Send Orders for Reprints to [email protected] Current Pharmaceutical Design, 2014, 20, 000-000 1 Internet Addiction: A Systematic Review of Epidemiological Research for the Last Decade D. J. Kuss*,1, M. D. Griffiths1, L. Karila2 and J. Billieux3 1International Gaming Research Unit, Nottingham Trent University, Burton Street, Nottingham NG1 4BU, UK; 2Addiction Research and Treatment Center, Paul Brousse Hospital, Paris Sud-11 University, AP-HP, INSERM-CEA U1000, Villejuif, France; 3Laboratory for Experimental Psychology, Psychological Science Research Institute, Catholic University of Louvain, Louvain-La-Neuve, Belgium Abstract: In the last decade, Internet usage has grown tremendously on a global scale. The increasing popularity and frequency of Inter- net use has led to an increasing number of reports highlighting the potential negative consequences of overuse. Over the last decade, re- search into Internet addiction has proliferated. This paper reviews the existing 68 epidemiological studies of Internet addiction that (i) contain quantitative empirical data, (ii) have been published after 2000, (iii) include an analysis relating to Internet addiction, (iv) include a minimum of 1000 participants, and (v) provide a full-text article published in English using the database Web of Science. Assessment tools and conceptualisations, prevalence, and associated factors in adolescents and adults are scrutinised. The results reveal the following. First, no gold standard of Internet addiction classification exists as 21 different assessment instruments have been identified. They adopt official criteria for substance use disorders or pathological gambling, no or few criteria relevant for an addiction diagnosis, time spent on- line, or resulting problems. Second, reported prevalence rates differ as a consequence of different assessment tools and cut-offs, ranging from 0.8% in Italy to 26.7% in Hong Kong. Third, Internet addiction is associated with a number of sociodemographic, Internet use, and psychosocial factors, as well as comorbid symptoms and disorder in adolescents and adults. The results indicate that a number of core symptoms (i.e., compulsive use, negative outcomes and salience) appear relevant for diagnosis, which assimilates Internet addiction and other addictive disorders and also differentiates them, implying a conceptualisation as syndrome with similar etiology and components, but different expressions of addictions. Limitations include the exclusion of studies with smaller sample sizes and studies focusing on specific online behaviours. Conclusively, there is a need for nosological precision so that ultimately those in need can be helped by trans- lating the scientific evidence established in the context of Internet addiction into actual clinical practice. Keywords: Internet addiction, literature review, epidemiology, empirical research, last decade, quantitative, large-scale. 1. INTRODUCTION condition as an analogue to substance dependence, as based on In contemporary society approximately 40% of the world popu- criteria in the Diagnostic and Statistical Manual for Mental Disor- lation is online. Furthermore, global Internet usage has grown ders [DSM-IV; 7]. Based on this, the individual had to experience a minimum of three of the following symptoms over the period of nearly six-fold over the last decade, with 96% of Internet users in Korea using high-speed Internet connections in comparison to 78% twelve months: tolerance, withdrawal, lack of control, relapse, large in the UK, and 56% in the USA [1, 2]. Compared to Internet access amounts of time spent online, negative consequences, and continua- tion of use irrespective of problem awareness [6]. Following this in 2000, the USA has more than doubled its usage, while mobile Internet use has increased substantially up to 2011 [3], indicating initial proposal, Young (1996a) and Griffiths [8] emerged as the that Internet use via different hardware has become a highly preva- pioneers of early research into Internet addiction as they were the first to scrutinise the phenomenon empirically. Modelling the Inter- lent activity for both adolescents and adults. From a global perspec- tive, Google is the most popular online destination, closely fol- net addiction criteria after the APA’s substance dependence diagno- lowed by the social networking site Facebook1 [3]. In 2012, chil- sis [7], Young [9] presented the case of a female homemaker who progressively increased her engagement in chat rooms because of dren and adolescents in Australia spent an average of 24 hours on- line per month, compared with 65 hours for those aged 18-24 years, her growing commitment to virtual communities, which have been and more than 100 hours per month in 25-34 year olds [4]. This described as offering emotional support and a platform for discus- sion and information [10]. The homemaker spent increasing suggests that young adults are the most active Internet users as they spend approximately three hours online per day. amounts of time online to the detriment of her real life responsibili- ties and eventually developed withdrawal symptoms [9]. This case The increasing popularity and frequency of Internet use has led exemplified for the first time that the stereotypical view of the ex- to the emergence of clinical cases presenting abuse symptoms. cessive Internet user, i.e., a young male technophile, had to be over- Since the 1980s, school counsellors were advised to take excessive thrown and in its place appeared a female user seeking a sense of use of video games seriously as it could result in “addiction” [5]. In belonging and comfort on the Internet. Griffiths (1996) also pub- 1996, the concept of Internet Addiction