0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Article | Journal Club

Predictive Values of Psychiatric Symptoms for Internet Addiction in Adolescents:  A 2-Year Prospective Study FREE

Chih-Hung Ko, MD; Ju-Yu Yen, MD; Cheng-Sheng Chen, MD; Yi-Chun Yeh, MD; Cheng-Fang Yen, MD, PhD
[+] Author Affiliations

Author Affiliations: Department of Psychiatry, Kaohsiung Medical University Hospital (Drs Ko, J.-Y. Yen, Chen, Yeh, and C.-F. Yen), Graduate Institute of Medicine (Drs Ko, J.-Y. Yen, and Chen) and Department of Psychiatry (Drs Ko, J.-Y. Yen, Chen, and C.-F. Yen), College of Medicine, Center of Excellence for Environmental Medicine (Dr Ko), and Department of Psychiatry, Kaohsiung Municipal Hsiao-Kang Hospital (Dr J.-Y. Yen), Kaohsiung Medical University, Kaohsiung City, Taiwan.


Arch Pediatr Adolesc Med. 2009;163(10):937-943. doi:10.1001/archpediatrics.2009.159.
Text Size: A A A
Published online

Objectives  To evaluate the predictive values of psychiatric symptoms for the occurrence of Internet addiction and to determine the sex differences in the predictive value of psychiatric symptoms for the occurrence of Internet addiction in adolescents.

Design  Internet addiction, depression, attention-deficit/hyperactivity disorder, social phobia, and hostility were assessed by self-reported questionnaires. Participants were then invited to be assessed for Internet addiction 6, 12, and 24 months later (the second, third, and fourth assessments, respectively).

Setting  Ten junior high schools in southern Taiwan.

Participants  A total of 2293 (1179 boys and 1114 girls) adolescents participated in the initial investigation.

Main Exposure  The course of time.

Main Outcome Measure  Internet addiction as assessed using the Chen Internet Addiction Scale.

Results  Depression, attention-deficit/hyperactivity disorder, social phobia, and hostility were found to predict the occurrence of Internet addiction in the 2-year follow-up, and hostility and attention-deficit/hyperactivity disorder were the most significant predictors of Internet addiction in male and female adolescents, respectively.

Conclusions  These results suggest that attention-deficit/hyperactivity disorder, hostility, depression, and social phobia should be detected early on and intervention carried out to prevent Internet addiction in adolescents. Also, sex differences in psychiatric comorbidity should be taken into consideration when developing prevention and intervention strategies for Internet addiction.

Figures in this Article

The Internet has become one of the most important information resources for adolescents.1 However, addiction to the Internet can also have a negative impact on academic performance, family relationships, and emotional state in adolescents.2,3 This phenomenon has been described as Internet addiction or problematic Internet use4,5 and classified as a possible behavior addiction.6 Previous reports found that 1.4% to 17.9% of adolescents have Internet addiction in both Western and Eastern societies,711 and this high percentage led Block12 to argue that Internet and gaming addictions should be added to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. Identification of the risk factors for Internet addiction is therefore of clinical significance for the prevention of, and early intervention into, Internet addiction in adolescents.

There are currently no universally accepted diagnosis criteria for Internet addiction in adolescents. Several researchers have tried to construct definitions for Internet addiction4,5,13,14; however, these have not been developed for use in adolescents. Ko and colleagues15 proposed diagnostic criteria for adolescent Internet addiction based on empirical diagnostic interviews, in which preoccupation, uncontrolled impulses, usage more than intended, tolerance, withdrawal, impairment of control, excessive time and effort spent on the Internet, and impairment of decision-making ability were defined as core symptoms of Internet addiction. As these criteria proved to be of good diagnostic accuracy (95.4%),15 we use this diagnostic method to define Internet addiction in adolescents in the present study.

One of our research interests is the association between Internet addiction and psychiatric symptoms. Depression is the most frequently reported psychiatric symptom associated with Internet addiction among adolescents9,11,1618; however, these investigations were cross-sectional. In a longitudinal study, Internet use was found to increase the risk of depression in adults,19 but a later longitudinal study revealed a positive effect of Internet use on well-being.20 Recently, another longitudinal study found that compulsive Internet use increased the risk of depression in adolescents 6 months later.21 While these 3 studies focused on examining whether Internet use or compulsive Internet use results in depression, they did not examine whether depression is a risk factor for the development of Internet addiction.

Attention-deficit/hyperactivity disorder (ADHD) has been reported to be associated with Internet addiction in cross-sectional investigations among adolescents.11,16,22 Because ADHD is a persistent and pervasive mental disorder for which the age at onset is younger than 7 years,23 it has been suggested to be an important associated factor for Internet addiction11,24; however, to our knowledge, this hypothesis has not been proven by any prospective studies.

It has been reported that Internet use and Internet addiction are associated with social phobia,11,25,26 but a causal relationship has not been demonstrated. In addition, hostility has been reported to be associated with Internet addiction in adolescents,11,27 and further, a prospective study found that high levels of interpersonal sensitivity and hostility predicted the persistence of Internet addiction 1 year later.28 However, no psychiatric symptoms were found to predict the occurrence of Internet addiction,28 although the short follow-up might have contributed to this negative finding. Thus, a prospective study with a longer follow-up period might be necessary to examine the predictive values of psychiatric symptoms for the occurrence of Internet addiction.

