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Article |

Effect of Pathological Use of the Internet on Adolescent Mental Health:  A Prospective Study FREE

Lawrence T. Lam, PhD; Zi-Wen Peng, MSc
[+] Author Affiliations

Author Affiliations: School of Medicine, Sydney, and the University of Notre Dame, Fremantle, Australia (Dr Lam); and Department of Psychological Education of Elementary School and Secondary School, Ministry of Education, and the School of Public Health, SunYat-Sen University, Guangzhou, Guangdong Province, China (Mr Peng).


Arch Pediatr Adolesc Med. 2010;164(10):901-906. doi:10.1001/archpediatrics.2010.159.
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Published online

Objective  To examine the effect of pathological use of the Internet on the mental health, including anxiety and depression, of adolescents in China. It is hypothesized that pathological use of the Internet is detrimental to adolescents' mental health.

Design  A prospective study with a randomly generated cohort from the population.

Setting  High schools in Guangzhou, China.

Participants  Adolescents aged between 13 and 18 years.

Main Exposure  Pathological use of the Internet was assessed using the Pathological Use of the Internet Test.

Outcome Measures  Depression and anxiety were assessed by the Zung Depression and Anxiety Scales.

Results  After adjusting for potential confounding factors, the relative risk of depression for those who used the Internet pathologically was about 2½ times (incidence rate ratio, 2.5; 95% confidence interval, 1.3-4.3) that of those who did not exhibit the targeted pathological internet use behaviors. No significant relationship between pathological use of the Internet and anxiety at follow-up was observed.

Conclusions  Results suggested that young people who are initially free of mental health problems but use the Internet pathologically could develop depression as a consequence. These results have direct implications for the prevention of mental illness in young people, particularly in developing countries.

Pathological use of the Internet has been suggested as a problematic behavior that exhibits similar signs and symptoms to other established addictions since the mid-1990s.1 While studies have indicated that individuals who pathologically use the Internet are mostly young men with introverted personalities, it has also been shown that the rates of exhibiting the behaviors among girls is increasing.24 In recent years, with the greater availability of the Internet in most Asian countries, pathological use of the Internet has become an increasing mental health issue among adolescents. Growing prevalence in adolescence has been reported by researchers in Taiwan and China to have increased from about 6% in 2000 to about 11% in 2004.5,6

Pathological use of the Internet has been suggested to be associated with interpersonal and intrapersonal relationships, other mental health problems, and physical ill health.710 Studies have described potential relationships between psychiatric symptoms, aggressive behaviors, depression, and pathological Internet use among adolescents.1114 In the prospective study by Ko et al,15 it was further reported that depression and social phobia are found to be predictive of pathological use of the Internet in a 2-year follow-up. These results suggest that depression and anxiety may be important factors in the causal pathway of the pathological use of the Internet among adolescents.

While there is a growing wealth of literature on pathological use of the Internet among adolescents, the shortcoming of most of these studies is that they are cross-sectional by nature. Owing to the fact that the strength of evidence provided by a study with cross-sectional design is insufficient to draw any causal inference, these studies can be considered exploratory for identifying potential relationships between exposure and outcome variables.8 Furthermore, the focus of these studies is pathological use of the Internet as the outcome. Information on the medium to long-term mental health effect of pathological use of the Internet among adolescents is scarce. As mentioned earlier, depression and anxiety may play a role in the development of pathological use of the Internet. However, the association between pathological use of the Internet and other mental health problems may instead indicate that using the Internet pathologically has an effect on the mental health of young people. Furthermore, these 2 factors may also share a common pathway that leads to the Internet behaviors as well as mental health problems. The limited information from the literature suggests a potential pathway that starts with mental health problems and finishes at Internet behaviors. However, no studies so far have explored the alternative direction of the pathway that begins with pathological Internet use. To determine the effect of pathological use of the Internet on the mental health of adolescents, an appropriate study type would be a cohort study with a “noncase” population. In other words, to follow a cohort of young people who are free of depression and anxiety but with various levels of Internet use and to determine their mental health outcomes at the end of the follow-up period.