Disorder emerged for the lished case study accounts including both males and females. Fol- first time, initially as a satirical hoax as a response to the perceived lowing these initial case reports, Young [11] was among the first to pathologising of everyday behaviours [6]. Goldberg understood the present findings from an exploratory survey comprising 396 de- pendent Internet users who endorsed a minimum of five out of eight *Address correspondence to this author at the Doctoral Researcher, Interna- criteria adapted from a diagnosis of pathological gambling [7], and tional Gaming Research Unit. Nottingham Trent University, NG1 4BU 100 non-dependent Internet users. On average, the dependent users Nottingham, UK; Tel: + 44 789 111 94 90; E-mail: [email protected] spent eight times more hours online than the controls, and used chat rooms and MUDs2 more frequently [11]. These early studies can be 1 Only in Japan, Google and Facebook fall a few places behind services such as Yahoo! 2 Multi-User Dungeons, the exclusively textual precursors of today’s Massively Multi- 3. The Nielsen Company. State of the media: U.S. digital consumer report. The player Online Role-Playing Games (MMORPGs) 12.Mortensen TE. WoW is the new Nielsen Company2012.. MUD: Social gaming from text to video. Games and Culture2006;1(4):397-413. 1381-6128/14 $58.00+.00 © 2014 Bentham Science Publishers 2 Current Pharmaceutical Design, 2014, Vol. 20, No. 00 Kuss et al. seen as the beginning of empirical research into the area of Internet results. Following a thorough inspection of the articles’ titles and addiction. abstracts, the articles not meeting the inclusion criteria were ex- Since these initial efforts to shed light upon an emerging mental cluded. Data were organised with regards to assessment approach, prevalence, and factors associated with Internet addiction. health problem, empirical research into Internet addiction has greatly increased. Various terms have been used to name the condi- 3. RESULTS tion, including compulsive computer use (Black, Belsare, & Schlosser, 1999), Internet dependency (te Wildt, 2011), pathologi- A total of 69 epidemiological research papers were identified cal Internet use (Morahan-Martin & Schumacher, 2000), problem- from the literature search that met the initial inclusion criteria. atic Internet use (Davis, Flett, & Besser, 2002), virtual addiction However, one study [27] had to be excluded as it did not provide (Greenfield, 1999), and Internet addiction disorder (Ko, Yen, Chen, sufficient information on how Internet addiction was assessed. Chen, & Yen, 2005a). Recently, the APA (2013b) published the Therefore, a total of 68 studies were included in this literature re- updated version of the DSM and included Internet Gaming Disorder view. The first part of the results section will present the assessment in the appendix as condition that requires further empirical and approaches adopted, as they highlight the various conceptualisa- clinical research. In the DSM-5, Internet Gaming Disorder includes tions of Internet addiction, which will be classed in accordance with nine criteria, namely preoccupation, withdrawal, tolerance, loss of the specific samples used, namely adolescents and adults. Three control, continued use irrespective of problem awareness, neglect of main diagnostic assessment approaches comprised Young’s Internet alternative recreational activities, escapism and mood modification Addiction Test and Internet Addiction Diagnostic Questionnaire as usage motivations, deception, and jeopardisation of relationships [11, 28], Chen et al.’s Chinese Internet Addiction Scale [29], and and job. This clearly situates the behaviour within the new diagnos- various miscellaneous approaches for classification. The next part tic entity of Addiction and Related Disorders. Five or more symp- will summarize the reported prevalence rates, which will be fol- toms need to be met over a 12-month period for diagnosis which lowed by the last part that outlines the factors that that have been must cause the individual clinically significant impairment or dis- found to be statistically associated with Internet addiction. tress [13, 14]. The conflation of Internet use and online gaming in 3.1. What is Internet addiction? Assessment tools and Concep- this diagnostic category creates further diagnostic imprecision as tualisations seven out of the nine criteria relate to gaming specifically. There- fore, although the inclusion of Internet Gaming Disorder in the 3.1.1. The Internet Addiction Test and the Internet Addiction research appendix of the DSM-5 emphasise the necessity for further Diagnostic Questionnaire research, the new research diagnosis appears somewhat crude and Two related, but slightly different tools for Internet addiction vague, further complicating a clinical evaluation. Although empiri- assessment have been developed by Young [11, 28]. The Internet cal research over the last decade has significantly increased, the Addiction Test (IAT) [28] is a 20-item self-report scale that as- classification of Internet addiction is still controversial as no gold sesses Internet addiction as based on criteria for substance depend- standard of Internet addiction assessment has emerged. A number ence and pathological gambling [7]. The criteria include loss of of review papers on Internet addiction have been published since control, neglecting everyday life, relationships and alternative rec- 2005 [15-18]. Some of the most recently published reviews specifi- reation activities, behavioural and cognitive salience, negative con- cally integrated treatment outcome research [19-21] and comorbid- sequences, escapism/mood modification, and deception, and are ity [22], while others have looked at the biological basis and the rated on a Likert scale ranging from 1 (“not at all”) to 5 (“always”), psychological factors involved in the aetiology for the disorder allowing a dimensional rather than categorical assessment. Internet [e.g., 23, 24]. Another study [25] suggests that current Internet ad- users are classed as having significant problems due to Internet use diction assessment tools tap into the following dimensions of addic- if they score 70-100, and having frequent problems when scoring tion: compulsive use, negative outcomes, salience, withdrawal 40-69 [28]. The internal consistency of the IAT has been reported symptoms, mood regulation, escapism and social comfort, which as satisfactory, with a Cronbach’s alpha of .84 [30]. The IAT does are comparable with Griffiths’ [26] behavioural addiction compo- not contain a temporal dimension by asking the participant to rate nents. These reviews highlight the dissimilarity in assessment the presence of the symptoms over a specified period of time. across studies that impede the possibility of cross-comparisons as Moreover, the cut-offs appear rather arbitrary as they are not based well as an evaluation of the epidemiological prevalence rates across on empirical considerations, such as a clinical evaluation of disor- samples. In order to elucidate the potential problem of Internet ad- der severity based on the presence and impact of symptoms. A re- diction, the aim of this paper is to review the epidemiological Inter- cent study [31] including a Greek adolescent sample indicates that a net addiction research of the last decade. This review sets out to lower cut-off point of 51 presents the highest specificity and sensi- answer the following research questions: (i) what is Internet addic- tivity. This finding raises issues concerning the cultural context of tion (i.e., how is it assessed)?, (ii) how common is it?, and (iii) what analysis, suggesting that sociocultural factors impact upon Internet are the associated factors? addiction assessment. 2. METHOD The Internet Addiction Diagnostic Questionnaire (IADQ) [11] is a parsimonious 8-item self-report measure based on the diagnos- A literature search was conducted using the database Web of tic symptoms of pathological gambling [7]. The criteria utilised for Science. This database was used as it is more comprehensive than the IADQ include preoccupation, tolerance, loss of control, with- other commonly used databases, such as Psycinfo or PubMed be- drawal, negative consequences, denial, and escapism. Two of the cause it includes various multidisciplinary databases. The following original ten criteria for pathological gambling (i.e., committing search terms (and their derivatives) were entered with regards to illegal acts to finance the behaviour and reliance on others for Internet addiction specifically: ‘Internet’ or ‘online’ and ‘exces- money) were omitted to produce a “slightly more rigorous cut-off sive’, ‘problematic’, ‘compulsive’, and ‘addictive’. Studies were score” [11]. Endorsing five or more of the criteria indicates Internet selected based on the following inclusion criteria. Studies had to (i) addiction. contain quantitative empirical data, (ii) have been published after 2000, (iii) include an analysis relating to Internet addiction, (iv) 3.1.2. Chen’s Internet Addiction Scale include a minimum of 1000 participants, and (v) provide a full-text Chen’s Internet Addiction Scale (CIAS) [29] was the most fre- article published in English. For comparison purposes, studies fo- quently used scale in the included empirical research papers as a cusing solely on particular online applications (e.g., gaming, social total of 16 studies made use of it to assess Internet addiction. The networking) were excluded from analysis. The databases were CIAS is a 26-item self-report measurement scored on a 4-point searched in April and May 2013. The initial search yielded 1,332 Likert scale, assessing the core symptoms of Internet addiction, Internet Addiction Current Pharmaceutical Design, 2014, Vol. 20, No. 00 3 tolerance, compulsive use, and withdrawal, as well as related prob- sample in Southern Taiwan [72], whereas in other adolescents sam- lems in terms of negative impact on social activities, interpersonal ples in Southern Taiwan prevalence rates between 18% and 21% relationships, physical condition, and time management. In addition were reported [73, 75, 77, 79, 80, 82, 83]. to this, it inquires into weekly online hours and Internet use experi- A total of 13 studies used miscellaneous criteria to identify ence. The internal consistency of the scale was found to be good, Internet addiction in adolescents [33-45]. Sample sizes varied from with Cronbach’s alpha values between .79 to .93 for the respective including 1,098 adolescents in Singapore [36] to 73,238 adolescents subscales [29]. It has also been reported that the screening cut-off in South Korea [44]. Sung and colleagues [44] used the Internet of 57/58 points has high sensitivity, and the diagnostic cut-off point Addiction Proneness Scale - Short Form [KS-Scale; 84] in a sample of 63/64 as performed by psychiatrists revealed the highest diagnos- of 73,238 adolescents in South Korea and reported that 3.0% and tic accuracy with 87.6% of patients diagnosed with Internet addic- 11.9% of adolescents were at high risk and at potential risk for de- tion appropriately [32].