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Suicidal Behavior: A Prospective Study
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Internet Addiction among Adolescents May Predict Self-Harm/Suicidal Behavior: A Prospective Study Pei-Yin Pan, MD, and Chin-Bin Yeh, MD, PhD Objective To explore the role of Internet addiction in the development of self-harm/suicidal behavior among adolescents after 1-year of follow-up.
Study design We conducted this 1-year, prospective cohort study of 1861 adolescents (mean age 15.93 years) attending a senior high school in Taiwan; 1735 respondents (93.2%) were classified as having no history of selfharm/suicidal attempts in the initial assessment and were referred to as the “noncase” cohort. The Chen Internet Addiction Scale was used to identify individuals with Internet addiction. The participants were evaluated for selfharm/suicidal behavior again 1 year later and the “noncase” cohort was selected for statistical analysis. To examine the relationship between Internet addiction and self-harm/suicidal behavior, multivariate logistic regression analysis was performed using Internet addiction at baseline as the predictor for newly developed self-harm/suicidal behavior in the next year, after adjustment for potential confounding variables. Results The prevalence rate of Internet addiction at baseline was 23.0%. There were 59 students (3.9%) who were identified as having developed new self-harm/suicidal behaviors on follow-up assessments. After controlling for the effects of potential confounders, the relative risk of newly emerging self-harm/suicidal behavior for participants who were classified as Internet addicted was 2.41 (95% CI 1.16-4.99, P = .018) when compared with those without Internet addiction. Conclusions Our findings indicate that Internet addiction is prospectively associated with the incidence of selfharm/suicidal behavior in adolescents. (J Pediatr 2018;■■:■■-■■). See related article, p •••
I
nternet addiction, often referred to as uncontrollable and problematic use of information technologies, is a growing public health issue among adolescents.1,2 The concept of Internet addiction has been proposed to be a compulsive-impulsive spectrum disorder with clinical manifestations including preoccupation, excessive use, loss of control, withdrawal, tolerance, and harmful effects.3 In the past decade, many studies have reported an increasing prevalence of Internet addiction in adolescents, varying worldwide from 0.8% to 26.7%.4 Internet addiction is also associated with emotional and behavioral problems, social isolation, poor family relationships, and dysfunction in daily life.5-8 The biopsychosocial process of Internet addiction results in clinical features and difficulties in adolescents who use this technology excessively.9 Adolescents with Internet addiction have been reported to exhibit withdrawal symptoms of restlessness, irritability, and mood lability after discontinuing use of the Internet.3,10 In addition, their increased aggression and hostility have been reported to exacerbate the frequency and severity of interpersonal conflicts at home and in school.11,12 These adolescents also tend to lack real-life relationships and sacrifice study time because of their obsessive use of the Internet, which then contributes to greater social frustration and worse academic performance.13-16 Internet addiction among adolescents has been associated with a variety of psychopathologies, including symptoms of attention deficit-hyperactivity disorder, substance abuse, depression, anxiety, aggression, and poor sleep quality.4-6,17 Although the underlying mechanisms remain unclear, research has demonstrated that patients with addictive or psychiatric disorders share similar personality characteristics and biological substrates such as reward-related neurocircuitry and genetic traits.18-21 These overlapping neurophysiological and psychosocial risk factors between Internet addiction and these associated psychopathologies may play a role in the manifestation of high-frequency comorbidities. In addition, it has been suggested that Internet From the Department of Psychiatry, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
BAI BDI-II CIAS IRR PSQI WHOQOL-BREF
Beck’s Anxiety Inventory Beck’s Depression Inventory-II Chen Internet Addiction Scale Incidence rate ratio Pittsburgh Sleep Quality Index World Health Organization Quality of Life-Short Version
Supported by the Tri-Service General Hospital (TSGHC105-126, TSGH-C106-103, and TSGH-C106-162) and the Ministry of Science and Technology, Taiwan (1032314-B-016-004) to C.B.Y. The authors declare no conflicts of interest. 0022-3476/$ - see front matter. © 2018 Elsevier Inc. All rights reserved. https://doi.org10.1016/j.jpeds.2018.01.046
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THE JOURNAL OF PEDIATRICS • www.jpeds.com addiction and psychiatric symptoms may interact with each other bidirectionally; this may then amplify the symptoms and worsen the course of both illnesses.22,23 Recently, several cross-sectional studies have revealed that adolescents with Internet addiction also have a higher risk of self-harm/suicidal behavior. Lin et al found that in Southern Taiwan, adolescents aged 12-18 years with Internet addiction had higher risks of suicidal ideation and attempts than those without it.24 Another study on a sample of adolescents aged 15-16 years in Korea reported that the severity of Internet addiction was positively correlated with levels of depression and suicidal ideation.25 In terms of self-harm, Lam et al found that Internet addiction among adolescents aged 13-18 years increased the risk of self-injurious behavior in China.26 Another study in Europe reported that pathologic Internet use based on Young’s Diagnostic Questionnaire1 among adolescents with a mean age of 14.9 years was significantly correlated with selfharm/suicidal behavior.27 Moreover, a positive relationship between the number of symptoms of Internet addiction and temporal changes in the severity of suicidal ideation has been reported among adolescents.28 Based on these findings, it is clinically important to further explore the association between Internet addiction and suicidality in a longitudinal investigation. If Internet addiction among adolescents is predictive of suicidality, then it would be necessary to target Internet addiction in this population to reduce the incidence of self-harm/suicidal behavior. The existing literature lacks studies assessing a prospective relationship between Internet addiction and suicidality; therefore, the aim of the present 1-year follow-up study was to examine the role of Internet addiction in the development of self-destructive behavior among adolescents.