To bridge the knowledge gap, this prospective study aims to examine the effect of the pathological use of the Internet on adolescent mental health, including anxiety and depression, using a noncase population. It is hypothesized that pathological use of the Internet is detrimental to the mental health of adolescents such that young people who use the Internet extensively and pathologically would have an increased risk of anxiety and depression.

SETTING AND ETHICS APPROVAL

This prospective cohort study was conducted in Guangzhou of the Guangdong Province in Southeast China in July 2008. Guangdong Province is the most populous province in China, and Guangzhou is the capital. It is the biggest and most populated city of the province, with an estimated population of nearly 10 million in 2006. Institute ethics approval for the study was granted by the Department of Psychological Education of Elementary and Secondary Schools of the Province Administration.

SAMPLING AND PROCEDURES

The methodologies of the baseline phase of the study were described previously.8 In brief, the sample was generated from the total student population of adolescents who attended high school within the region and were registered with the Guangzhou secondary school registry. A stratified random sampling method with stratification according to the proportion of students in metropolitan and rural areas was used for sample generation. The sample consisted of adolescents aged between 13 and 18 years.

The cohort study was conducted on campus at different schools, with baseline data collected via a health survey carried out the same week. Participants were selected randomly from the citywide student registry. Information on the study was provided to selected students and their parents via school principals and their teachers. While there was no written consent signed by parents, students younger than 16 years were instructed to obtain verbal consent from parents before filling in the self-reported questionnaire designed specifically for the study. For students older than 16 years (age of self-consent), consent was implicated by a voluntary response to the questionnaire. The cohort was then followed up for 9 months, with the survey conducted again on main mental health outcomes at the end of follow-up. For the present study, a “noncase” cohort was generated from the larger cohort with a screening for anxiety and depression at baseline.

MEASURES

Anxiety was measured using the Zung Self-rating Anxiety Scale,16 and depression was assessed using the Zung Self-rating Depression Scale17 at baseline as well as at follow-up. The Self-rating Anxiety Scale was a fully validated instrument designed to assess anxiety disorders.18 It consists of 20 questions on affect according to clinical symptoms of anxiety. An exemplary question is, “I feel afraid for no reason at all.” Respondents were asked to answer these questions on how often they experienced these signs and symptoms in the last 3 months and rated on a Likert scale with 1 indicating a little of the time to 4, most of the time. Scores from 1 to 4 were assigned to these responses, with a total raw score ranging from 20 to 80. These scores were further categorized into 4 levels of anxiety severity: normal, less than 45; mild to moderate, 45 to 59; marked to severe, 60-74; and extreme, 75 or greater, according to the recommended cutoff.16 The Self-rating Depression Scale was a validated, standardized scale for assessing depression. Participants were asked to respond to 20 questions regarding how often they experienced certain conditions or were in certain states of mind in the last 3 months at the time of the survey. For example, one question asked the respondent to rate how often “I find it easy to do the things I used to do” on a Likert scale with 4 responses including little or none of the time, some of the time, a large part of the time, and most or all of the time. Similarly to the Self-rating Anxiety Scale, scores from 1 to 4 were assigned to these responses with a total raw score ranging from 20 to 80. These scores were further categorized into 4 levels of depression severity: normal, less than 50; mild depression, 50 to 59; moderate to marked major depression, 60 to 69; and severe or extreme major depression, 70 or greater, according to the recommended cutoff.17 The outcome measure was further dichotomized into normal, less than 50, and depressed, 50 or greater, for ease of analysis. The Chinese versions of both instruments were validated in a Chinese adolescent population with good validity and reliability.19