(cid:1)Adopted cut-off points for Internet addic- veloping Internet addiction in South Korea, respectively [44]. On tion varied marginally between studies, as scores of 63/64 or 67/68 the other end of the spectrum, Xu et al. [38] used the DRM 52 have been used as cut-offs for Internet addiction classification, Scale of Internet use in a random sample of 5,122 adolescents in without the respective authors specifying reasons for their choice, Shanghai, China with the result that 8.8% of adolescents in this such as the instrument’s factor structure. sample were classified as Internet addicts [38]. The only cross- 3.1.3. Miscellaneous Diagnostic Assessment Tools cultural study of Internet addiction prevalence included two sepa- rate samples of 1,761 high school students in China and 1,182 stu- The remaining assessment tools represent a plethora of newly dents in the USA were used in a longitudinal study by Sun et al. designed measurement instruments or alternative criteria based on which Internet addiction and Internet use-related problems have [40] using the (CIUS), and showed that the prevalence rates were 5.8% in Chinese females, 15.7% in Chinese males, 9.7% in US been categorised. A total of 21 studies were identified that used females, and 7.3% in US males [40]. A detailed summary of the miscellaneous criteria. Of these, 14 studies used miscellaneous criteria to identify Internet addiction in adolescents [33-45]. In ad- epidemiological studies that assessed Internet addiction prevalence in adolescents is provided in Table 2. dition to the adolescent samples, miscellaneous classification crite- ria for Internet addiction have been used in adult samples, including 3.2.2. The Prevalence of Internet Addiction in Adults a total of eight studies [46-53]. Classifications vary tremendously, In six studies, Young’s Internet Addiction Test [28] was used to ranging from the adoption of official criteria for substance use dis- assess Internet addiction in adults [85-90]. The sample sizes ranged orders or pathological gambling, to no or few criteria relevant for from 1,034 young adults in Turkey [86] to 13,588 Internet users in an addiction diagnosis. In yet other cases excessive use is assessed Korea [89]. Similar to the usage of the IAT in adolescent samples, based on how much time is spent online or how many problems in the adult samples, various cut-off criteria have been utilised in occur as a consequence of use, providing an overly simplistic pic- order to demarcate Internet addiction from non-pathological Inter- ture of Internet addiction. Detailed information concerning each of net usage behaviours. Reported prevalence rates using the IAT the assessment instruments, criteria, and problems with the respec- ranged from 1.2% of Internet users in the UK [88] to 9.7% of Turk- tive classifications are provided in Table 1. ish college students [86]. 3.2. How Common is Internet Addiction? The Internet Addiction Diagnostic Questionnaire [11] was used in three adult samples [91-93]. The reported Internet addiction 3.2.1. Prevalence of Internet Addiction in Adolescents prevalence rates in these studies were notably diverse as in a sample A total of seven studies used the IAT for Internet addiction of Norwegian adults, 1.0% [92] and in a sample of 1,856 Iranian assessment in adolescents and children aged 8 to 24 years [54-60], Internet users 22.8% [91] were found to be addicted to the Internet. with sample sizes ranging from 1,618 [58] to 17,599 participants Chen’s Internet Addiction Scale was used in seven studies in- [56]. Although the same measurement instrument has been used in cluding adult samples [94-100]. All samples included college or these studies, various cut-offs have been applied to demarcate ad- university students in Taiwan. Sample sizes ranged from 1,360 diction or excessive use across studies. Reported prevalence rates university freshmen [96] to 4,456 college students [99]. The studies varied significantly with 0.8% in Italian high school students were that reported prevalence rates used teenage samples. Using the considered to be seriously addicted [55], and 20.3% of adolescents rather conservative cut-off of 67/68 on the CIAS, relatively similar and 13.8% of children in a South Korean sample were classed as prevalence rates of 12.9% and 12.3% have been reported by Yen et addicted to using the Internet [60]. al. in Taiwan [97, 101], ranging up to 17.9% as reported by Tsai et In eleven studies, the IADQ [11] was used to assess Internet al. [96]. addiction in adolescents [61-71]. The sample sizes ranged from Miscellaneous classification criteria for Internet addiction have 1,270 in Greece [64, 65] to 10,988 adolescents and young adults in been used in a total of eight studies including adult samples [46- China, aged 13-23 years [71]. The same cut-off, i.e., endorsing a 53]. All sample sizes were between 1000 and 2000 participants, minimum of five out of eight diagnostic items, has been applied to a with the exception of a sample of 16,925 regular Internet users in majority of these studies. Internet addiction prevalence rates ranged the Netherlands [46]. Prevalence rates varied, ranging from 1.8% of from 1.7% of boys and 1.4% of girls in a representative sample of a sample of 1,147 participants in Sweden (age range 15-94 years) Finnish adolescents [70] to 26.4% and 26.7% at wave one and wave experienced all of the inquired problems due to Internet use, two, in a longitudinal sample of adolescent students in Hong Kong, whereas Demetrovics and colleagues [48] reported that of a sample respectively [66]. The reported prevalence rates in Asian adoles- of 1,037 Hungarian young adults, 4.3% had significant problems cents have been found to be significantly higher in comparison to because of their Internet use as measured via the PIUQ. A complete both, Western countries, as well as samples of children. summary of the epidemiological studies of Internet addiction in Chen’s Internet Addiction Scale was used in nine studies in- adults is provided in Table 3. cluding adolescent samples [72-80]. The sample sizes ranged from 1,890 students in Taiwan [80] to including 9,405 in Southern Tai- 3.3. What are the Associated Factors? wan [81]. In all of these studies, the relatively liberal cut-off point Four main factors have been found to be associated with Inter- of 63/64 on the CIAS has been applied. Prevalence estimates varied net addiction. A visual representation of these factors is presented substantially, with the lowest rate of 10.8% found in an adolescent in (Fig. 1). 