A disparity has been found in the prevalence of Internet addiction in males and females.29,30 Differences in the association of Internet addiction with psychiatric symptoms have also been found: for example, hostility is associated with Internet addiction in male adolescents but not in females.11 The results of previous studies suggest that sex differences in the predictive power of psychiatric symptoms for the occurrence of Internet addiction should be addressed in further studies.

The aims of this 2-year prospective study were to (1) evaluate the predictive values of psychiatric symptoms for the occurrence of Internet addiction in adolescents and (2) determine the sex differences in the predictive value of psychiatric symptoms for the occurrence of Internet addiction in adolescents.

SAMPLE

The subjects of this study were randomly selected from students in the seventh grade at 10 junior high schools (4 in urban areas, 4 in suburban areas, and 2 in rural areas) in southern Taiwan in September 2005. All students from 8 randomly selected classes in each school were invited to participate in the investigation. Research assistants explained the goals and procedure of the study to the students in their classrooms. A total of 2293 adolescents (1179 boys and 1114 girls) agreed to participate in the prospective investigation and the other 77 refused the assessment. Informed consent was obtained from the adolescents before assessment. The mean (SD) age of the participants was 12.36 (0.55) years.

INSTRUMENTS
Chen Internet Addiction Scale

The Chen Internet Addiction Scale (CIAS) contains 26 items on a 4-point Likert scale with a scoring range of 26 to 104. The internal reliability of the scale and the subscales in the original study ranged from 0.79 to 0.93.31 According to the Ko diagnostic criteria of Internet addiction,15 a cutoff score of 64 has the highest diagnostic accuracy (87.6%).32 Accordingly, subjects with CIAS scores of 64 or more were classified as the Internet addiction group in this study.

Attention-Deficit/Hyperactivity Disorder Self-rated Scale

The Attention-Deficit/Hyperactivity Disorder Self-rated Scale (ADHDS) was designed for this research to measure ADHD symptoms. The 18 items in the ADHDS were modified from the Vanderbilt ADHD Diagnostic Parent Rating Scale33 and represent the 18 diagnostic symptoms for ADHD in the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision),23 taking the form of a 4-point Likert-type self-reported questionnaire, ranging from 1 (not at all) to 4 (almost always). The scores of items 1 to 9 and 10 to 18 were summed, giving the severity of symptoms for an attention-deficit score and a hyperactivity score, respectively; the attention-deficit score and hyperactivity score were then summed to give the ADHD score. Higher scores indicate more severe symptoms.11 In this study, those whose total ADHDS score was 1 SD higher than the mean of the whole study population were classified as the group with significant ADHD symptoms.

Center for Epidemiological Studies Depression Scale

The 20-item Mandarin Chinese version of the Center for Epidemiological Studies Depression Scale (CES-D)34 is a self-administered evaluation assessing participants' frequency of depressive symptoms over the previous week. The Cronbach α for the CES-D in the present study was 0.78. A previous study using the CES-D in a 2-phase survey for depressive disorders among nonreferred adolescents in Taiwan found that adolescents with total CES-D scores higher than 28 were more likely to have major depressive disorder with or without functional impairment.35 In this study, we defined those adolescents whose CES-D score was higher than 28 as having significant depression.

Brief Version of the Fear of Negative Evaluation Scale

The original Fear of Negative Evaluation Scale (FNE) was developed to evaluate the cognitive symptoms of social phobia.36 The Brief Version of the FNE (BV-FNE) is a brief, 5-point, Likert-type, 12-item version of the FNE that is highly correlated (r = 0.96) with the original scale and has a high level of internal consistency (α = 0.90) and a 4-week test-retest reliability coefficient (0.75).37 In this investigation, this scale was used to evaluate cognitive symptoms of social phobia. Those participants whose total BV-FNE score was 1 SD higher than the mean of the whole study population were classified as the group with significant social phobia symptoms.

The Buss-Durkee Hostility Inventory–Chinese Version–Short Form

The Buss-Durkee Hostility Inventory–Chinese Version–Short Form (BDHIC-SF) 20-item, 5-point Likert-type scale was used to assess 4 dimensions of the hostility construct: hostility cognition, hostility affection, expressive hostility behavior, and suppressive hostility behavior. The coefficient of internal consistency (Cronbach α) was 0.93 and the 4-week test-retest reliability was 0.80. Higher scores indicate higher hostility.38 Those participants whose total BDHIC-SF score was 1 SD higher than the mean of the whole study population were classified as the group with significant hostility.

STUDY PROCEDURE AND STATISTICAL ANALYSIS

The study was approved by the institutional review board of Kaohsiung Medical University Hospital. The participants completed the assessments for demographic data and Internet behavior, CIAS, ADHDS, CES-D, BV-FNE, and BDHIC-SF in the initial assessment. They were then invited to complete the CIAS 6, 12, and 24 months later (the second, third, and fourth assessments, respectively). To determine the risk factors for Internet addiction, data from participants who were classified as non–Internet-addicted in the initial assessment and who had completed at least 1 follow-up CIAS assessment were selected for statistical analysis. Among the 10 schools initially evaluated, 8, 10, and 9 schools participated in the second, third, and fourth assessments, respectively.