Pathological use of the Internet was assessed by the Internet Addiction Test, also known as the Young's Internet Addiction Scale, designed by Young.20 The Internet Addiction Test is a 20-item self-reported scale, and the design was based on the concepts and behaviors exhibited by pathological gamblers as definite by the DSM-IV diagnostic criteria. It includes questions that reflect typical behaviors of addiction. An example question is, “How often do you feel depressed, moody, or nervous when you are off-line, which goes away once you are back on-line?” Respondents were asked to indicate the propensity of their responses on a Likert scale ranging from 1, rarely, to 5, always. A study on the psychometric properties of the Internet Addiction Test suggested good reliability, with Cronbach α values ranging from .82 to .54 for various factors.21 Total scores were calculated, with possible scores ranging from a minimum of 20 to a maximum of 100. The severity of addiction was then classified according to the suggested cutoff scores, with 20 to 49 points as normal; 50 to 79, moderate; and 80 to 100, severe.20 As there were only 10 students who scored 80 points or higher in this study; the exposure variable was dichotomized into 2 categories, severe/moderate and normal, for ease of data analysis.

Other information collected in the survey included demographics, metropolitan or rural schools, location of family residence, whether the respondent was a single child, parental education levels, health condition, and behaviors including drinking, smoking, physical activity, and sleeping hours. Information on respondents' perceptions of family financial situation, parental expectations, burden of study, disruption to daily life, family satisfaction, and recent stressful life events was also collected. As mentioned, these variables were known to be associated with anxiety and depression among adolescents.

STATISTICAL ANALYSIS

Data were analyzed using the Stata V10.0 statistical software program.22 Bivariate analyses were conducted to examine unadjusted relationships between pathological use of the Internet, all variables of interest and anxiety, and depression. Because this was a prospective cohort study, the unadjusted Incidence Rate Ratios (IRR) and their corresponding 95% confidence intervals (CI) for anxiety, depression, pathological use of the Internet, and all variables of interest were estimated. For binary variables, the IRRs and their corresponding 95% CIs were calculated directly using the cs procedures of the program. For variables with more than 2 categories, Poisson regression with robust variance was used to calculate the IRRs according to the suggestion by Barros and Hirakata on rate calculation for binary outcomes.23 Selection of potential confounding variables to be included in the multiple regression analyses was based on the significance level of these variables in the bivariate analyses. Variables that attained a significance level of P < .1 were included in further analysis for the adjusted relationship between the exposure and outcome variables. Poisson regression with robust variance was also used to calculate the adjusted IRRs of anxiety and depression with adjustment for potential confounding factors.

A total of 1618 students provided usable information on the baseline survey. Of these 1618 respondents, screening results at baseline indicated that 1122 were below the cutoff for both the Self-rating Anxiety Scale and Self-rating Depression Scale. Of the 1122 students, 1041 also responded to the follow-up questionnaire. This represented a follow-up rate of 92.8%. Comparisons between the respondents and nonrespondents indicated no statistically significant differences in terms of age, sex, and whether they attended city or rural schools. The characteristics and outcome measures of the respondents are summarized in Table 1. The sample consisted mainly of adolescents aged between 13 and 16 years (n = 881; 84.7%) with a mean (SD) age of 15.0 (1.8) years. There was an almost even distribution between boys and girls and between urban and nonurban schools. In terms of demographics, most families resided in the city (n = 761; 73.1%) and slightly more than a half were the only child in the family (n = 623; 60.0%). Most of their parents attained at least a level of secondary education with about 17% of fathers and 12% of mothers receiving postsecondary education levels including university and postgraduate education.

Table Graphic Jump LocationTable 1. Frequency Distribution of Anxiety and Depression At Follow-up, and Pathological Use of the Internet Status, Demographics, Health Behaviors, and Perception of Personal Conditions of Adolescents At Baseline

In terms of health conditions and behaviors, only 21 students (2.0%) reported having experienced serious illness in the past. Most (n = 683; 65.7%) had 6 to 8 hours of sleep on a normal weekday, and a quarter (n = 265; 25.7%) were involved in regular physical activity each week. A few students reported that they had either tried or were smoking currently on the baseline survey (n = 15; 2.1%), and 8% (n = 83) reported that they had consumed alcohol more than twice at the time of the survey. Most of the students perceived their family financial situation as about the same as others (n = 669; 64.4%). Slightly more than half perceived that they were heavily or very heavily burdened by their studies (n = 546; 52.6%), and most (n = 846; 81.5%) perceived that their parents had high and very high expectations of them. Slightly less than one-fifth of these students were satisfied with their family (n = 230; 22.1%), and about half (n = 536; 51.7%) perceived their body as normal, with about 20% (n = 214) feeling overweight and about 30% (n = 286) underweight.