4 Current Pharmaceutical Design, 2014, Vol. 20, No. 00 Kuss et al. Table 1. Internet Addiction Assessment Instruments. Study Instrument Structure Addiction classification and criteria Cut-off Problems Young, 1998a Internet Addic- 20-item self-report Criteria for substance dependence and - Score of 70-100: - No temporal tion Test (IAT) scale rated on a Likert pathological gambling (American Psy- significant prob- dimension scale ranging from 1 chiatric Association, 1994): loss of con- lems - Cut-offs arbitrary (“not at all”) to 5 trol, neglecting everyday life, relation- - Score of 40-69: (“always”) ships and alternative recreation activities, frequent problems behavioural and cognitive salience, nega- tive consequences, escapism/mood modi- fication, and deception Young , 1998b Internet Addic- 8-item self-report Based on the diagnostic symptoms of Endorsing (cid:1)5/8: - No equivalents for tion Diagnostic measure scored dicho- pathological gambling (American Psy- Internet addiction PG criteria commit- Questionnaire tomously chiatric Association, 1994): preoccupa- ting illegal acts to (IADQ) tion, tolerance, loss of control, with- finance the behav- drawal, negative consequences, denial, iour and reliance on and escapism others for money - Dichotomous scoring Chen et al., 2003 Chen’s Internet 26-item self-report Core symptoms of Internet addiction, - Liberal scoring: Different cut-offs Addiction Scale measurement scored tolerance, compulsive use, and with- 63/64, used for classifica- (CIAS) on a 4-point Likert drawal, as well as related problems in - Conservative: tion scale terms of negative impact on social activi- 67/68 indicates ties, interpersonal relationships, physical Internet addiction condition, and time management Meerkerk et al., Compulsive 14-item unidimen- Based on the DSM-IV-TR diagnoses for N/A - No cut-off 2009a Internet Use sional self-report ques- substance dependence and pathological - No assessment of Scale (CIUS) tionnaire rated on a 5- gambling (American Psychiatric Asso- tolerance point scale ciation, 2000): loss of control, preoccu- pation, withdrawal symptoms, cop- ing/mood modification, and conflict (inter- and intrapersonal) Caplan, 2000 Generalized 29-item self-report Based on Davis’ (2001) cognitive- N/A Not all items rele- Problematic questionnaire rated on behavioural model of problematic Inter- vant for addiction Internet Use 5-point Likert scale net use; measures mood alteration, per- classification Scale (GPIUS) ceived social benefits online, negative consequences of and compulsive Internet use, excessive amounts of time spent online, withdrawal, and perceived social control online Caplan, 2010 Modified Gener- 15-item self-report Similar to GPIUS (Caplan, 2000), but N/A Not all items rele- alised Problem- questionnaire rated on includes 2 additional factors: preference vant for addiction atic Internet Use 8-point Likert scale for online social interaction and deficient classification Scale (GPIUS2) self-regulation (as higher-order factor impacting upon cognitive preoccupation and compulsive Internet use), and the previous factors social benefits and so- cial control were combined Kim et al., 2008 Internet Addic- 20 items scored on a 4- Criteria: tolerance, withdrawal, addictive - Scoring (cid:1) 52/80: Not all items rele- tion Proneness point Likert scale automatic thoughts, disturbance of adap- high risk for In- vant for addiction Scale - Short tive function, deviate behaviours, and ternet addiction classification Form (KS-Scale) virtual interpersonal relationships - Scoring 48-52: potential risk Internet Addiction Current Pharmaceutical Design, 2014, Vol. 20, No. 00 5 (Table 1) Contd…. Study Instrument Structure Addiction classification and criteria Cut-off Problems Lopez-Fernandez Problematic 30 items rated on a 7- Based on DSM-IV-TR criteria for sub- N/A No cut-off et al., 2013 Internet Enter- point Likert scale stance dependence and pathological tainment Use gambling disorders: assesses symptom Scale for Adoles- experience over last 12 months cents (PIEUSA) Xuet al., 2012 DRM 52 Scale of Includes direct and Adapted from Young’s Internet Addic- Scoring >163/260 Not all items rele- Internet Use indirect questions tion Scale (Young, 1996a); criteria: indicates Internet vant for addiction organised into 52 tolerance, withdrawal, planning, lack of addiction classification items assessed on a 5- control, time-consuming, socialisation, point Likert scale and negative life consequences because of Internet use Beranuy et al., Questionnaire on 10 questions scored on Criteria: interpersonal and intrapersonal N/A No use of recog- 2009 Internet-Related a 4-point Likert scale conflicts nised diagnostic Experiences criteria (CERI) Sun et al., 2010 Compulsive 4 items on 5-point Based on Davis et al.’s (2002) Online Scoring mean of No use of recog- Internet Use Likert scale Cognition Scale 4/possible 5: nised diagnostic Scale (CIUS) Internet addiction criteria Liu et al., 2011 Problematic 6 items scored Based on Minnesota Impulsive Disorder Endorsing crav- Overly simplistic Internet Use dichotomously Inventory (Grant, Levine, Kim, & ing, withdrawal, classification Scale (PIU) Potenza, 