Methods This prospective study was conducted at a vocational high school with predominantly male students in Taipei City, Taiwan from September 2006 to September 2008. Before the recruitment, the principal investigator met with the principal of the school, the director of general affairs, the director of counseling, and the school nurses to describe the procedures of this study. We explained the aim of this study, procedures, questionnaires, confidentiality, the ways to refer to school psychologists or clinicians if a participant with suicidal risk were identified during the study, and the alternative ways of evaluating mental disorders if the students or parents were not willing to be recruited for the screening in this study and answered questions from teachers, students, and parents at school. Parental consent was obtained. All participants provided signed informed consent after the procedures for data collection and ensuring confidentiality of the responses had been thoroughly explained to them. The Institutional Review Board of Tri-Service General Hospital, National Defense Medical Centre, Taipei, Taiwan approved the study protocol. Questionnaires were in their classrooms after school. A total of 1947 first-year students (1590 male, 357 female) were recruited to participate in this investigation. At base-
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line, the respondents completed the Chen Internet Addiction Scale (CIAS),29,30 Pittsburgh Sleep Quality Index (PSQI),31-33 Beck’s Depression Inventory-II (BDI-II),34 Beck’s Anxiety Inventory (BAI),35 World Health Organization Quality of LifeShort Version (WHOQOL-Bref),36,37 and a set of self-reported questions about self-harm/suicidal behavior in the previous 6 months. We also collected the respondents’ demographic data including age and sex. The cohort was then followed up for 1 year, and the participants were invited to complete the questionnaire about selfharm/suicidal behavior again at the end of the follow-up period. Measures The CIAS consists of 26 items designed to identify people with Internet-related symptoms and problems.29 The items are classified into four factors, including symptoms of tolerance, compulsive use and withdrawal, interpersonal and health-related problems, and time management problems.29 Respondents are required to rate each item on a 4-point scale. The CIAS has been widely used in adolescents to screen and diagnose Internet addiction with cut-off scores of 57/58 and 63/64, respectively.30 In this study, a total CIAS score higher than 63 indicated Internet addictions. The PSQI, a 19-item questionnaire, is a self-rated inventory that assesses general sleep quality over a 1-month period.31,32 It has 7 components of sleep patterns: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. A total score of the 7 components ≥5 indicates poor sleep quality. The PSQI has been well validated and it is extensively used in children and adolescents.33 The BDI-II is a self-administered questionnaire which is used to measure the severity of depression in adults and adolescents. The BDI-II is a revision of the BDI, which was published in 1996 to correspond more closely to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSMIV) criteria for 2-week major depressive episodes.34 The BDIII is composed of 21 items with a total score ranging from 0 to 63, with a score ≤9 being considered minimal depression, 10-18 mild depression, 19-29 moderate depression, and 3063 severe depression. The BAI was developed with the primary purpose of discriminating anxiety from depression.35 This self-reported scale consists of 21 anxiety symptoms, most of which represent an individual panic symptom. Respondents rate each item to indicate how much they were bothered by the particular symptom “during the past week, including today.” Each response is scored on a 0-3 scale from “not at all” to “severely.” The severity of anxiety was then classified according to the suggested cut-off scores, with 0-7 as “minimal,” 8-15 as “mild,” 16-25 as “moderate,” and 26-63 as “severe.” The WHOQOL-Bref, a simplified form of the original WHOQOL-100, was developed by the WHOQOL group as a more practical version for use in clinical and epidemiologic surveys.36 This inventory contains 26 items, including 2 general items and the other 24 items classified into 4 domains: physical, psychological, social relationships and environment. Each
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item is rated on a five-point Likert scale (1-5). The scoring of WHOQOL-Bref is to multiply the mean score of items within each domain by 4. This method converts the score of each domain to range from 4 to 20. Previous studies have reported that the Taiwan version of the WHOQOL-Bref, a culturally specific adaptation of the brief form, is a reliable and valid instrument in clinical studies in Taiwan.37 The Cronbach’s alpha values for each questionnaire were: 0.96 for CIAS, 0.71 for PSQI, 0.89 for BDI-II, 0.93 for BAI, and 0.88 for WHOQOL-Bref. Questionnaire to Assess Self-Harm/Suicidal Behavior This questionnaire assessed the subjects’ experiences of selfharm/suicidal attempts during the previous 6 months. The items include the first time and the reasons for the behavior, as well as the frequency of this behavior during this period. For example, “Have you had self-harm or suicidal behavior in the past 6 months?” This question elicited a “yes” or “no” answer. Suicidal attempts were defined as self-injurious acts committed with the intention of killing one’s self. Self-harm was defined as intentional self-injurious behavior not related to the wish to die but severe enough to be noticeable by others. When the respondents reported either suicidal attempt(s) or self-harm behavior, they were counted as “yes.” Statistical Analyses All statistical analyses were performed using the SPSS software package v 17 (SPSS Inc, Chicago, Illinois). To determine the effect of Internet addiction on the development of suicidality among these students, respondents with no history of selfharm/suicidal attempts in the initial assessment were classified as the “noncase” cohort and were selected for statistical analysis. Logistic regression analysis was conducted to examine the predictability of newly developed self-harm/suicidal behavior with Internet addiction. Unadjusted incidence rate ratios (IRRs) of the outcomes in this prospective study and the corresponding 95% CIs for all variables of interest at the initial assessment were calculated. Here we use the term “incidence rate ratio” rather than “odds ratio” based on the assumption that our results with a low-prevalence outcome gave a very close approximation of the rate ratio. Multivariate logistic regression analysis was then used to explore the adjusted relationships between Internet addiction at baseline screening and suicidality 1 year later, after adjustment for potential confounding variables. Statistical significance was set at P < .05.
