altered reward-related brain function in healthy
adolescents, Biological Psychology. 2012; 91(3): 334-
41.
Hastings MH, Maywood ES and Reddy AB. Two
decades of circadian time, journal of
Neuroendocrinology, 2008; 20: 812–819.
Heo J, Oh J, Subramanian S, Kim Y and Kawachi I.
Addictive Internet Use among Korean Adolescents: A
National Survey. PLoS ONE, 2014; 9(2), p.e87819.
Heo JY, Kim SH, Han MA, Ahn YJ. Correlation
between smartphone addiction and quality of sleep
among university school students, graduate students.
The Journal of the Korea institute of electronic
communication sciences. 2015;10(6):737-48.
References
122
Hirshkowitz M, Whiton K, Albert SM, Alessi C,
Bruni O, DonCarlos L, Hazen N, Herman J, Katz
ES, Kheirandish-Gozal L, Neubauer DN, O’Donnell
AE, Ohayon M, Peever J, Rawding R, Sachdeva
RC, Setters B, Vitiello MV, Ware JC and Hillard
PJ. National Sleep Foundation‟s sleep time duration
recommendations: methodology and results summary,
Sleep Health, 2015; 1(1): 40-43.
Ho RC, Zhang MWB,Tsang TY, Toh AH, Pan F, Lu
Y, Cheng C,Yip PS, Lam LT, Lai CM,Watanabe H
and Mak KK. The association between internet
addiction and psychiatric co-morbidity: a meta-analysis,
BMC Psychiatry. 2014; 14(183).
Huang RL, Lu Z, Liu JJ, You YM, Pan ZQ, Wei Z,
He Q and Wang ZZ. Features and predictors of
problematic internet use in Chinese college students.
Behaviour & Information Technology, 2009; 28(5):
485-90
ITU. Committed to connecting the world [internet].
ITU. 2016. Available at: http://www.itu.int.
Kamal NN and Mosallem FA. Determinants of
Problematic Internet Use among El-Minia High School
References
123
Students in Egypt, International Journal of
Preventive Medicine, 2013; 4(12), pp.1429–1437.
Kanda T, Tsujino N, Kuramoto E, Koyama
Y, Susaki EA, Chikahisa S and Funato H. Sleep as a
biological problem: an overview of frontiers in sleep
research, The Journal of Physiological Sciences, 2016;
66: 1–13.
Kandell JJ. Internet addiction on campus: The
vulnerability of college students. Cyberpsychology &
behavior. 1998; 1(1):11-7.
Kim K, Ryu E, Chon MY, Yeun EJ, Choi SY, Seo
JS, Nam BW. Internet addiction in Korean adolescents
and its relation to depression and suicidal ideation: A
questionnaire survey. International Journal of Nursing
Studies2006 Feb; 43(2): 185-92.
Kim Y, Park JY, Kim SB, Jung IK, Lim
YS and Kim JH. The effects of Internet addiction on
the lifestyle and dietary behavior of Korean
adolescents. Nutrition Research and Practice journal.
2010; 4: 51–57.
King DL and Delfabbro PH. Internet Gaming
Disorder Treatment: A Reviewof Definitions of
References
124
Diagnosisand Treatment Outcome, Journal of Clinical
Psychology, 2014; 70(10): 942–955.
King DL, Delfabbro PH, Wu AMS, Doh YY, Kuss
DJ, Pallesen S, Mentzoni R, Carragher N and
Sakuma H. Treatment of Internet gaming disorder: An
international systematic review and CONSORT
evaluation, Clinical Psychology Review. 2017; 54: P.
123–133.
Ko C, Yen j, Chen S, Yang M, Lin H and Yen C.
Proposed diagnostic criteria and the screening and
diagnosing tool of Internet addiction in college
students, Comprehensive Psychiatry. 2009; 50: 378–
384.
Ko CH, Yen JY, Yen CF, Lin HC, Yang MJ. Factors
predictive for incidence and remission of internet
addiction in young adolescents: a prospective study.