2005) abstinence at- tempts simultane- ously: problem- atic Internet use Bener et al., Excessive Inter- Daily hours spent Length of daily Internet use Spending (cid:1) Overly simplistic 2011 net use online 3hours on- classifcation line/daily: exces- sive Internet use Mythily et al., Excessive Inter- Daily hours spent Length of daily Internet use Spending (cid:1) Overly simplistic 2008 net use online 5hours on- classifcation line/daily: exces- sive Internet use Wölfling et al., Assessment for 16 items scored on 5- Based on diagnostic criteria of substance Scoring (cid:1) Lack of time crite- 2010 Computer and point Likert scale dependence by DSM-IV-TR (American 13.5/27: Internet rion Internet Addic- Psychiatric Association, 2000) and ICD- addiction tion-Screener 10 (World Health Organization, 1992); (AICA-S) criteria: craving, tolerance, withdrawal, loss of control, preoccupation and nega- tive consequences concerning poorer health, family conflicts or deteriorating achievements, mood modification Thatcher & Problematic 20 items scored on 5- Based on Young’s criteria for Internet N/A Not all items rele- Goolam, 2005 Internet Use point Likert scale addiction (1996b) and the South Oaks vant for addiction Questionnaire Gambling Screen (Lesieur & Blume, classification (PIUQ) 1987), assesses online preoccupation, adverse effects, and online social interac- tions 6 Current Pharmaceutical Design, 2014, Vol. 20, No. 00 Kuss et al. (Table 1) Contd…. Study Instrument Structure Addiction classification and criteria Cut-off Problems Demetrovics et Problematic 30 items scored on a 5- Based on the Internet Addiction Ques- - Scoring > 2SD Overly simplistic al., 2008 Internet Use point Likert scale tionnaire (Nyikos, Szeredi, & above mean: classification, lacks Questionnaire Demetrovics, 2001) and the Internet significant prob- some addiction (PIUQ) Addiction Test (Young, 1998a), assesses lems because of criteria obsession, neglect and control disorder Internet use - Scoring 1-2SD above mean: problematic Inter- net use Ceyhan et al., Problematic 33 items scored on 5- Factors: negative consequences, social N/A Overly simplistic 2007 Internet Use point Likert scale benefit/comfort, and excessive usage classification, lacks Scale (PIUS) important addiction criteria Huang et al., Chinese Internet 42 items scored on 5- Based on Young’s Internet Addiction For diagnosis, all N/A 2007 Addiction Inven- point Likert scale Test (1998a), 3 dimensions of Internet of the following tory (CIAI) addiction: conflicts, mood modification, must be endorsed: and dependence; classification based on preoccupation, “5+3” principle (Beard & Wolf, 2001) tolerance, lack of impulse control, mood modifica- tion, increasing usage, and (cid:1) 1 of conflict, lying to others, and escap- ing from problems Bergmark et al., Indicators of Presence of 5 indica- Indicators: time spent online, family N/A - Likert-scale scores 2011 Internet addiction tors rated on 4-point conflicts due to Internet use, withdrawal converted to binary Likert scale symptoms, neglect of needs, and unsuc- measures cessful abstinence attempts - Not all items rele- vant for addiction classification used Beutel et al., Problems because Number of problems Problem areas: work, school, family, N/A No use of recog- 2011 of Internet use due to Internet use partnership, finances, recreational activi- nised diagnostic ties, health-related criteria Table 2. Epidemiological Internet Addiction Studies in Adoelscents. Addiction classification Study Sample and design1 Instruments Results and criteria Ak et al., N = 4,311 adolescents in Tur- - Turkish version of Internet Addiction - Scoring (cid:1)60/100 on the - 5% excessive users 2013 key (46% male, age range 15- Test (IAT) (Young, 1998b) IAT = excessive Internet - Predictors of Internet addic- 19 years) users tion: Internet access at home, male gender, family income Poli & N = 2,533 high school students - Italian version of the Internet Addiction - Scoring 50-79/100 = - 5.01% moderately and 0.79% Agrimi, in Cremona, Italy (44.3% Test (IAT) (Young, 1998b) moderately addicted seriously addicted to the Inter- 2012 males, mean age = 16.4 years, - Scoring (cid:1) 80 = seri- net SD = 1.51, range 14-21) ously addicted - Higher prevalence in males Internet Addiction Current Pharmaceutical Design, 2014, Vol. 20, No. 00 7 (Table 2) Contd…. Addiction classification Study Sample and design1 Instruments Results and criteria Cao et al., N = 17,599 students in 8 cities - Young’s Internet Addiction Test (YIAT) - Potential problematic - Problematic Internet use 2011 in China (51.2% male, mean (Young, 1998a) prevalence 8.1% Internet use (PIU): sco- age = 16.1, SD = 2.8 years, - Multidimensional Sub-health Question- res > 50/100 on YIAT - PIU associated with male range = 10-24) naire of Adolescents (Tao, Hu, Sun, & gender, high school status, Hao, 2008) urban, Eastern and Western areas, high family economy, - Multidimensional Students’ Life Satisfac- tion Scale (Tian & Liu, 2005) Internet for entertainment use, loneliness motivation, and - Demographics and Internet usage patterns Internet use frequency - PIU adolescents had higher psychosomatic symptoms, lacked physical energy, physio- logical dysfunction, weakened immunity, emotional and be- havioural symptoms, social adaptation problems , low life satisfaction relative to non-PIU Wang et N = 14,296 high school stu- - Young Internet Addiction Test (YIAT) - Potential problematic - 12.2% problematic Internet al., 2011 dents in Guangdong Province, (Young, 1998a) Internet use: scoring > users China (48.7% males) - Demographics 50/100 on YIAT - Risk factors for PIU: study- - Family and school factors related stress, social friends, poor relations with teachers