All statistical analyses were performed using the software package SPSS (SPSS Inc, Chicago, Illinois). Univariate Cox proportional hazard regression analysis with time-dependent variables was used to examine the predictive values of baseline psychiatric symptoms (depression, ADHD, social phobia, and hostility), Internet use behaviors (time spent on the Internet and the kinds of online activities participated in), and demographic characteristics (sex and age) for the occurrence of Internet addiction during the 2-year period of follow-up. The outcome variable (survival time) in Cox proportional hazard regression was defined as the period between the initial assessment and the detection of occurrence of Internet addiction during follow-up. If a participant was found to meet the criteria for Internet addiction in a follow-up assessment, an event was recorded and that case's data were not censored. On the other hand, if Internet addiction was not detected in any follow-up assessment, no event was recorded and the participant's data were censored. Thus, censored individuals were those who either had no Internet addiction by the end of the study or were lost to follow-up during the course of the study before Internet addiction had been identified.

We first examined the predictive values of categorical variables (sex and Internet use behaviors) for the occurrence of Internet addiction using univariate Cox proportional hazard regression. Then, the predictive values of age and the psychiatric symptoms group were also examined using the same statistical method for all, male, and female participants. All significant psychiatric predictors found in the univariate analysis were then further used in forward stepwise multivariate Cox proportional hazard regression to determine which psychiatric symptom was the most significant predictor of Internet addiction among all, male, and female participants. Because the aim of this study was to examine the psychiatric predictors of Internet addiction, the variables pertaining to Internet use behaviors were not selected into the multivariate analysis to prevent limiting the possible predictive value of psychiatric symptoms. A P value lower than .05 was considered significant.

A total of 2162 students completed all questionnaires at the first investigation. Of them, 233 participants (10.8%) were classified as having Internet addiction, and 1929 participants (89.2%) were classified as having no Internet addiction in the initial assessment. Among the 1929 participants (921 male, 1008 female) without Internet addiction, 1848 (95.8%) (882 male, 966 female) completed at least 1 follow-up assessment and their data were included in the statistical analysis. No sex difference (χ2 = 0.006; P = .94) was found between the enrolled participants and those for whom data were missing. The numbers of censored and noncensored cases in the 3 follow-up assessments are shown in Figure.

Place holder to copy figure label and caption
Figure.

Flowchart of the recruitment, assessment, and survival analysis processes. *Participants who did not participate in this investigation but were assessed in subsequent investigations. †Participants who were diagnosed with Internet addiction. ‡Participants who were lost to follow-up assessment.

Graphic Jump Location

The results of univariate Cox proportional hazard regression analysis are shown in Table 1 and Table 2 and indicate that being male, playing online games, and using the Internet every day and/or for more than 20 hours/wk were risk factors for Internet addiction (Table 1). Depression, ADHD, social phobia, and hostility were also found to predict the occurrence of Internet addiction among all participants and female participants (Table 2); among male participants, only ADHD and hostility were identified as risk factors for Internet addiction.

Table Graphic Jump LocationTable 1. Predictive Value of Sex and Internet-Using Behaviors for Internet Addiction in the Univariate Cox Proportional Hazard Regression
Table Graphic Jump LocationTable 2. Predictive Value of Age and Psychiatric Symptoms for Internet Addiction: Univariate Cox Proportional Hazard Regression

The significant psychiatric predictors were further selected into the forward stepwise multivariate Cox proportional hazard regression (Table 3). The results indicated that ADHD was the most significant predictor of the occurrence of Internet addiction among all participants after controlling for sex and age, followed by hostility. Further, hostility and ADHD significantly predicted the occurrence of Internet addiction among male and female participants, respectively.

Table Graphic Jump LocationTable 3. Predictive Value of Age and Psychiatric Symptoms for Internet Addiction: Multivariate Cox Proportional Hazard Regressiona

The results of this 2-year prospective study revealed that ADHD and hostility were the leading risk factors for the occurrence of Internet addiction, and depression and social phobia predicted Internet addiction among female, but not male, adolescents. In addition, further analysis revealed that hostility and ADHD were the most significant risk factors for Internet addiction among male and female adolescents, respectively.

In line with previous cross-sectional studies,11,16 we found that adolescents with significant ADHD symptoms were more likely to become addicted to the Internet. The biopsychosocial characteristics of adolescents with ADHD might explain this association. First, being easily bored and having an aversion to delayed reward have been identified as the 2 core symptoms of ADHD.39,40 Internet behavior is characterized by rapid response, immediate reward, and multiple windows with different activities, which may reduce feelings of boredom or delayed aversion in adolescents with ADHD. Second, Koepp and colleagues41 reported that striatal dopamine is released during video gaming, which may compensate for the dopamine deficit of adolescents with ADHD; thus, they may perform well in games and this may compensate for real-life frustration. Third, adolescents with ADHD have abnormal brain activities associated with impaired inhibition.42 A lack of self-control may cause them to experience difficulty in controlling Internet use, and hence, they become vulnerable to Internet addiction.

Having significant symptoms of ADHD was the most important predictive factor of Internet addiction among girls. One possible explanation for this is that peer tolerance to ADHD symptoms is lower in girls than in boys,43 which may lead girls with ADHD to experience more severe difficulties in peer relationships. Engaging in anonymous Internet-based activities might decrease discrimination and reduce some of the difficulties in social interaction caused by ADHD, an effect that might make female adolescents with ADHD more likely to use the Internet heavily and develop an addiction to the Internet if the primary problem is not resolved. Thus, ADHD should be effectively screened for and treated to prevent the emergence of Internet addiction, especially in girls.