CyberPsychology & Behavior. 2007 Aug 1;10(4):545-
51.
Koh YS: Development and application of K-Scale as
diagnostic scale for Korean Internet addiction, in 2007
International Symposium on the Counseling and
References
125
Treatment of Youth Internet Addiction. Seoul, Korea,
National Youth Commission, 2007, p 294
Koo HJ and Kwon JH. Risk and Protective Factors
of Internet Addiction: A Meta-Analysis of Empirical
Studies in Korea. Yonsei Medical Journal. 2014; 55(6):
1691–1711.
Kratzer S and Hegerl U. Is ”Internet Addiction” a
disorder of its own? A study on subjects with excessive
internet use. Psychiatrische Praxis journal. 2008; 35(2):
80–3.
Krishnan L, Kang A, Sperling G and Srinivasan R
Neural strategies for selective attention distinguish fastaction
video game players. Brain Topography. 2013;
26(1):83-97.
Kuss D, Griffiths MD, Karila L and Billieux J.
Internet Addiction: A Systematic Review of
Epidemiological Research for the Last Decade, Current
Pharmaceutical Design, 2014; 20(25): 4026-4052.
Kuss DJ, Griffiths MD, Binder JF. Internet addiction
in students: Prevalence and risk factors. Computers in
Human Behavior. 2013 May 31; 29(3):959-66.
References
126
Lam LT. Internet Gaming Addiction, Problematic Use
of the Internet, and Sleep Problems: A Systematic
Review, Current Psychiatry Reports. 2014; 16: p.444.
Lee YS, Han DH, Kim SM and Renshaw PF.
Substance abuse precedes internet addiction, Addictive
Behaviors. 2013; 38(4): 2022–2025.
Lee YS, Han DH, Yang KC, Daniels MA, Na C, Kee
BS and Renshaw PF. Depression like characteristics
of 5HTTLPR polymorphism and temperament in
excessive internet users. Journal of Affective Disorders,
2008; 109: 165–169.
Lemmens JS, Valkenburg PM and Gentile DA. The
Internet Gaming Disorder Scale, APA PsycNET,
Psychological Assessment. 2015; 27(2), pp.567-582.
Lenhart A, Kahne J, Middaugh E, Macgill AR,
Evans C and Vitak J. Teens, video games, and civics:
Teens‟ gaming experiences are diverse and include
significant social interaction and civic engagement.
ERIC, 2008; p.:78
Lesieur HR and Blume SB. Pathological Gambling,
Eating Disorders, and the Psychoactive Substance Use
References
127
Disorders, Journal of Addictive Diseases, 1993; 12(3):
89-102.
Leung L. Predicting Internet risks: a longitudinal panel
study of gratifications-sought, Internet addiction
symptoms, and social media use among children and
adolescents, Health Psychology and Behavioral
Medicine, 2014; 2(1).
Levi F, Canon C, Dipalma M, Florentin I, Misset
JL. When should the immune clock be reset? From
circadian pharmacodynamics to temporally optimized
drug delivery. Annals of the New York Academy of
Sciences. 1991; 618: 312–329.
Lin M-P, Ko H-C and Wu JY-W. Prevalence and
psychosocial risk factors associated with Internet
addiction in a nationally representative sample of
college students in Taiwan. Cyberpsychology, Behavior
and Social Networking, 2011; 14(12): 741-6.
Maremmani I, Pacini M, Lamanna F, Pani
PP, Perugi G, Deltito J, Salloum IM and Akiskal H.
Mood stabilizers in the treatment of substance use
disorders. CNS Spectrums. 2010; 15(2): 95-109.
References
128
Masa JF, Rubio M and Findley LJ. Habitually sleepy
drivers have a high frequency of automobile crashes
associated with respiratory disorders during sleep.
American Journal of Respiratory and Critical Care
Medicine., 2000; 162: pp. 1407–1412.