and - Internet usage pattern students, conflicts in family relations, time spent online Lam et N = 1,618 adolescents (45.4% - Internet Addiction Test (Young, 2009) - Scoring 20-49 on IAS - 10.2% moderately and 0.6% al., 2009 male, age range = 13-18 years) = normal, 50-79 moder- severely addicted to the Inter- - Zung Self-Rating Depression Scale in Guangzhou city, China (Zung, 1965) ate, and 80-100 = severe net Internet addiction - Risk factors: male gender, drinking behaviour, family dissatisfaction, and recent stressful events Choi et N = 2,336 high school students - Korean version of Young’s Internet Ad- - Scoring (cid:1) 70 on IAT - Prevalence of Internet addic- al., 2009 in South Korea (57.5% male, diction Test (Kim, 2000; Young, 1998a) addicted, 40-69 possibly tion and possible Internet ad- mean age = 16.7, SD = 1.0 addicted diction: 2.5% and 53.7% for - Epworth Sleepiness Scale (ESS) (Johns, years) boys, and 1.9% and 38.9% for 1991) girls - Internet addicts more likely to be male, drink more alcohol, have poor health condition, experience EDS Kim et N = 1,573 high school students - Korean version of the modified Internet - Scoring > 70/100 on - 1.6% addicted to the Internet al., 2006 in Korea (35.0% males, aged Addiction Scale (Kim, 2000; Young, IAS = Internet addiction, - 38.0% possibly addicted to 15-16 years) 1998a) scoring 40-69 = possible the Internet Internet addiction - Korean version of the Diagnostic Inter- - Depression and suicidal idea- view Schedule for Children-Major Depres- tion highest in the Internet sion Disorder-Simple Questionnaire addicts (Korean Neuropsychiatry Association, 1999) - Suicidal Ideation Questionnaire-Junior (Reynolds, 1988) 8 Current Pharmaceutical Design, 2014, Vol. 20, No. 00 Kuss et al. (Table 2) Contd…. Addiction classification Study Sample and design1 Instruments Results and criteria Ha et al., - Structured clinical interview - Young’s Internet addiction scale - Cut- off of 80 - Internet addiction prevalence 2006 in adolescents 20.3%, in chil- - Ns = 455 children (50.3%; - K-SADS-PL-K for children dren 13.8% mean age = 11, SD = .9 years) - SCID-IV for adolescents and 836 adolescents (92.9% - In child Internet addiction male; mean age = 15.8, SD = .8 group, 7 with ADHD years) - In adolescent Internet addic- - Of Internet addicts, 12 chil- tion group, 3 with depression, 1 dren and 12 adolescents ran- schizophrenia, 1 obsessive- domly selected for psychiatric compulsive disorder evaluation Guo et N = 3,254 children (mean age - Young's 8-item Internet Addiction Scale - Endorsing (cid:1) 5/8 items - Internet addiction prevalence al., 2012 = 12.56, SD = 1.83 years; age (Young, 1998) on IAT = Internet ad- = 3.7% in RC, 6.4% in MC and range = 8-17-years), with n = - Children's Depression Inventory-Short dicted 3.2% in LBC 1143 left behind children Form (CDI-S) (Kovacs, 2004) - LBC and MC with Internet (LBC; 49.9% male), n = 574 - Nutritional status, health condition and addiction, and MC without migrant children (MC; 57.1% health behaviours Internet addiction more at risk male), and n = 1287 non-left- for depression than RC with no behind rural children (RC; Internet addiction 51.8% male) in China Siomos et N = 2,017 teenage students - Diagnostic Questionnaire for Internet - Scoring min. 5/8 indi- - 15.2% addicted to the Inter- al., 2012 (51.8% males, boys’ mean age Addiction (YDQ) (Young, 1998) cates Internet addiction net, 26.9% moderately addicted = 15.05, SE = .05;8, girls’ - Greek version of Adolescent Computer - Internet addiction predicted mean age = 15.08, SE = .05; Addiction Test (ACAT; modelled after by parental bonding, not paren- overall age range = 12-19) in Internet Addiction Test) (Siomos, Floros, tal security practices Greece, and n = 1,214 parents Mouzas, & Angelopoulos, 2009) - Online activities associated - Parental Bonding Instrument (Parker, with Internet addiction: online 1990) pornography, gambling, and gaming Siomos et Randomized stratified sample - Diagnostic Questionnaire for Internet - Scoring min. 5/8 indi- - Prevalence of Internet addic- al., 2008 of N = 2,200 adolescents stu- Addiction (YDQ) (Young, 1998) cates Internet addiction tion 8.2%, mostly male online dents in Greece (mean age = - Sociodemographics gamers who visit Internet cafés 15.34, SD = 1.66, range = 12- 18 years) Fisoun et N = 1,270 adolescent students - Diagnostic Questionnaire for Internet N/A - 5.3% addicted users, 14.7% al., 2012 on Kos (48.3% male, mean age Addiction (YDQ) (Young, 1998) heavy Internet users = 15.99, SE = .05, girls’ mean - Internet Addiction Test (Young, 1998) - Correlations between antiso- age = 16.02, SE = .05, age cial and aggressive behaviours - Demographic questions range 14-18 years) with Internet abuse regarding interest-driven activities for boys, and communication ac- tivities for girls Fisoun et N = 1,270 adolescent students - Internet Addiction Test (Young, 1998) - Scoring 5/8 on IAT = - 7.2% of males, and 5.1% of al., 2012 on Kos (48.3% male, mean age addicted to the Internet females addicted to the Internet - Demographic questionnaire, incl. ques- = 15.99, SE = .05, girls’ mean tions on substance use - Internet and substance abusers age = 16.02, SE = .05, age - Eysenck’s Personality Questionnaire share personality characteris- range 14-18 years) tics, i.e., psychoticism (Gossop & Eysenck, 1980) - Pathological Internet use severity related to illicit sub- stance use Internet Addiction Current Pharmaceutical Design, 2014, Vol. 20, No. 00 9 (Table 2) Contd…. Addiction classification and Study Sample and design1 Instruments Results criteria Shek & - Longitudinal survey (2 - Internet Addiction Test (IAT) - Internet addiction diagnosis - Internet addiction prevalence Yu, 2012 waves) in