In terms of the exposure, namely pathological use of the Internet, most respondents were classified as normal users (n = 944, 93.6%), with 62 (6.2%) moderate and 2 (0.2%) severely at risk. The most common use of the Internet was for entertainment (n = 448; 45.5%), followed by searching for information and knowledge (n = 276; 28.1%) and communication with school mates, making friends, and avoiding boredom (n = 260; 26.4%). There was a significant association between how the Internet was used and pathological use at baseline (χ22 = 21.78; P < .001). Young people who used the Internet pathologically were more likely to use it for entertainment and less likely to use it for information. At the 9-month follow-up, 8 students (0.2%) were classified as having significant anxiety symptoms and 87 (8.4%) scored higher than the cutoff of 50 on the depression scale.

The bivariate relationships between pathological use of the Internet, other variables of interest, depression, and anxiety were examined. The results were summarized in Table 2. As shown, pathological use of the Internet was significantly associated with depression, unadjusted for other potential confounding factors. Results suggested that students who used the Internet pathologically at baseline were more than 2 times as likely to experience depression at the 9-month follow up (IRR, 2.3; 95% CI, 1.2-4.1) compared with those who did not exhibit the targeted pathological behaviors. The results suggested that there was no significant effect of pathological use of the Internet on anxiety at follow-up (IRR, 2.0; 95% CI, 0.3-12.7). In this sample, study burden was the only potential confounding variable found to be significantly associated with a higher risk of anxiety and depression bivariately. Hence, it was included in further Poisson regression analyses to be adjusted for its effects on the relationships between Internet use and depression as well as anxiety. Other potential confounding variables suggested in the literature to be associated with depression and anxiety were also considered. These included age, sex, rural or urban residence, involvement in physical activity, family dissatisfaction, and study burden.

Table Graphic Jump LocationTable 2. Unadjusted Rate Ratios of Anxiety and Depression At Follow-up for Pathological Use of the Internet, Demographics, Health Behaviors, and Perception of Personal Conditions of Adolescents

The results obtained from the multivariate Poisson regression analyses were also presented in Table 3. These results indicated that pathological use of the Internet was still significantly associated with depression but not anxiety. After adjusting for potential confounding factors, the relative risk for depression for those who used the Internet pathologically was 2½ times (IRR, 2.5; 95% CI, 1.3-4.3) that of the group who did not. No significant relationship between pathological use of the Internet and anxiety at follow-up was observed.

Table Graphic Jump LocationTable 3. Adjusted Rate Ratios of Anxiety and Depression for Pathological Use of the Internet Among Adolescents

This study aimed to examine the effect of pathological or addictive use of the Internet on the mental health, including anxiety and depression, in a population of young people in Southeast China. The results suggested that pathological use of the Internet is detrimental to the mental health of these individuals. Particularly, pathological use of the Internet at baseline is predictive of depression at the 9-month follow-up. After adjusting for potential confounding factors, there was an increased risk of depression for those who used the Internet pathologically by 1½ times compared with those who did not exhibit the targeted pathological behaviors. This result suggests that young people who are initially free of mental health problems but use the Internet pathologically could develop depression as a consequence. However, such a relationship was not demonstrated for anxiety. This study is unique in terms of its ability to demonstrate the mental health sequelae of pathological use of the Internet for young people who were initially healthy to begin with.