McKnight-Eily LR, Eaton DK, Lowry R, Croft JB,
Presley-Cantrell L and Perry GS. Relationships
between hours of sleep and health-risk behaviors in US
adolescent students, Preventive Medicine. 2011; 53(4-
5): 271-3.
Meerkerk G, Van Den Eijnden R, Vermulst A and
Garretsen H. The Compulsive Internet Use Scale
(CIUS): Some Psychometric Properties. Cyber
Psychology & Behavior, 2009; 12(1): 1-6.
Meerlo P, Mistlberger RE, Jacobs BL, Heller HC
and McGinty D. New neurons in the adult brain: the
role of sleep and consequences of sleep loss, Sleep
Medicine Reviews. 2009; 13(3): 187-94.
Melamed S and Oksenberg A. Excessive daytime
sleepiness and risk of occupational injuries in non-shift
daytime workers. Sleep, 2002; 25: p. 315–322.
References
129
Mobilia P. Gambling as a rational addiction, Journal
of Gambling Studies, 1993; Vol. 9(2): 121–151
Montag C, Reuter M. Internet Addiction:
Neuroscientific Approaches and Therapeutical
Interventions, Springer. 2015
Monti JM and Jantos H. The roles of dopamine and
serotonin, and of their receptors, in regulating sleep and
waking, Progress in Brain Research, 2008; 172: p.625-
646.
Morahan-Martin J and Schumacher P. Incidence
and correlates of pathological Internet use among
college students. Computers in Human Behavior. 2000;
16: p.13–29.
Moreno MA, Jelenchick LA and christakis DA.
“Problematic internet use among older adolescents: A
conceptual framework”. Computers and Human
Behavior. 2013; 29: p.1879–1887.
Morrison CM and Gore H. The relationship between
excessive Internet use and depression: A questionnairebased
study of 1,319 young people and adults.
Psychopathology, 2010; 43(2): 121-6.
References
130
Mosher D. High Wired: Does Addictive Internet Use
Restructure the Brain?, Scientific Americans, 2011.
Mu¨ller KW, Janikian M, Dreier M, Wo¨lfling K,
Beutel ME, Tzavara C, Richardson C and Tsitsika
A. Regular gaming behavior and internet gaming
disorder in European adolescents: results from a crossnational
representative survey of prevalence, predictors,
and psychopathological correlates, European Child
Adolescent Psychiatry, 2015; 24: p. 565–574.
Ni X, Yan H, Chen S and Liu Z. Factors influencing
Internet addiction in a sample of freshmen university
students in China. Cyberpsychology & Behavior, 2009;
12(3): 327-30.
Oh W. The Influence of Internet Expectation and Selfefficacy
on Internet Addiction among High School
Students, Journal of Korean Public Health Nursing.
2005; 19(2): 339-348.
Palaus M, Marron E, Viejo-Sobera R and Redolar-
Ripoll D. Neural Basis of Video Gaming: A Systematic
Review, Frontiers in Human Neuroscience, 2017; 11: p.
248.
References
131
Petry N, Rehbein F, Gentile D, Lemmens J, Rumpf
H, Mößle T, Bischof G, Tao R, Fung D, Borges G,
Auriacombe M, González Ibáñez A, Tam P and
O’Brien C. An international consensus for assessing
internet gaming disorder using the new DSM-5
approach. Addiction, 2014; 109(9): 1399-1406.
Pontes H, Kuss D and Griffiths M. Clinical
psychology of internet addiction: a review of its
conceptualization, prevalence, neuronal processes, and
implications for treatment, Neuroscience and
Neuroeconomics, 2015; 4: p. 11-23.
Porter G, Starcevic V, Berle D and Fenech P.
Recognizing problem video game use. Australian and
New Zealand Journal of Psychiatry. 2010; 44, p. 120–
128.
Powell NB, Schechtman KB, Riley RW, Li K and
Guilleminault C. Sleepy driving: Accidents and injury.
Otolaryngology, head and neck surgery. 2002; 126: p.
217–227.