Results A total of 1861 participants completed all inventories at baseline; of these, 126 (6.8%) reported having previous self-harm/ suicidal behavior, and 1735 (93.2%) were classified as having no history of self-harm/suicidal attempts. Of these 1735 students, 1507 (1241 male, 266 female) also completed the followup assessments and were included in the incidence analysis (follow-up rate 86.9%). Comparisons of the respondents and no respondents at follow-up showed that a higher propor-
tion of male students (c2 = 7.221, P = .007) and those with Internet addiction (c2 = 4.041, P = .044) were lost to follow-up. The study cohort consisted of first-year high school students who were predominantly male (82.4%) with a mean (SD) age of 15.93 (0.73) years at baseline. In terms of mental health status at the initial assessment, most students (n = 676; 53.8%) reported having poor sleep quality (PSQI ≥5), 250 (18.6%) had at least mild depression, and 22 (1.6%) had severe depression. Symptoms of at least mild anxiety were noted in 23.9% of the students. The quality of life scores in each domain of the WHOQOL-Bref were rated between 13 and 14, which ranged between the 56 and 63 percentiles. The scores were lower than the 70-80 percentiles as the standard level of subjective well-being in the general population.38 With regard to Internet addiction, the prevalence rate was high, with 331 (23.0%) classified as being pathologic users of the Internet. In the followup assessments, 59 students (3.9%) stated that they had newly developed self-harm/suicidal behavior. The mental health profiles and outcomes of the respondents are shown in Table I. The results of logistic regression analysis for the bivariate relationships between self-harm/suicidal behavior and Internet addiction, sex, age, and baseline variables of mental health status are shown in Table II. The results showed that Internet addiction was significantly associated with the incidence of self-destructive behavior, unadjusted for other variables. The students with Internet addiction at baseline were more than twice as likely to have self-harm/suicidal behavior in the subsequent year (IRR = 2.63; 95% CI 1.53-4.53, P < .001) compared with those without Internet addiction. Other potential confounding factors associated with the development of selfharm/suicidal behavior in this study included sex (male to female, IRR = 0.51; 95% CI 0.29-0.92, P = .024), poor sleep quality (IRR = 2.28; 95% CI 1.22-4.27, P = .01), depressive symptoms (IRR = 2.73; 95% CI 1.52-4.90, P = .001), and psychological health as measured by the WHOQOL-Bref (IRR = 0.90; 95% CI 0.81-1.00, P = .044). Table III shows the results of multivariate logistic regression analyses to further examine the effects of potential confounding variables. The results showed that Internet addiction was still significantly associated with the development of selfharm/suicidal behavior. After adjustments for sex, age, poor sleep quality, depression, anxiety, and quality of life, those with Internet addiction had a greater relative risk for newly emerged self-destructive behavior (IRR = 2.41; 95% CI 1.16-4.99, P = .018) than those without Internet addiction. Variables were compared between subjects with completed data and those with any missing data to investigate the role of missing data in our conclusions. The results of comparison showed that there was no statistically significant difference in all the variables between the 2 groups.
Discussion The results demonstrated that Internet addiction was predictive of self-destructive behavior after 1 year of follow-up independently of sex, age, self-reported depressive, anxiety, and insomnia, as well as quality of life. These findings suggest that
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Table I. Outcomes of newly developed self-harm/suicidal behavior at follow-up, and Internet addiction status, characteristics of demographics, and mental health status at baseline Presence of self-harm/suicidal behavior (n = 59)
The entire sample (n = 1507) Variables (No. of missing data) Outcomes Self-harm/suicidal behavior at follow-up Yes Exposure Internet addiction (67) Yes Demographics Age (4) Years , mean ± SD Sex* Male Mental health status at baseline Poor sleep quality (250)* Yes Depressive symptoms (164)* Minimal depression Mild depression Moderate depression Severe depression Anxiety symptoms (42) Minimal anxiety Mild anxiety Moderate anxiety Severe anxiety Quality of life, mean ± SD (8) Physical health Psychological* Social relationships Environment
Frequency, No.