Hong Kong (Young, 1998) based on DSM-IV gambling 26.4% in W1, and 26.7% in W2 criteria (5/8?) - N = 3,328 students, (52.1% - Internet addiction at W1 1 males; mean age = 12.59, - Chinese Positive Youth De- increased chance of Internet SD = .74 years) velopment Scale (CPYDS) addiction at W2 by 7.6 - N = 3,580 students, mean age 2 = 13.50 years, SD = .75) Gong et N = 3,018 secondary school - Young’s Internet Addiction Diag- - Scoring (cid:1) 5/8 on DQ = Inter- - Prevalence of addictive Inter- al., 2009 and university students (47% nostic Questionnaire (DQ) (Young, net addicted net use 5% male, mean age = 15.8, SD = 1999) - DU and DU intentions pre- 2.1 years, age range = 11-23 - Lifetime drug use dicted by AIU, and mediated by years) in Wuhan, China PDA, ADA, and perceived - Susceptibility to drugs social norm of DU - PDA and ADA as based on Stan- dardized Attitudes and Knowledge Scale (STAK) (Chappel, Veach, & Krug, 1985) - Social norm of drug use Lin et al., N = 1,289 adolescents from 11 - Internet Addiction Diagnostic - Endorsing (cid:1) 5/8 of criteria = - 23.4% addicted to the Internet 2009 senior high schools in Taiwan Questionnaire (Young, 1998) Internet addiction - Internet addiction predicted (52.1% males, mean age = - Parental monitoring (Patterson & by parental monitoring percep- 17.46, SD = 1.00, range 16-19 Stouthamer-Loeber, 1984) tion, leisure boredom and ac- years) - Adapted Leisure Boredom Scale tivities (Iso-Ahola & Weissinger, 1990) - Family and outdoor activities, - Leisure activities participation supportive parental monitoring decreased addiction likelihood Johansson Representative sample of Nor- - Internet Addiction Diagnostic - Endorsing (cid:1) 5/8 criteria = - 1.98% Internet addicts & wegian youth (N = 3,237, Questionnaire (Young, 1998) classed as Internet addicts, - 8.68% at risk for developing Gotestam, 51.0% male, mean age = 14.9 endorsing 3-4 = at risk Internet addiction 2004 years, age range 12-18 years) Kaltiala- Representative sample of Fin- - Internet Addiction Test (Center for - Endorsing (cid:1) 4/7 DSM –IV - 1.7% of boys and 1.4% of Heino et nish adolescents (N = 7,292, On-Line Addiction, 2001) pathological gambling criteria girls addicted to the Internet al., 2004 age range 12-18 years) classed as Internet addicted - Addicts spent more time online than non-addicts Wang et N = 10,988 adolescents from 9 - Diagnostic Questionnaire (DQ) for - Endorsing (cid:1) 5/8 symptoms = - 7.5% prevalence of Internet al., 2013 cities in China (age mean = Internet addiction (Kuang, Cao, & Internet addiction addiction 17.2 years, range 13-23 years) Dai, 2011) - Breadth of extracurricular - Center for Epidemiologic Studies activities, age of first Internet Depression Scale (Chien & Cheng, use, Internet use for first time 1985) in Internet bar: significant predictors of Internet addiction - Rosenberg Self-esteem Scale (Rosenberg, Schooler, & - Problematic use associated Schoenbach, 1989) with low self-esteem, life satis- faction, high depression - Adolescent's Satisfaction with Life Scale (Zhang & Gao, 2010) 10 Current Pharmaceutical Design, 2014, Vol. 20, No. 00 Kuss et al. (Table 2) Contd…. Addiction classification and Study Sample and design1 Instruments Results criteria Ko et al., - 2-year prospective study - Chen Internet Addiction Scale - Scoring (cid:1) 64/104 on CIAS = - 10.8% addicted to the Internet 2009 (Chen, Weng, Su, Wu, & Yang, addicted to the Internet - N = 2,293 adolescents (51.4% - Depression, ADHD, social 2003) male, mean age = 12.36, SD = phobia, and hostility predicted .55 years) in Southern Taiwan - Modified Vanderbilt ADHD Diag- Internet addiction nostic Parent Rating Scale (Wolraich - Hostility predicted Internet et al., 2003) addiction in males and ADHD - Mandarin Chinese version of the in predicted Internet addiction Center for Epidemiological Studies in females Depression Scale (CES-D) (Chien & Cheng, 1985) - Brief Version of the Fear of Nega- tive Evaluation Scale (BV-FNE) (Leary, 1983) - Buss-Durkee Hostility Inventory- Chinese Version-Short Form (BDHIC-SF) (Lin et al., 2008) Ko et al., N = 9,405 adolescents (48.2% - Chen Internet Addiction Scale - Scoring (cid:1) 64/104 on CIAS - 18.8% addicted to the Internet 2009 male, age range = 13-17 years) (CIAS) (Chen, et al., 2003) indicates Internet addiction - Internet addicts more likely to in Southern Taiwan - Adolescent Aggressive Behaviors behave aggressively during last Questionnaire (McConville & year (particularly in junior high Cornell, 2003) school rather than senior high - Internet behaviours school) - Violent TV programme exposure - Chinese version of APGAR index of family function satisfaction (Smilkstein, 1978) - Mandarin Chinese version of Cen- ter for Epidemiological Studies’ Depression Scale (CES-D) (Chien & Cheng, 1985) - Rosenberg Self-Esteem Scale (Rosenberg, 1986) Yen et al., N = 8,941 adolescents in Tai- - Chen Internet Addiction Scale - Scoring >63 on CIAS = - Internet addiction prevalence: 2009 wan (48.0% male, mean age = (CIAS) (Chen, et al., 2003) Internet addicted 13.8% in old girls ((cid:1) 15 years), 17.7, SD = 1.7 years) 12.2% in young girls (< 15 - Chinese version of the Center for Epidemiological Studies’ Depression years), 26.6% in old boys ((cid:1) 15 Scale (CES-D) (Radloff, 1977) years), and 22.5% in young boys (< 15 years) - Adapted subscale of the Adolescent Family and Social Life Question- - Internet addiction predicted naire (AFSLQ) (Yen & Shieh, 2006; by depression, low family Yen, Yang, & Chong, 2006) monitoring, low connectedness to school, family conflict, drin- - Chinese-version of the Family king peers, living in rural areas APGAR Index (Smilkstein, 1978) - Rosenberg Self-Esteem Scale (RSES) (Rosenberg, 1965)