Przybylski AK, Weinstein N, Murayama K. Internet
gaming disorder: investigating the clinical relevance of
References
132
a new phenomenon. American Journal of Psychiatry.
2016 Nov 4;174(3):230-6.
Reda M, Rabie M, Mohsen N and Hassan A.
Problematic Internet Users and Psychiatric Morbidity in
a Sample of Egyptian Adolescents. SciRes, 2012; 3(8):
p. 626-631.
Reed P and Reay E. Relationship between levels of
problematic Internet usage and motivation to study in
university students. Higher Education. 2015; p.1–13.
Rehbein F, Kliem S, Baier D, Mößle T and Petry N.
Prevalence of internet gaming disorder in German
adolescents: diagnostic contribution of the nine DSM-5
criteria in a state-wide representative sample.
Addiction, 2015; 110(5): 842-851.
Rosen LD, Lim AF, Felt J, Carrier LM, Cheever
NA, Lara-Ruiz JM, Mendoza JS, Rokkum J. Media
and technology use predicts ill-being among children,
preteens and teenagers independent of the negative
health impacts of exercise and eating habits. Computers
in human behavior. 2014 Jun 30;35:364-75.
References
133
Rosen LD. Idisorder: Understanding Our Obsession
with Technology and Overcoming Its Hold on Us, New
York: St. Martin’s Press. 2012.
Russoniello CV, O’Brien K, Parks JM. EEG, HRV
and Psychological Correlates while Playing Bejeweled
II: A Randomized Controlled Study. Annual review of
cybertherapy and telemedicine. 2009 Jun 11;144:189-
92.
Sadock B, Ruiz P, Sadock V. Kaplan et Sadock’s
synopsis of psychiatry. 11th ed. Philadelphia [u.a.]:
Wolters Kluwer; 2015.
Saied SM, Elsabagh HM and El-Afandy AM.
Internet and facebook addiction among Egyptian and
Malaysian medical students: a comparative study, Tanta
University, Egypt, International Journal of community
medicine and public health, 2016; 3(5).
Saper CB, Scammell TE and Lu J. Hypothalamic
regulation of sleep and circadian rhythms, Nature.
2005; 437: p.1257-1263.
Sasmaz T, Oner S, Kurt A, Yapıcı G, Yazıc A,
Bugdaycı R and Sis M. Prevalence and risk factors of
References
134
Internet addiction in high school students, European
Journal of Public Health, 2013; 24(1): 15–20.
Sateia MJ. International Classifi cation of Sleep
Disorders-Th ird Edition Highlights and Modifi cations,
CHEST, 2014; 146(5): 1387 – 1394
Satghare P, Abdin E, Vaingankar JA, Chua BY,
Pang S, Picco L, Poon LY, Chong SA and
Subramaniam M. Prevalence of sleep problems
among those with internet gaming disorder in
Singapore, ASEAN Journal of Psychiatry, 2016; 17(2).
Schwartz JR and Roth T. Neurophysiology of Sleep
and Wakefulness: Basic Science and Clinical
Implications. Current Neuro-pharmacology, 2017;
6 (4): 367–378.
Sexton CE, Storsve AB, Walhovd KB, Johansen-
Berg H. and Fjell AM. Poor sleep quality is associated
with increased cortical atrophy in community-dwelling
adults, Neurology, 2014; 83(11): 967–973.
Shaffer HJ, Hall MN and Vander BJ. Computer
addiction”: a critical consideration. American Journal
of Orthopsychiatry, 2000; 70:p. 162-8.
References
135
Sheehan D, Lecrubier Y, Harnett-Sheehan K,
Amorim PA, Janavs J, Weiller EHT, Hergueta T,
Baker RDG and Dunbar GCL. The Mini-
International Neuropsychiatric Interview (M.I.N.I.):
The development and validation of a structured
diagnostic psychiatric interview for DSM-IV and ICD-
10. The Journal of clinical psychiatry. 1998; 59(20):
p.22-33; quiz 34.
Shields N, Kane J. Social and psychological correlates
of Internet use among college students.