%
59
3.92
331
22.99
Frequency, No.
%
Absence of self-harm/suicidal behavior (n = 1448) Frequency, No.
59
24
15.93 ± 0.73
%
0
40.68
307
16.03 ± 0.68
21.20
15.92 ± 0.73
1241
82.35
42
71.19
1199
82.80
676
53.78
36
61.12
640
44.20
1093 153 75 22
81.38 11.39 5.58 1.64
32 12 4 3
54.24 20.34 6.78 5.08
1061 141 71 19
73.27 9.74 4.90 1.31
1115 167 139 44
76.11 11.40 9.49 3.00
35 8 8 3
59.32 13.56 13.56 5.08
1080 159 131 41
74.59 10.98 9.05 2.83
13.82 ± 2.34 13.67 ± 2.64 13.80 ± 2.51 13.87 ± 2.39
13.83 ± 2.36 13.00 ± 2.96 14.01 ± 2.57 13.61 ± 2.49
13.82 ± 2.34 13.69 ± 2.62 13.79 ± 2.51 13.89 ± 2.39
*The result of c2/independent t tests showed significant difference between the 2 groups of presence and absence of self-harm/suicidal behavior at follow-up.
Table II. Unadjusted IRRs of self-harm/suicidal behavior for baseline Internet addiction, demographics, and mental health status Variables Exposure Internet addiction (reference: no addiction) Demographics Sex Male (reference: female) Age Mental health status at baseline Poor sleep quality (reference: no poor sleep quality) Depressive symptoms Mild, moderate, severe depression (reference: minimal depression) Anxiety symptoms Mild, moderate, severe anxiety (reference: minimal anxiety) Quality of life Physical health Psychological Social relationships Environment *P < .05, **P < .01, ***P < .001.
Unadjusted IRR
Variables
1.53
4.53
0.51* 1.00
0.29 1.00
0.92 1.00
2.28*
1.22
4.27
1.77
1.00 0.90* 1.04 0.95
Adjusted IRR
95% CI
95% CI
2.63***
2.73**
Table III. Adjusted IRRs of self-harm/suicidal behavior for baseline Internet addiction among the adolescents
1.52
1.00
0.90 0.81 0.93 0.85
4.90
3.14
1.12 1.00 1.15 1.06
Exposure Internet addiction (reference: no addiction) Adjusted variables Sex Male (reference: female) Age Poor sleep quality (reference: no poor sleep quality) Depressive symptoms Mild, moderate, severe depression (reference: minimal depression) Anxiety symptoms Mild, moderate, severe anxiety (reference: minimal anxiety) Quality of life Physical health Psychological Social relationships Environment
2.41*
1.16
4.99
0.62 1.00 1.66
0.28 1.00 0.77
1.36 1.00 3.60
2.09
0.91
4.78
0.89
0.40
1.99
1.19 0.86 1.53 1.01
1.00 0.71 0.94 0.84
1.42 1.05 1.38 1.22
Adjusted for sex, age, poor sleep quality, depressive symptoms, anxiety symptoms, and quality of life. *P < .05.
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adolescents who are initially free of a history of self-harm/ suicidal behavior but use the Internet pathologically are twice as likely to subsequently develop self-harm/suicidal behavior. Clinicians working with adolescents need to be aware of the relationship between Internet addiction and self-harm/ suicidal behavior. These data may also inform future interventions for adolescents with Internet addictions. Internet addiction among adolescents has been associated with decreased self-esteem39 and lower tolerance to frustration and emotional discomfort.40 Adolescents with Internet addiction also showed less efficiency in information processing and poorer impulse control than their peers.41 When facing stressful situations, they tended to use inflexible and avoidant coping strategies. 42,43 In addition, adolescents with Internet addiction exhibited a propensity for risky decisionmaking, which was associated with their insensitivity to disadvantageous outcomes and altered brain function in cognitive control.44,45 Those neuropsychological deficits may account for the increased risk of self-harm/suicidal behaviors among adolescents with Internet addiction. Several studies have suggested that students with Internet addiction experience more culturally defined failures in real life46 and that they are more likely to perceive an inadequate availability of social support.47,48 They have also been reported to have lower academic achievements15,16 and poorer interpersonal relationships, both with family members and their peers.16,49-53 The development of social-emotional processing across adolescence coupled with psychosocial stress and maladaptive cognitive schemas may then contribute to their vulnerability to suicidality as well as self-injurious coping strategies.54-56 There are some limitations to this study. First, participants were from only 1 vocational high school with predominantly male students, affecting generalizability. Second, adolescent use of the Internet has evolved tremendously over the past decade. For example, smartphones allow adolescents to be continuously online. Third, the prevalence of reported