Owing to the lack of a similar study on the medium to long-term effect of pathological use of the Internet on adolescent mental health, comparison of results obtained from this study with others reported in the literature would be difficult. However, the results are consistent with those obtained in the general literature of pathological use of the Internet and psychiatric symptomatology in adolescence.4,11,13,24 Results of this study demonstrate not only a correlation between pathological use of the Internet and depression but also a direct effect of the pathological use of the Internet on the mental health of young people. Considering the results obtained in previous studies, particularly Ko et al,15 as well as the argument presented in the “Introduction,” one can further hypothesize that the relationship between pathological Internet use and mental health may not necessarily be linear. It could be possible to apply a recursive model to understand the effect of pathological Internet use on the mental health of young people and its consequently greater involvement in pathological behaviors, triggering a vicious cycle that may spiral downward.

The results obtained from this study directly implicate the prevention of mental illness among young people, particularly in developing countries such as China. The results of the study indicated that young people who use the Internet pathologically are most at risk for mental problems and would develop depression if they continued the behavior. As we understand that mental health problems among adolescents bear a significant personal costs as well as costs to the community, early intervention and prevention that targets at-risk groups with identified risk factors is effective in reducing the burden of depression among young people.25 Screening for at-risk individuals in the school setting could be considered an effective early prevention strategy according to recent meta-analysis.26 Hence, a screening program for pathological use of the Internet could also be considered in all high schools to identify individuals at risk for early counseling and treatment.

As in all studies, there are strengths and weaknesses in this study. This is a population-based study that includes a random sample of students. No significant differences have been found between respondents and nonrespondents, suggesting a representative sample. The use of a standardized and validated assessment instrument for the outcome measure minimized some measurement biases. Moreover, because this is a cohort study, results provide further information on the effect of pathological use of the Internet on adolescent mental health, particularly depression, not just an association between the two. This study has demonstrated a chronological sequence between pathological use of the Internet and depression in a sample of healthy adolescents. Some potential limitations have also been identified in this study. First, information on outcome is obtained via a self-reported questionnaire. Hence, this constitutes a report bias in the outcome variable, although it would most likely be nondifferential bias. Second, information on the exposure variable is also collected via self-reporting and is also subject to recall or report bias. Third, not all potential confounding factors were measured and adjusted for in the analysis. Factors such as genetic variations and history of familial depression were not assessed in this study.

Correspondence: Lawrence T. Lam, School of Medicine Sydney, University of Notre Dame Australia, Darlinghurst Campus, 160 Oxford St, Darlinghurst, New South Wales, Australia 2010 (llam@nd.edu.au).

Accepted for Publication: March 17, 2010.

Published Online: August 2, 2010. doi:10.1001/archpediatrics.2010.159

Author Contributions:Study concept and design: Lam. Acquisition of data: Peng. Analysis and interpretation of data: Lam. Drafting of the manuscript: Lam and Peng. Critical revision of the manuscript for important intellectual content: Lam. Statistical analysis: Lam. Administrative, technical, and material support: Peng.

Financial Disclosure: None reported.