Cyberpsychology: Journal of Psychosocial Research on
Cyberspace. 2011 Jul 1; 5(1).
Smyth JM. Beyond self-selection in video game play:
An experimental examination of the consequences of
massively multiplayer online role-playing game
play. Cyberpsychology and Behavior journal, 2007;
10: p.717–721.
Soule LC, Shell LW and Kleen BA. Exploring
Internet addiction: Demographic characteristics and
stereotypes of heavy Internet users. Journal of
Computer Information Systems. 2003; 44(1): 64-73.
References
136
Spada M. An overview of problematic Internet use, Elsevier,
Addictive Behaviors, 2014; 39(1): 3-6.
Steriade M. The corticothalamic system in sleep,
Frontiers in Bioscience. 2003; 1: p.8.
St-Onge M, Roberts AL, Chen J, Kelleman M,
O’Keeffe M, RoyChoudhury A and Jones P. Short
sleep duration increases energy intakes but does not
change energy expenditure in normal-weight
individuals, The American Journal of clinical Nutrition,
2011; 94(2): 410-416.
Taneli T, Guo YH and Mushtaq S. Internet Gaming
Disorder, Behavioral addiction book, American
Psychiatric Association. 2015, Ch 6
Tanji J and Hoshi E. Role of the lateral prefrontal
cortex in executive behavioral control. Physiological
Reviews, 2008; 88(1): 37–57.
Telzer EH, Fuligni AJ, Lieberman MD and Galván
A. The effects of poor quality sleep on brain function
and risk taking in adolescence, Neuroimage, 2013; 71:
10.1016.
Tsai HF, Cheng SH, Yeh TL, Shih CC, Chen KC
and Yang YC. The risk factors of internet addiction -
References
137
A survey of university freshmen. Psychiatry Research,
2009; 167(3): 294-9.
Uddin M, Mamun A, Iqbal M, Nasrullah M,
Asaduzzaman M, Sarwar M and Amran M. Internet
Addiction Disorder and Its Pathogenicity to
Psychological Distress and Depression among
University Students: A Cross-Sectional Pilot Study in
Bangladesh. Psychology, 2016; 07(08): 1126-1137.
United Nations Children‟s Fund (UNICEF). THE
STATE OF THE WORLD‟S CHILDREN 2011:
Adolescence An Age of Opportunity [Internet]. New
York; 2011. Available from: https://www.unicef.org/
adolescence/files/SOWC_2011_Main_Report_EN_020
92011.pdf
United Nations Development Programme (UNDP) &
Institute of National Planning (INP). Egypt Human
Development Report. Youth in Egypt: Building our
Future, 2010. Available at: http://www.undp.org.eg/
Portals/english.pdf
Uttal DH, Meadow NG, Tipton E, Hand LL, Alden
AR, Warren C and Newcombe NS. The malleability
References
138
of spatial skills: A meta-analysis of training studies,
Psychological Bulletin. 2013; 139: p.352– 402.
Van-Doorn MD, Branje SJT and Meeus WHJ.
Developmental changes in conflict resolution styles in
parent-adolescent relationships: a four-wave
longitudinal study. Journal of Youth and Adolescence,
2011; 40(1): 97–107.
Ventura M, Shute V and Zhao W. The relationship
between video game use and a performance-based
measure of persistence, Computers & Education, 2013;
60: p.52–58.
Walker MP. A refined model of sleep and the time
course of memory formation. Behavioral Brain Science,
2005; 28: p.51–64.
Wallis D. ”Just Click No”, The New Yorker, 1997; P.
28.
Wang CW, Chan CL, Mak KK, Ho SY, Wong PW,
Ho RT. Prevalence and correlates of video and internet
gaming addiction among Hong Kong adolescents: a
pilot study. The Scientific World Journal. 2014 Jun
16;2014.
References
139
Weinstein A, Feder LC, Rosenberg KP, Dannon P.
Internet addiction disorder: Overview and
controversies. Behavioral addictions. 2014:99-117.