self-harm/ suicidal behavior during the follow-up period was very low, potentially because of self-report of a stigmatizing behavior. Future research may benefit from a more strict measure of selfharm. Fourth, unmeasured factors may have confounded results, including biological vulnerability, socioeconomic status, alcohol and drug misuse, family problems, psychiatric disorders, victimization, and social contagion.56 Because there may be a complex interplay between these factors and Internet addiction, such as social withdrawal and generalized addiction risk, further studies are needed to explore the possibility of Internet addiction as a mediator in the relationship between these identified risk factors and suicidality. Sixth, there is still some debate about whether “Internet addiction” can be characterized as a definable psychiatric diagnosis.57 From clinical and public health perspectives, paying more attention and providing suitable early interventions for adolescents with Internet addiction may potentially reduce the incidence of self-harm/suicidal behavior in this population. In addition, future investigations are warranted to investigate the mechanism underpinning vulnerability to self-destructive be-
havior among adolescents with Internet addiction to help develop effective preventive strategies. ■ We thank the participants, parents, and teachers who took part in this study. Submitted for publication Aug 7, 2017; last revision received Nov 14, 2017; accepted Jan 12, 2018
References 1. Young KS. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav 1998;1:237-44. 2. Christakis DA, Moreno MA. Trapped in the net: will Internet addiction become a 21st-century epidemic? Arch Pediatr Adolesc Med 2009;163:95960. 3. Beard KW, Wolf EM. Modification in the proposed diagnostic criteria for Internet addiction. Cyberpsychol Behav 2001;4:377-83. 4. Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: a systematic review of epidemiological research for the last decade. Curr Pharm Des 2014;20:4026-52. 5. Carli V, Durkee T, Wasserman D, Hadlaczky G, Despalins R, Kramarz E, et al. The association between pathological Internet use and comorbid psychopathology: a systematic review. Psychopathology 2013;46:1-13. 6. Ko CH, Yen JY, Yen CF, Chen CS, Chen CC. The association between Internet addiction and psychiatric disorder: a review of the literature. Eur Psychiatry 2012;27:1-8. 7. Lam LT. Risk factors of Internet addiction and the health effect of Internet addiction on adolescents: a systematic review of longitudinal and prospective studies. Curr Psychiatry Rep 2014;16:508. 8. Kuss DJ, Griffiths MD. Internet gaming addiction: a systematic review of empirical research. Int J Ment Health Addict 2012;10:278-96. 9. Griffiths M. A ‘components’ model of addiction within a biopsychosocial framework. J Subst Use 2005;10:191-7. 10. Kaptsis D, King DL, Delfabbro PH, Gradisar M. Withdrawal symptoms in Internet gaming disorder: a systematic review. Clin Psychol Rev 2016;43:58-66. 11. Ko CH, Yen JY, Liu SC, Huang CF, Yen CF. The associations between aggressive behaviors and Internet addiction and online activities in adolescents. J Adolesc Health 2009;44:598-605. 12. Ko CH, Yen JY, Chen CS, Yeh YC, Yen CF. Predictive values of psychiatric symptoms for Internet addiction in adolescents: a 2-year prospective study. Arch Pediatr Adolesc Med 2009;163:937-43. 13. Peters CS, Malesky LA. Problematic usage among highly-engaged players of massively multiplayer online role playing games. Cyberpsychol Behav 2008;11:481-4. 14. Allison SE, von Wahlde L, Shockley T, Gabbard GO. The development of the self in the era of the Internet and role-playing fantasy games. Am J Psychiatry 2006;163:381-5. 15. Xu J, Shen LX, Yan CH, Hu H, Yang F, Wang L, et al. Personal characteristics related to the risk of adolescent Internet addiction: a survey in Shanghai, China. BMC Public Health 2012;12:1106. 16. Mythily S, Qiu S, Winslow M. Prevalence and correlates of excessive Internet use among youth in Singapore. Ann Acad Med Singapore 2008;37:914. 17. Lam LT. Internet gaming addiction, problematic use of the Internet, and sleep problems: a systematic review. Curr Psychiatry Rep 2014;16:444. 18. Ko CH, Liu GC, Hsiao S, Yen JY, Yang MJ, Lin WC, et al. Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res 2009;43:739-47. 19. 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;17:807-25. 20. Castellanos FX, Tannock R. Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes. Nat Rev Neurosci 2002;3:61728.