As in previous cross-sectional studies,11,27 the results of this study indicated that male adolescents with significant hostility were more likely to develop Internet addiction than those without. Hostility was also reported to predict the persistence of Internet addiction in a previous prospective study.28 Many Internet activities, especially online gaming, provide a world in which hostility can be expressed and violence perpetrated without restriction, providing a space in which adolescents with significant hostility can express their aggression in ways that might be prohibited in the real world. Accordingly, more attention to and intervention into hostility should be provided through a preventive and treatment schedule for Internet addiction. In addition, previous reports have found that violence in games increases physical violence,44 and Internet addiction is associated with violent behavior45 among adolescents. Whether addiction to the Internet results in a vicious cycle of violence should therefore be addressed in a longitudinal study in the future.

Previous reports have suggested that women with alcohol use disorder are more likely to have a mood disorder, and depression was identified as the antecedent diagnosis for alcoholism among most women.46 In line with findings on alcohol use disorder, female adolescents with depression were identified as having a higher risk of Internet addiction in this study. As the Internet provides adolescents with social support,16,47 achievement,48 the pleasure of control,49 and a virtual world in which to escape from emotional difficulties in the real world, it appears reasonable that female adolescents with depression would be more likely to use the Internet to alleviate that depression. Kraut and colleagues20 proposed a “rich get richer” model in which the Internet provides more benefits to those who are already well-adjusted, and in contrast, poorly adjusted adolescents with depression may experience more deleterious effects with heavy Internet use and may be vulnerable to becoming addicted to the Internet.

Whether Internet communication benefits adolescents with social phobia is still under debate.50,51 However, the results of this study demonstrate that female adolescents with higher social phobia were more likely to develop Internet addiction. Because Internet use can provide social support,52 female adolescents with social phobia might benefit from avoiding the stress caused by face-to-face interaction; however, if their social difficulties in the real world do not improve, female adolescents with social phobia might receive social support predominantly from the Internet, and hence, the risk of becoming addicted to the Internet may increase.

Thus, screening and treatment for depression and social phobia are essential for the prevention of and intervention into Internet addiction among adolescents, especially girls. Only female adolescents were found to be vulnerable to the effects of depression and social phobia on Internet addiction, which may indicate that alleviating emotional difficulty is a more important mechanism underlying Internet addiction among female adolescents than among boys. However, this hypothesis should be investigated in further research into sex differences in the mechanism of Internet addiction.

The results of this study raise several important suggestions and implications for clinical practice. First, because ADHD, hostility, depression, and social phobia are predictors of the emergence of Internet addiction, albeit to different degrees in different sexes, effective screening and intervention for these psychiatric symptoms are necessary to prevent Internet addiction among adolescents. Second, because a strategy of integrated therapy for both disorders in dual diagnosis could provide more consistent treatment effects,53 it is important to evaluate and treat these psychiatric symptoms in adolescents with Internet addiction. Third, sex differences in psychiatric comorbidity should be taken into consideration when developing prevention and intervention strategies for Internet addiction.

This study had the following limitations. First, the classification of Internet addiction was based only on the results of a self-reported questionnaire. Second, ADHD symptoms were determined solely on self-reported data, which might make the results less objective. Some previous studies have suggested that parents may be more reliable than adolescents at detecting ADHD symptoms54; however, other research has reported that both parents and adolescents themselves are able to report ADHD symptoms distinctly.55 Third, 2 schools and 1 school did not participate in the second and fourth investigations, respectively, because their administrative departments were unable to provide adequate time and space for the assessment at the time of the investigation.

This 2-year prospective study revealed that ADHD and hostility are leading risk factors for Internet addiction among young adolescents, followed by depression and social phobia. In addition, hostility was identified as the most significant predictor of Internet addiction in male adolescents and ADHD was the most significant predictor for the same condition in female adolescents. These results suggest that ADHD, hostility, depression, and social phobia should be detected early and intervention carried out to prevent the occurrence of Internet addiction in adolescents.

Correspondence: Cheng-Fang Yen, MD, PhD, Department of Psychiatry, Kaohsiung Medical University Hospital, 100 Tzyou 1st Rd, Kaohsiung City, Taiwan 807 (chfaye@cc.kmu.edu.tw).

Accepted for Publication: March 5, 2009.

Author Contributions:Study concept and design: Ko and C.-F. Yen. Acquisition of data: J.-Y. Yen, Chen, Yeh, and C.-F. Yen. Analysis and interpretation of data: Ko and C.-F. Yen. Drafting of the manuscript: Ko and C.-F. Yen. Critical revision of the manuscript for important intellectual content: J.-Y. Yen, Chen, Yeh, and C.-F. Yen. Obtained funding: Ko. Administrative, technical, and material support: Ko, J.-Y. Yen, Chen, Yeh, and C.-F. Yen. Study supervision: C.-F. Yen.

Financial Disclosure: None reported.

Funding/Support: This study was supported by grants NSC 94-2413-H-037-006 and NSC 95-2413-H-037-008-SSS from the National Science Council of Taiwan.