OReilly  M Internet addiction: a new disorder enters the medical lexicon. CMAJ 1996;154 (12) 1882- 1883
PubMed
Young  KS Psychology of computer use XL: addictive use of the Internet: a case that breaks the stereotype. Psychol Rep 1996;79 (3 pt 1) 899- 902
PubMed
Scherer  K College-life online: healthy and unhealthy Internet use. J Coll Student Dev 1997;38 (6) 655- 665
Young  KS Caught in the Net.  New York, NY John Wiley & Sons1998;
Chou  CHsiao  MC Internet addiction, usage, gratification, and pleasure experience: the Taiwan college students' case. Comput Educ 2000;35 (1) 65- 8010.1016/S0360-1315(00)00019-1
Wu  HRZhu  KJ Path analysis on related factors causing pathological use of the Internet disorder in college students [in Chinese]. Chin J Publ Health 2004;201363- 1364
Liu  TPotenza  MN Problematic Internet use: clinical implications. CNS Spectr 2007;12 (6) 453- 466
PubMed
Lam  LTPeng  ZMai  JJing  J The association between internet addiction and self-injurious behaviour among adolescents. Inj Prev 2009;15 (6) 403- 408
PubMed
Seo  MKang  HSYom  YHSeo  MKang  HSYom  YH Internet addiction and interpersonal problems in Korean adolescents. Comput Inform Nurs 2009;27 (4) 226- 233
PubMed
Kwon  JHChung  CSLee  J The effects of escape from self and interpersonal relationship on the pathological use of Internet games [published online August 23, 2009]. Community Ment Health J 2009;
PubMed10.1007/s10597-009-9236-1
Jang  KSHwang  SYChoi  JY Internet addiction and psychiatric symptoms among Korean adolescents. J Sch Health 2008;78 (3) 165- 171
PubMed
Morrison  CMGore  H The relationship between excessive Internet use and depression: a questionnaire-based study of 1,319 young people and adults. Psychopathology 2010;43 (2) 121- 126
PubMed
Ha  JHKim  SYBae  SC  et al.  Depression and Internet addiction in adolescents. Psychopathology 2007;40 (6) 424- 430
PubMed
Ko  CHYen  JYLiu  SCHuang  CFYen  CF The associations between aggressive behaviors and internet addiction and online activities in adolescents [published online ahead of print February 24, 2009]. J Adolesc Health 2009;44 (6) 598- 605
PubMed
Ko  CHYen  JYChen  CSYeh  YCYen  CF Predictive values of psychiatric symptoms for internet addiction in adolescents: a 2-year prospective study. Arch Pediatr Adolesc Med 2009;163 (10) 937- 943
PubMed
Zung  WW A rating instrument for anxiety disorders. Psychosomatics 1971;12 (6) 371- 379
PubMed
Zung  WW A self-rating depression scale. Arch Gen Psychiatry 1965;1263- 70
PubMed
Jedege  RO Psychometric attributes of the self-rating anxiety scale. Psychol Rep 1977;40 (1) 303- 306
PubMed
Lee  HCChiu  HFWing  YKLeung  CMKwong  PKChung  DW The Zung Self-rating Depression Scale: screening for depression among the Hong Kong Chinese elderly. J Geriatr Psychiatry Neurol 1994;7 (4) 216- 220
PubMed
Young  KS The Internet Addiction Test. Center for On-Line Addictions Web site. http://www.netaddiction.com/index.php?option=com_bfquiz&view=onepage&catid=46&Itemid=106. Accessed January 18, 2010
Widyanto  L McMurran  M The psychometric properties of the internet addiction test. Cyberpsychol Behav 2004;7 (4) 443- 450
PubMed
StataCorp, Stata Statistical Software: Release 10.0.  College Station, TX Stata Corporation2007;
Barros  AJDHirakata  VN Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol 2003;321
PubMed
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
Bramesfeld  APlatt  LSchwartz  FW Possibilities for intervention in adolescents' and young adults' depression from a public health perspective. Health Policy 2006;79 (2-3) 121- 131
PubMed
Cuijpers  Pvan Straten  ASmits  NSmit  F Screening and early psychological intervention for depression in schools: systematic review and meta-analysis. Eur Child Adolesc Psychiatry 2006;15 (5) 300- 307
PubMed

Figures

Tables

Table Graphic Jump LocationTable 1. Frequency Distribution of Anxiety and Depression At Follow-up, and Pathological Use of the Internet Status, Demographics, Health Behaviors, and Perception of Personal Conditions of Adolescents At Baseline
Table Graphic Jump LocationTable 2. Unadjusted Rate Ratios of Anxiety and Depression At Follow-up for Pathological Use of the Internet, Demographics, Health Behaviors, and Perception of Personal Conditions of Adolescents
Table Graphic Jump LocationTable 3. Adjusted Rate Ratios of Anxiety and Depression for Pathological Use of the Internet Among Adolescents