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THE JOURNAL OF PEDIATRICS • www.jpeds.com 21. Lee YS, Han DH, Yang KC, Daniels MA, Na C, Kee BS, et al. Depression like characteristics of 5HTTLPR polymorphism and temperament in excessive Internet users. J Affect Disord 2008;109:165-9. 22. Pani PP, Maremmani I, Trogu E, Gessa GL, Ruiz P, Akiskal HS. Delineating the psychic structure of substance abuse and addictions: should anxiety, mood and impulse-control dysregulation be included? J Affect Disord 2010;122:185-97. 23. Mueser KT, Drake RE, Wallach MA. Dual diagnosis: a review of etiological theories. Addict Behav 1998;23:717-34. 24. Lin IH, Ko CH, Chang YP, Liu TL, Wang PW, Lin HC, et al. The association between suicidality and Internet addiction and activities in Taiwanese adolescents. Compr Psychiatry 2014;55:504-10. 25. Kim K, Ryu E, Chon MY, Yeun EJ, Choi SY, Seo JS, et al. Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey. Int J Nurs Stud 2006;43:185-92. 26. Lam LT, Peng Z, Mai J, Jing J. The association between Internet addiction and self-injurious behavior among adolescents. Inj Prev 2009;15:4038. 27. Kaess M, Durkee T, Brunner R, Carli V, Parzer P, Wasserman C, et al. Pathological Internet use among European adolescents: psychopathology and self-destructive behaviors. Eur Child Adolesc Psychiatry 2014;23:1093-102. 28. Fu KW, Chan WS, Wong PW, Yip PS. Internet addiction: prevalence, discriminant validity and correlates among adolescents in Hong Kong. Br J Psychiatry 2010;196:486-92. 29. Chen SH, Weng LJ, Su YJ, Wu HM, Yang PF. Development of a Chinese internet addiction scale and its psychometric Study. Chin J Psychol 2003;45:279-94. 30. Ko CH, Yen JY, Yen CF, Chen CC, Yen CN, Chen SH. Screening for Internet addiction: an empirical study on cut-off points for the Chen Internet Addiction Scale. Kaohsiung J Med Sci 2005;21:545-51. 31. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989;28:193-213. 32. Tsai PS, Wang SY, Wang MY, Su CT, Yang TT, Huang CJ, et al. Psychometric evaluation of the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) in primary insomnia and control subjects. Qual Life Res 2005;14:1943-52. 33. Megdal SP, Schernhammer ES. Correlates for poor sleepers in a Los Angeles high school. Sleep Med 2007;9:60-3. 34. Beck AT, Steer RA, Brown GK. BDI-II: beck depression inventory manual. 2nd ed. San Antonio: Psychological Corporation; 1996. 35. Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol 1988;56:8937. 36. Development of the World Health Organization WHOQOL-BREF quality of life assessment. The WHOQOL Group. Psychol Med 1998;28:551-8. 37. Yao G, Chung CW, Yu CF, Wang JD. Development and verification of validity and reliability of the WHOQOL-BREF Taiwan version. J Formos Med Assoc 2002;101:342-51. 38. Cummins RA. On the trail of the gold standard for subjective wellbeing. Soc Indic Res 1995;35:179-200.
Volume ■■ 39. Wang L, Luo J, Bai Y, Kong J, Luo J, Gao W, et al. Internet addiction of adolescents in China: prevalence, predictors, and association with wellbeing. Addict Res Theory 2012;21:62-9. 40. Ko CH, Yen JY, Yen CF, Chen CS, Wang SY. The association between Internet addiction and belief of frustration intolerance: the gender difference. Cyberpsychol Behav 2008;11:273-8. 41. Dong G, Zhou H, Zhao X. Impulse inhibition in people with Internet addiction disorder: electrophysiological evidence from a Go/NoGo study. Neurosci Lett 2010;485:138-42. 42. Cheng C, Sun P, Mak KK. Internet addiction and psychosocial maladjustment: avoidant coping and coping inflexibility as psychological mechanisms. Cyberpsychol Behav Soc Netw 2015;18:539-46. 43. Li H, Wang J, Wang L. A survey on the generalized problematic internet use in chinese college students and its relations to stressful life events and coping style. Int J Ment Health Addict 2009;7:333-46. 44. Yao YW, Chen PR, Li S, Wang LJ, Zhang JT, Yip SW, et al. Decisionmaking for risky gains and losses among college students with Internet gaming disorder. PLoS ONE 2015;10:e0116471. 45. Seok JW, Lee KH, Sohn S, Sohn JH. Neural substrates of risky decision making in individuals with Internet addiction. Aust N Z J Psychiatry 2015;49:923-32. 46. Yee N. Motivations for play in online games. Cyberpsychol Behav 2006;9:772-5. 47. Ozcan NK, Buzlu S. Internet use and its relation with the psychosocial situation for a sample of university students. Cyberpsychol Behav 2007;10:767-72. 48. Casale S, Fioravanti G. Psychosocial correlates of Internet use among Italian students. Int J Psychol 2011;46:288-98. 49. Yen JY, Yen CF, Chen CC, Chen SH, Ko CH. Family factors of Internet addiction and substance use experience in Taiwanese adolescents. Cyberpsychol Behav 2007;10:323-9. 50. Wang H, Zhou X, Lu C, Wu J, Deng X, Hong L. Problematic Internet Use in high school students in Guangdong Province, China. PLoS ONE 2011;6:e19660. 51. Lam LT, Peng ZW, Mai JC, Jing J. Factors associated with Internet addiction among adolescents. Cyberpsychol Behav 2009;12:551-5. 52. Yen CF, Ko CH, Yen JY, Chang YP, Cheng CP. Multi-dimensional discriminative factors for Internet addiction among adolescents regarding gender and age. Psychiatry Clin Neurosci 2009;63:357-64. 53. Milani L, Osualdella D, Di Blasio P. Quality of interpersonal relationships and problematic Internet use in adolescence. Cyberpsychol Behav 2009;12:681-4. 54. Platt B, Cohen Kadosh K, Lau JY. The role of peer rejection in adolescent depression. Depress Anxiety 2013;30:809-21. 55. Sebastian C, Viding E, Williams KD, Blakemore SJ. Social brain development and the affective consequences of ostracism in adolescence. Brain Cogn 2010;72:134-45. 56. Hawton K, Saunders KE, O’Connor RC. Self-harm and suicide in adolescents. Lancet 2012;379:2373-82. 57. Musetti A, Cattivelli R, Giacobbi M, Zuglian P, Ceccarini M, Capelli F, et al. Challenges in internet addiction disorder: is a diagnosis feasible or not? Front Psychol 2016;7:842.