Gray  NJKlein  JD Adolescents and the internet: health and sexuality information. Curr Opin Obstet Gynecol 2006;18 (5) 519- 524
PubMed Link to Article
Lin  SSJTsai  CC Sensation seeking and internet dependence of Taiwanese high school adolescents. Comput Human Behav 2002;18 (4) 411- 426
Link to Article
Young  KSRogers  RC The relationship between depression and Internet addiction. Cyberpsychol Behav 1998;1 (1) 25- 28
Link to Article
Young  KS Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav 1998;1 (3) 237- 244
Link to Article
Shapira  NALessig  MCGoldsmith  TD  et al.  Problematic internet use: proposed classification and diagnostic criteria. Depress Anxiety 2003;17 (4) 207- 216
PubMed Link to Article
Holden  C 'Behavioral' addictions: do they exist? Science 2001;294 (5544) 980- 982
PubMed Link to Article
Mythily  SQiu  SWinslow  M Prevalence and correlates of excessive Internet use among youth in Singapore. Ann Acad Med Singapore 2008;37 (1) 9- 14
PubMed
Kaltiala-Heino  RLintonen  TRimpelä  A Internet addiction? potentially problematic use of the Internet in a population of 12-18 year-old adolescents. Addict Res Theory 2004;12 (1) 89- 96
Link to Article
Jang  KSHwang  SYChoi  JY Internet addiction and psychiatric symptoms among Korean adolescents. J Sch Health 2008;78 (3) 165- 171
PubMed Link to Article
Cao  FSu  L Internet addiction among Chinese adolescents: prevalence and psychological features. Child Care Health Dev 2007;33 (3) 275- 281
PubMed Link to Article
Yen  JYKo  CHYen  CFWu  HYYang  MJ The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. J Adolesc Health 2007;41 (1) 93- 98
PubMed Link to Article
Block  JJ Issues for DSM-V: internet addiction. Am J Psychiatry 2008;165 (3) 306- 307
PubMed Link to Article
Anderson  KJ Internet use among college students: an exploratory study. J Am Coll Health 2001;50 (1) 21- 26
PubMed Link to Article
Morahan-Martin  JSchumacher  P Incidence and correlates of pathological internet use among college students. Comput Human Behav 2000;16 (1) 13- 29
Link to Article
Ko  CHYen  JYChen  CCChen  SHYen  CF Proposed diagnostic criteria of internet addiction for adolescents. J Nerv Ment Dis 2005;193 (11) 728- 733
PubMed Link to Article
Ha  JHYoo  HJCho  IHChin  BShin  DKim  JH Psychiatric comorbidity assessed in Korean children and adolescents who screen positive for Internet addiction. J Clin Psychiatry 2006;67 (5) 821- 826
PubMed Link to Article
Whang  LSLee  SChang  G Internet over-users' psychological profiles: a behavior sampling analysis on internet addiction. Cyberpsychol Behav 2003;6 (2) 143- 150
PubMed Link to Article
Kim  KRyu  EChon  MY  et al.  Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey. Int J Nurs Stud 2006;43 (2) 185- 192
PubMed Link to Article
Kraut  RPatterson  MLundmark  VKiesler  SMukopadhyay  TScherlis  W Internet paradox: a social technology that reduces social involvement and psychological well-being? Am Psychol 1998;53 (9) 1017- 1031
PubMed Link to Article
Kraut  RKiesler  SBoneva  BCummings  JHelgeson  VCrawford  A Internet paradox revisited. J Soc Issues 2002;58 (1) 49- 74
Link to Article
van den Eijnden  RJJMMeerkerk  GJVermulst  AASpijkerman  REngels  RC Online communication, compulsive internet use, and psychosocial well-being among adolescents: a longitudinal study. Dev Psychol 2008;44 (3) 655- 665
PubMed Link to Article
Yoo  HJCho  SCHa  J  et al.  Attention deficit hyperactivity symptoms and internet addiction. Psychiatry Clin Neurosci 2004;58 (5) 487- 494
PubMed Link to Article
American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders. 4th ed, text revision Washington, DC American Psychiatric Association2000;
Ko  CHYen  JYChen  CSChen  CCYen  CF Psychiatric comorbidity of internet addiction in college students: an interview study. CNS Spectr 2008;13 (2) 147- 153
PubMed
Lo  SKWang  CCFang  W Physical interpersonal relationships and social anxiety among online game players. Cyberpsychol Behav 2005;8 (1) 15- 20
PubMed Link to Article
Liu  CYKuo  FY A study of internet addiction through the lens of the interpersonal theory. Cyberpsychol Behav 2007;10 (6) 799- 804
PubMed Link to Article
Yen  JYKo  CHYen  CFChen  SHChung  WLChen  CC Psychiatric symptoms in adolescents with Internet addiction: comparison with substance use. Psychiatry Clin Neurosci 2008;62 (1) 9- 16
PubMed Link to Article
Ko  CHYen  JYYen  CFLin  HCYang  MJ Factors predictive for incidence and remission of internet addiction in young adolescents: a prospective study. Cyberpsychol Behav 2007;10 (4) 545- 551
PubMed Link to Article
Chou  CCondron  LBelland  JC A review of the research on Internet addiction. Educ Psychol Rev 2005;17 (4) 363- 388