References

OReilly  M Internet addiction: a new disorder enters the medical lexicon. CMAJ 1996;154 (12) 1882- 1883
PubMed
Young  KS Psychology of computer use XL: addictive use of the Internet: a case that breaks the stereotype. Psychol Rep 1996;79 (3 pt 1) 899- 902
PubMed
Scherer  K College-life online: healthy and unhealthy Internet use. J Coll Student Dev 1997;38 (6) 655- 665
Young  KS Caught in the Net.  New York, NY John Wiley & Sons1998;
Chou  CHsiao  MC Internet addiction, usage, gratification, and pleasure experience: the Taiwan college students' case. Comput Educ 2000;35 (1) 65- 8010.1016/S0360-1315(00)00019-1
Wu  HRZhu  KJ Path analysis on related factors causing pathological use of the Internet disorder in college students [in Chinese]. Chin J Publ Health 2004;201363- 1364
Liu  TPotenza  MN Problematic Internet use: clinical implications. CNS Spectr 2007;12 (6) 453- 466
PubMed
Lam  LTPeng  ZMai  JJing  J The association between internet addiction and self-injurious behaviour among adolescents. Inj Prev 2009;15 (6) 403- 408
PubMed
Seo  MKang  HSYom  YHSeo  MKang  HSYom  YH Internet addiction and interpersonal problems in Korean adolescents. Comput Inform Nurs 2009;27 (4) 226- 233
PubMed
Kwon  JHChung  CSLee  J The effects of escape from self and interpersonal relationship on the pathological use of Internet games [published online August 23, 2009]. Community Ment Health J 2009;
PubMed10.1007/s10597-009-9236-1
Jang  KSHwang  SYChoi  JY Internet addiction and psychiatric symptoms among Korean adolescents. J Sch Health 2008;78 (3) 165- 171
PubMed
Morrison  CMGore  H The relationship between excessive Internet use and depression: a questionnaire-based study of 1,319 young people and adults. Psychopathology 2010;43 (2) 121- 126
PubMed
Ha  JHKim  SYBae  SC  et al.  Depression and Internet addiction in adolescents. Psychopathology 2007;40 (6) 424- 430
PubMed
Ko  CHYen  JYLiu  SCHuang  CFYen  CF The associations between aggressive behaviors and internet addiction and online activities in adolescents [published online ahead of print February 24, 2009]. J Adolesc Health 2009;44 (6) 598- 605
PubMed
Ko  CHYen  JYChen  CSYeh  YCYen  CF Predictive values of psychiatric symptoms for internet addiction in adolescents: a 2-year prospective study. Arch Pediatr Adolesc Med 2009;163 (10) 937- 943
PubMed
Zung  WW A rating instrument for anxiety disorders. Psychosomatics 1971;12 (6) 371- 379
PubMed
Zung  WW A self-rating depression scale. Arch Gen Psychiatry 1965;1263- 70
PubMed
Jedege  RO Psychometric attributes of the self-rating anxiety scale. Psychol Rep 1977;40 (1) 303- 306
PubMed
Lee  HCChiu  HFWing  YKLeung  CMKwong  PKChung  DW The Zung Self-rating Depression Scale: screening for depression among the Hong Kong Chinese elderly. J Geriatr Psychiatry Neurol 1994;7 (4) 216- 220
PubMed
Young  KS The Internet Addiction Test. Center for On-Line Addictions Web site. http://www.netaddiction.com/index.php?option=com_bfquiz&view=onepage&catid=46&Itemid=106. Accessed January 18, 2010
Widyanto  L McMurran  M The psychometric properties of the internet addiction test. Cyberpsychol Behav 2004;7 (4) 443- 450
PubMed
StataCorp, Stata Statistical Software: Release 10.0.  College Station, TX Stata Corporation2007;
Barros  AJDHirakata  VN Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol 2003;321
PubMed
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
Bramesfeld  APlatt  LSchwartz  FW Possibilities for intervention in adolescents' and young adults' depression from a public health perspective. Health Policy 2006;79 (2-3) 121- 131
PubMed
Cuijpers  Pvan Straten  ASmits  NSmit  F Screening and early psychological intervention for depression in schools: systematic review and meta-analysis. Eur Child Adolesc Psychiatry 2006;15 (5) 300- 307
PubMed

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