Winkler A, Dörsing B, Rief W, Shen Y and
Glombiewski JA. Treatment of Internet addiction: a
meta-analysis. Clinical Psychology Review. 2013;
33(2): 317–329.
Wu A, Cheung V, Ku L and Hung E. Psychological
risk factors of addiction to social networking sites
among Chinese smartphone users, Journal of
Behavioral Addictions. 2013; 2(3): 160–166.
Yates TM, Gregor MA and Haviland MG. Child
maltreatment, alexithymia, and problematic Internet use
in young adulthood. Cyberpsychology, Behavior and
Social Networking. 2012; 15(4): 219-25.
Yee N. The demographics, motivations, and derived
experiences of users of massively multi-user online
graphical environments. Presence: Teleoperators and
virtual environments. 2006 Jun 1;15(3):309-29.
Yen JY, Ko CH, Yen CF, Chen CS and Chen CC.
The association between harmful alcohol use and
Internet addiction among college students: Comparison
References
140
of personality. Psychiatry and Clinical Neurosciences.
2009; 63(2): 218-24.
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. Journal of
adolescent health. 2007 Jul 31;41(1):93-8.
Young KS, Pistner M, O’Mara J and Buchanan J.
Cyber disorders: The mental health concern for the new
millennium. CyberPsychology and Behavior. 1999; 2:
p.475–479.
Young KS, Rogers RC. The relationship between
depression and Internet addiction. CyberPsychology &
Behavior. 1998;1(1):25-8.
Young KS. Caught in the net: how to recognize the
signs of Internet addiction and a winning strategy for
recovery. New York: J. Wiley. 1998.
Young KS. CBT-IA: The First Treatment Model for
Internet Addiction, Journal of Cognitive Psychotherapy:
An International Quarterly. 2011; 25(4).
References
141
Young KS. Cognitive behavior therapy with Internet
addicts: treatment outcomes and implications.
CyberPsychology & Behavior. 2007 Oct 1;10(5):671-9.
Young KS. Internet Addiction, American Behavioral
Scientist, 2004; 48(4): 402 – 415.
Young KS. Internet addiction: symptoms, evaluation
and treatment. Innovations in clinical practice: A source
book. 1999; 17: p.19-31.
Young KS. Internet addiction: the emergence of a new
clinical disorder. CyberPsychology & Behavior. 1998;
1(3), pp.237–44
Young KS. Psychology of computer use: XL.
Addictive use of the Internet: a case that breaks the
stereotype. Psychological Reports, 1996;79: p.899–902.
Young KS. Understanding online gaming addiction and
treatment issues for adolescents. The American Journal
of Family Therapy, 2009; 37: p.355-372.
Yuan K, Qin W, Wang G, Zeng F, Zhao L, Yang X,
Liu P, Liu J, Sun J, von Deneen KM, Gong Q.
Microstructure abnormalities in adolescents with
internet addiction disorder. PloS one. 2011 Jun
3;6(6):e20708.
References
142
Zohar D, Tzischinsky O, Epstein R and Lavie P. The
effects of sleep loss on medical residents’ emotional
reactions to work events: a cognitive-energy model,
Sleep. 2005; 28(1):47-54.
Appendices
143
Appendix (1)
Internet Addiction Test (IAT)
اجت على الأسئلة الحبلية الوحعلقة ثبسحخذاهك للانحشنث هسحخذهب دسجبت
الحقيين الحبلية:
Appendices
144
Appendices
145
Appendix (2)
Internet Gaming Disorder Scale
إرا كنث جسحخذم ألعبة الانحشنث اقشأ هزة الأسئلة جيذا هن فضلك أجت )ثنعن( أو )لا( ..
.
Appendices
146
Appendix (3)
PSQI
Appendices
147
Appendices
148
Appendix (4)
SES- Scale
□ □ □
0 0
2 2
4 4
6 6
8 8
10 10
12 12
14 14
0 0
Appendices
149
Appendices
150
ا