Link to Article
Ko  CHYen  JYChen  CCChen  SHWu  KYen  CF Tridimensional personality of adolescents with internet addiction and substance use experience. Can J Psychiatry 2006;51 (14) 887- 894
PubMed
Chen  SHWeng  LCSu  YJWu  HMYang  PF Development of Chinese Internet addiction scale and its psychometric study. Chin J Psychol 2003;45 (3) 279- 294
Ko  CHYen  JYYen  CFChen  CCYen  CNChen  SH Screening for Internet addiction: an empirical study on cut-off points for the Chen Internet Addiction Scale. Kaohsiung J Med Sci 2005;21 (12) 545- 551
PubMed Link to Article
Wolraich  MLLambert  WDoffing  MABickman  LSimmons  TWorley  K Psychometric properties of the Vanderbilt ADHD Diagnostic Parent Rating Scale in a referred population. J Pediatr Psychol 2003;28 (8) 559- 567
PubMed Link to Article
Chien  CPCheng  TA Depression in Taiwan: epidemiological survey utilizing CES-D. Seishin Shinkeigaku Zasshi 1985;87 (5) 335- 338
PubMed
Yang  HJSoong  WTKuo  PHChang  HLChen  WJ Using the CES-D in a two-phase survey for depressive disorders among nonreferred adolescents in Taipei: a stratum-specific likelihood ratio analysis. J Affect Disord 2004;82 (3) 419- 430
PubMed
Watson  DFriend  R Measurement of social-evaluative anxiety. J Consult Clin Psychol 1969;33 (4) 448- 457
PubMed Link to Article
Leary  MR A brief version of the Fear of Negative Evaluation Scale. Pers Soc Psychol Bull 1983;9 (3) 371- 375
Link to Article
Lin  TKWeng  CYWang  WCChen  CCLin  IMLin  CL Hostility trait and vascular dilatory functions in healthy Taiwanese. J Behav Med 2008;31 (6) 517- 524
PubMed Link to Article
Diamond  A Attention-deficit disorder (attention-deficit/hyperactivity disorder without hyperactivity): a neurobiologically and behaviorally distinct disorder from attention-deficit/hyperactivity disorder (with hyperactivity). Dev Psychopathol 2005;17 (3) 807- 825
PubMed Link to Article
Castellanos  FXTannock  R Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes. Nat Rev Neurosci 2002;3 (8) 617- 628
PubMed
Koepp  MJGunn  RNLawrence  AD  et al.  Evidence for striatal dopamine release during a video game. Nature 1998;393 (6682) 266- 268
PubMed Link to Article
Rubia  KSmith  ABBrammer  MJToone  BTaylor  E Abnormal brain activation during inhibition and error detection in medication-naive adolescents with ADHD. Am J Psychiatry 2005;162 (6) 1067- 1075
PubMed Link to Article
Diamantopoulou  SHenricsson  LRydell  AM ADHD symptoms and peer relations of children in a community sample: examining associated problems, self-perceptions, and gender differences. Int J Behav Dev 2005;29 (5) 388- 398
Link to Article
Huesmann  LR The impact of electronic media violence: scientific theory and research. J Adolesc Health 2007;41 (6) ((suppl 1)) S6- S13
PubMed Link to Article
Ko  CHYen  JYLiu  SCHuang  CFYen  CF The associations between aggressive behaviors and Internet addiction and online activities in adolescents. J Adolesc Health 2009;44 (6) 598- 605
PubMed Link to Article
Kranzler  HRRosenthal  RN Dual diagnosis: alcoholism and co-morbid psychiatric disorders. Am J Addict 2003;12 ((suppl 1)) S26- S40
PubMed Link to Article
Tichon  JGShapiro  M The process of sharing social support in cyberspace. Cyberpsychol Behav 2003;6 (2) 161- 170
PubMed Link to Article
Suler  JR To get what you need: healthy and pathological Internet use. Cyberpsychol Behav 1999;5 (2) 385- 393
Link to Article
Leung  L Net-generation attributes and seductive properties of the internet as predictors of online activities and internet addiction. Cyberpsychol Behav 2004;7 (3) 333- 348
PubMed Link to Article
Morahan-Martin  J The relationship between loneliness and internet use and abuse. Cyberpsychol Behav 1999;2 (5) 431- 439
PubMed Link to Article
Shepherd  RMEdelmann  RJ Reasons for internet use and social anxiety. Pers Individ Dif 2005;39 (5) 949- 958
Link to Article
Wangberg  SCAndreassen  HKProkosch  HUSantana  SMSørensen  TChronaki  CE Relations between Internet use, socio-economic status (SES), social support and subjective health. Health Promot Int 2008;23 (1) 70- 77
PubMed Link to Article
Drake  RE Dual diagnosis. Psychiatry 2007;6 (9) 381- 384
Link to Article
Achenbach  TMMcConaughy  SHHowell  CT Child/adolescent behavioral and emotional problems: implications of cross-informant correlations for situational specificity. Psychol Bull 1987;101 (2) 213- 232
PubMed Link to Article
Cantwell  DPLewinsohn  PMRohde  PSeeley  JR Correspondence between adolescent report and parent report of psychiatric diagnostic data. J Am Acad Child Adolesc Psychiatry 1997;36 (5) 610- 619
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure.