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Internet Addiction among Adolescents May Predict Self-Harm/Suicidal Behavior: A Prospective Study Pei-Yin Pan, MD, and Chin-Bin Yeh, MD, PhD Objective To explore the role of Internet addiction in the development of self-harm/suicidal behavior among adolescents after 1-year of follow-up.
Study design We conducted this 1-year, prospective cohort study of 1861 adolescents (mean age 15.93 years) attending a senior high school in Taiwan; 1735 respondents (93.2%) were classified as having no history of selfharm/suicidal attempts in the initial assessment and were referred to as the “noncase” cohort. The Chen Internet Addiction Scale was used to identify individuals with Internet addiction. The participants were evaluated for selfharm/suicidal behavior again 1 year later and the “noncase” cohort was selected for statistical analysis. To examine the relationship between Internet addiction and self-harm/suicidal behavior, multivariate logistic regression analysis was performed using Internet addiction at baseline as the predictor for newly developed self-harm/suicidal behavior in the next year, after adjustment for potential confounding variables. Results The prevalence rate of Internet addiction at baseline was 23.0%. There were 59 students (3.9%) who were identified as having developed new self-harm/suicidal behaviors on follow-up assessments. After controlling for the effects of potential confounders, the relative risk of newly emerging self-harm/suicidal behavior for participants who were classified as Internet addicted was 2.41 (95% CI 1.16-4.99, P = .018) when compared with those without Internet addiction. Conclusions Our findings indicate that Internet addiction is prospectively associated with the incidence of selfharm/suicidal behavior in adolescents. (J Pediatr 2018;■■:■■-■■). See related article, p •••
I
nternet addiction, often referred to as uncontrollable and problematic use of information technologies, is a growing public health issue among adolescents.1,2 The concept of Internet addiction has been proposed to be a compulsive-impulsive spectrum disorder with clinical manifestations including preoccupation, excessive use, loss of control, withdrawal, tolerance, and harmful effects.3 In the past decade, many studies have reported an increasing prevalence of Internet addiction in adolescents, varying worldwide from 0.8% to 26.7%.4 Internet addiction is also associated with emotional and behavioral problems, social isolation, poor family relationships, and dysfunction in daily life.5-8 The biopsychosocial process of Internet addiction results in clinical features and difficulties in adolescents who use this technology excessively.9 Adolescents with Internet addiction have been reported to exhibit withdrawal symptoms of restlessness, irritability, and mood lability after discontinuing use of the Internet.3,10 In addition, their increased aggression and hostility have been reported to exacerbate the frequency and severity of interpersonal conflicts at home and in school.11,12 These adolescents also tend to lack real-life relationships and sacrifice study time because of their obsessive use of the Internet, which then contributes to greater social frustration and worse academic performance.13-16 Internet addiction among adolescents has been associated with a variety of psychopathologies, including symptoms of attention deficit-hyperactivity disorder, substance abuse, depression, anxiety, aggression, and poor sleep quality.4-6,17 Although the underlying mechanisms remain unclear, research has demonstrated that patients with addictive or psychiatric disorders share similar personality characteristics and biological substrates such as reward-related neurocircuitry and genetic traits.18-21 These overlapping neurophysiological and psychosocial risk factors between Internet addiction and these associated psychopathologies may play a role in the manifestation of high-frequency comorbidities. In addition, it has been suggested that Internet From the Department of Psychiatry, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
BAI BDI-II CIAS IRR PSQI WHOQOL-BREF
Beck’s Anxiety Inventory Beck’s Depression Inventory-II Chen Internet Addiction Scale Incidence rate ratio Pittsburgh Sleep Quality Index World Health Organization Quality of Life-Short Version
Supported by the Tri-Service General Hospital (TSGHC105-126, TSGH-C106-103, and TSGH-C106-162) and the Ministry of Science and Technology, Taiwan (1032314-B-016-004) to C.B.Y. The authors declare no conflicts of interest. 0022-3476/$ - see front matter. © 2018 Elsevier Inc. All rights reserved. https://doi.org10.1016/j.jpeds.2018.01.046
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THE JOURNAL OF PEDIATRICS • www.jpeds.com addiction and psychiatric symptoms may interact with each other bidirectionally; this may then amplify the symptoms and worsen the course of both illnesses.22,23 Recently, several cross-sectional studies have revealed that adolescents with Internet addiction also have a higher risk of self-harm/suicidal behavior. Lin et al found that in Southern Taiwan, adolescents aged 12-18 years with Internet addiction had higher risks of suicidal ideation and attempts than those without it.24 Another study on a sample of adolescents aged 15-16 years in Korea reported that the severity of Internet addiction was positively correlated with levels of depression and suicidal ideation.25 In terms of self-harm, Lam et al found that Internet addiction among adolescents aged 13-18 years increased the risk of self-injurious behavior in China.26 Another study in Europe reported that pathologic Internet use based on Young’s Diagnostic Questionnaire1 among adolescents with a mean age of 14.9 years was significantly correlated with selfharm/suicidal behavior.27 Moreover, a positive relationship between the number of symptoms of Internet addiction and temporal changes in the severity of suicidal ideation has been reported among adolescents.28 Based on these findings, it is clinically important to further explore the association between Internet addiction and suicidality in a longitudinal investigation. If Internet addiction among adolescents is predictive of suicidality, then it would be necessary to target Internet addiction in this population to reduce the incidence of self-harm/suicidal behavior. The existing literature lacks studies assessing a prospective relationship between Internet addiction and suicidality; therefore, the aim of the present 1-year follow-up study was to examine the role of Internet addiction in the development of self-destructive behavior among adolescents.