Flowchart of the recruitment, assessment, and survival analysis processes. *Participants who did not participate in this investigation but were assessed in subsequent investigations. †Participants who were diagnosed with Internet addiction. ‡Participants who were lost to follow-up assessment.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Predictive Value of Sex and Internet-Using Behaviors for Internet Addiction in the Univariate Cox Proportional Hazard Regression
Table Graphic Jump LocationTable 2. Predictive Value of Age and Psychiatric Symptoms for Internet Addiction: Univariate Cox Proportional Hazard Regression
Table Graphic Jump LocationTable 3. Predictive Value of Age and Psychiatric Symptoms for Internet Addiction: Multivariate Cox Proportional Hazard Regressiona

References

Gray  NJKlein  JD Adolescents and the internet: health and sexuality information. Curr Opin Obstet Gynecol 2006;18 (5) 519- 524
PubMed Link to Article
Lin  SSJTsai  CC Sensation seeking and internet dependence of Taiwanese high school adolescents. Comput Human Behav 2002;18 (4) 411- 426
Link to Article
Young  KSRogers  RC The relationship between depression and Internet addiction. Cyberpsychol Behav 1998;1 (1) 25- 28
Link to Article
Young  KS Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav 1998;1 (3) 237- 244
Link to Article
Shapira  NALessig  MCGoldsmith  TD  et al.  Problematic internet use: proposed classification and diagnostic criteria. Depress Anxiety 2003;17 (4) 207- 216
PubMed Link to Article
Holden  C 'Behavioral' addictions: do they exist? Science 2001;294 (5544) 980- 982
PubMed Link to Article
Mythily  SQiu  SWinslow  M Prevalence and correlates of excessive Internet use among youth in Singapore. Ann Acad Med Singapore 2008;37 (1) 9- 14
PubMed
Kaltiala-Heino  RLintonen  TRimpelä  A Internet addiction? potentially problematic use of the Internet in a population of 12-18 year-old adolescents. Addict Res Theory 2004;12 (1) 89- 96
Link to Article
Jang  KSHwang  SYChoi  JY Internet addiction and psychiatric symptoms among Korean adolescents. J Sch Health 2008;78 (3) 165- 171
PubMed Link to Article
Cao  FSu  L Internet addiction among Chinese adolescents: prevalence and psychological features. Child Care Health Dev 2007;33 (3) 275- 281
PubMed Link to Article
Yen  JYKo  CHYen  CFWu  HYYang  MJ The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. J Adolesc Health 2007;41 (1) 93- 98
PubMed Link to Article
Block  JJ Issues for DSM-V: internet addiction. Am J Psychiatry 2008;165 (3) 306- 307
PubMed Link to Article
Anderson  KJ Internet use among college students: an exploratory study. J Am Coll Health 2001;50 (1) 21- 26
PubMed Link to Article
Morahan-Martin  JSchumacher  P Incidence and correlates of pathological internet use among college students. Comput Human Behav 2000;16 (1) 13- 29
Link to Article
Ko  CHYen  JYChen  CCChen  SHYen  CF Proposed diagnostic criteria of internet addiction for adolescents. J Nerv Ment Dis 2005;193 (11) 728- 733
PubMed Link to Article
Ha  JHYoo  HJCho  IHChin  BShin  DKim  JH Psychiatric comorbidity assessed in Korean children and adolescents who screen positive for Internet addiction. J Clin Psychiatry 2006;67 (5) 821- 826
PubMed Link to Article
Whang  LSLee  SChang  G Internet over-users' psychological profiles: a behavior sampling analysis on internet addiction. Cyberpsychol Behav 2003;6 (2) 143- 150
PubMed Link to Article
Kim  KRyu  EChon  MY  et al.  Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey. Int J Nurs Stud 2006;43 (2) 185- 192
PubMed Link to Article
Kraut  RPatterson  MLundmark  VKiesler  SMukopadhyay  TScherlis  W Internet paradox: a social technology that reduces social involvement and psychological well-being? Am Psychol 1998;53 (9) 1017- 1031
PubMed Link to Article
Kraut  RKiesler  SBoneva  BCummings  JHelgeson  VCrawford  A Internet paradox revisited. J Soc Issues 2002;58 (1) 49- 74
Link to Article
van den Eijnden  RJJMMeerkerk  GJVermulst  AASpijkerman  REngels  RC Online communication, compulsive internet use, and psychosocial well-being among adolescents: a longitudinal study. Dev Psychol 2008;44 (3) 655- 665
PubMed Link to Article
Yoo  HJCho  SCHa  J  et al.  Attention deficit hyperactivity symptoms and internet addiction. Psychiatry Clin Neurosci 2004;58 (5) 487- 494
PubMed Link to Article
American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders. 4th ed, text revision Washington, DC American Psychiatric Association2000;
Ko  CHYen  JYChen  CSChen  CCYen  CF Psychiatric comorbidity of internet addiction in college students: an interview study. CNS Spectr 2008;13 (2) 147- 153
PubMed
Lo  SKWang  CCFang  W Physical interpersonal relationships and social anxiety among online game players. Cyberpsychol Behav 2005;8 (1) 15- 20
PubMed Link to Article
Liu  CYKuo  FY A study of internet addiction through the lens of the interpersonal theory. Cyberpsychol Behav 2007;10 (6) 799- 804
PubMed Link to Article
Yen  JYKo  CHYen  CFChen  SHChung  WLChen  CC Psychiatric symptoms in adolescents with Internet addiction: comparison with substance use. Psychiatry Clin Neurosci 2008;62 (1) 9- 16
PubMed Link to Article
Ko  CHYen  JYYen  CFLin  HCYang  MJ Factors predictive for incidence and remission of internet addiction in young adolescents: a prospective study. Cyberpsychol Behav 2007;10 (4) 545- 551
PubMed Link to Article
Chou  CCondron  LBelland  JC A review of the research on Internet addiction. Educ Psychol Rev 2005;17 (4) 363- 388
Link to Article
Ko  CHYen  JYChen  CCChen  SHWu  KYen  CF Tridimensional personality of adolescents with internet addiction and substance use experience. Can J Psychiatry 2006;51 (14) 887- 894
PubMed
Chen  SHWeng  LCSu  YJWu  HMYang  PF Development of Chinese Internet addiction scale and its psychometric study. Chin J Psychol 2003;45 (3) 279- 294
Ko  CHYen  JYYen  CFChen  CCYen  CNChen  SH Screening for Internet addiction: an empirical study on cut-off points for the Chen Internet Addiction Scale. Kaohsiung J Med Sci 2005;21 (12) 545- 551
PubMed Link to Article
Wolraich  MLLambert  WDoffing  MABickman  LSimmons  TWorley  K Psychometric properties of the Vanderbilt ADHD Diagnostic Parent Rating Scale in a referred population. J Pediatr Psychol 2003;28 (8) 559- 567
PubMed Link to Article
Chien  CPCheng  TA Depression in Taiwan: epidemiological survey utilizing CES-D. Seishin Shinkeigaku Zasshi 1985;87 (5) 335- 338
PubMed
Yang  HJSoong  WTKuo  PHChang  HLChen  WJ Using the CES-D in a two-phase survey for depressive disorders among nonreferred adolescents in Taipei: a stratum-specific likelihood ratio analysis. J Affect Disord 2004;82 (3) 419- 430
PubMed
Watson  DFriend  R Measurement of social-evaluative anxiety. J Consult Clin Psychol 1969;33 (4) 448- 457
PubMed Link to Article
Leary  MR A brief version of the Fear of Negative Evaluation Scale. Pers Soc Psychol Bull 1983;9 (3) 371- 375
Link to Article
Lin  TKWeng  CYWang  WCChen  CCLin  IMLin  CL Hostility trait and vascular dilatory functions in healthy Taiwanese. J Behav Med 2008;31 (6) 517- 524
PubMed Link to Article
Diamond  A Attention-deficit disorder (attention-deficit/hyperactivity disorder without hyperactivity): a neurobiologically and behaviorally distinct disorder from attention-deficit/hyperactivity disorder (with hyperactivity). Dev Psychopathol 2005;17 (3) 807- 825
PubMed Link to Article
Castellanos  FXTannock  R Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes. Nat Rev Neurosci 2002;3 (8) 617- 628
PubMed
Koepp  MJGunn  RNLawrence  AD  et al.  Evidence for striatal dopamine release during a video game. Nature 1998;393 (6682) 266- 268
PubMed Link to Article
Rubia  KSmith  ABBrammer  MJToone  BTaylor  E Abnormal brain activation during inhibition and error detection in medication-naive adolescents with ADHD. Am J Psychiatry 2005;162 (6) 1067- 1075
PubMed Link to Article
Diamantopoulou  SHenricsson  LRydell  AM ADHD symptoms and peer relations of children in a community sample: examining associated problems, self-perceptions, and gender differences. Int J Behav Dev 2005;29 (5) 388- 398
Link to Article
Huesmann  LR The impact of electronic media violence: scientific theory and research. J Adolesc Health 2007;41 (6) ((suppl 1)) S6- S13
PubMed Link to Article
Ko  CHYen  JYLiu  SCHuang  CFYen  CF The associations between aggressive behaviors and Internet addiction and online activities in adolescents. J Adolesc Health 2009;44 (6) 598- 605
PubMed Link to Article
Kranzler  HRRosenthal  RN Dual diagnosis: alcoholism and co-morbid psychiatric disorders. Am J Addict 2003;12 ((suppl 1)) S26- S40
PubMed Link to Article
Tichon  JGShapiro  M The process of sharing social support in cyberspace. Cyberpsychol Behav 2003;6 (2) 161- 170
PubMed Link to Article
Suler  JR To get what you need: healthy and pathological Internet use. Cyberpsychol Behav 1999;5 (2) 385- 393
Link to Article
Leung  L Net-generation attributes and seductive properties of the internet as predictors of online activities and internet addiction. Cyberpsychol Behav 2004;7 (3) 333- 348
PubMed Link to Article
Morahan-Martin  J The relationship between loneliness and internet use and abuse. Cyberpsychol Behav 1999;2 (5) 431- 439
PubMed Link to Article
Shepherd  RMEdelmann  RJ Reasons for internet use and social anxiety. Pers Individ Dif 2005;39 (5) 949- 958
Link to Article
Wangberg  SCAndreassen  HKProkosch  HUSantana  SMSørensen  TChronaki  CE Relations between Internet use, socio-economic status (SES), social support and subjective health. Health Promot Int 2008;23 (1) 70- 77
PubMed Link to Article
Drake  RE Dual diagnosis. Psychiatry 2007;6 (9) 381- 384
Link to Article
Achenbach  TMMcConaughy  SHHowell  CT Child/adolescent behavioral and emotional problems: implications of cross-informant correlations for situational specificity. Psychol Bull 1987;101 (2) 213- 232
PubMed Link to Article
Cantwell  DPLewinsohn  PMRohde  PSeeley  JR Correspondence between adolescent report and parent report of psychiatric diagnostic data. J Am Acad Child Adolesc Psychiatry 1997;36 (5) 610- 619
PubMed Link to Article

Correspondence

CME
Also Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
Please click the checkbox indicating that you have read the full article in order to submit your answers.
Your answers have been saved for later.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
Submit a Comment

Multimedia

Supplemental Content
Supplemental Content

Some tools below are only available to our subscribers or users with an online account.

Web of Science® Times Cited: 68

Related Content

Customize your page view by dragging & repositioning the boxes below.

See Also...
Articles Related By Topic
Related Collections