Methods This prospective study was conducted at a vocational high school with predominantly male students in Taipei City, Taiwan from September 2006 to September 2008. Before the recruitment, the principal investigator met with the principal of the school, the director of general affairs, the director of counseling, and the school nurses to describe the procedures of this study. We explained the aim of this study, procedures, questionnaires, confidentiality, the ways to refer to school psychologists or clinicians if a participant with suicidal risk were identified during the study, and the alternative ways of evaluating mental disorders if the students or parents were not willing to be recruited for the screening in this study and answered questions from teachers, students, and parents at school. Parental consent was obtained. All participants provided signed informed consent after the procedures for data collection and ensuring confidentiality of the responses had been thoroughly explained to them. The Institutional Review Board of Tri-Service General Hospital, National Defense Medical Centre, Taipei, Taiwan approved the study protocol. Questionnaires were in their classrooms after school. A total of 1947 first-year students (1590 male, 357 female) were recruited to participate in this investigation. At base-
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line, the respondents completed the Chen Internet Addiction Scale (CIAS),29,30 Pittsburgh Sleep Quality Index (PSQI),31-33 Beck’s Depression Inventory-II (BDI-II),34 Beck’s Anxiety Inventory (BAI),35 World Health Organization Quality of LifeShort Version (WHOQOL-Bref),36,37 and a set of self-reported questions about self-harm/suicidal behavior in the previous 6 months. We also collected the respondents’ demographic data including age and sex. The cohort was then followed up for 1 year, and the participants were invited to complete the questionnaire about selfharm/suicidal behavior again at the end of the follow-up period. Measures The CIAS consists of 26 items designed to identify people with Internet-related symptoms and problems.29 The items are classified into four factors, including symptoms of tolerance, compulsive use and withdrawal, interpersonal and health-related problems, and time management problems.29 Respondents are required to rate each item on a 4-point scale. The CIAS has been widely used in adolescents to screen and diagnose Internet addiction with cut-off scores of 57/58 and 63/64, respectively.30 In this study, a total CIAS score higher than 63 indicated Internet addictions. The PSQI, a 19-item questionnaire, is a self-rated inventory that assesses general sleep quality over a 1-month period.31,32 It has 7 components of sleep patterns: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. A total score of the 7 components ≥5 indicates poor sleep quality. The PSQI has been well validated and it is extensively used in children and adolescents.33 The BDI-II is a self-administered questionnaire which is used to measure the severity of depression in adults and adolescents. The BDI-II is a revision of the BDI, which was published in 1996 to correspond more closely to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSMIV) criteria for 2-week major depressive episodes.34 The BDIII is composed of 21 items with a total score ranging from 0 to 63, with a score ≤9 being considered minimal depression, 10-18 mild depression, 19-29 moderate depression, and 3063 severe depression. The BAI was developed with the primary purpose of discriminating anxiety from depression.35 This self-reported scale consists of 21 anxiety symptoms, most of which represent an individual panic symptom. Respondents rate each item to indicate how much they were bothered by the particular symptom “during the past week, including today.” Each response is scored on a 0-3 scale from “not at all” to “severely.” The severity of anxiety was then classified according to the suggested cut-off scores, with 0-7 as “minimal,” 8-15 as “mild,” 16-25 as “moderate,” and 26-63 as “severe.” The WHOQOL-Bref, a simplified form of the original WHOQOL-100, was developed by the WHOQOL group as a more practical version for use in clinical and epidemiologic surveys.36 This inventory contains 26 items, including 2 general items and the other 24 items classified into 4 domains: physical, psychological, social relationships and environment. Each
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item is rated on a five-point Likert scale (1-5). The scoring of WHOQOL-Bref is to multiply the mean score of items within each domain by 4. This method converts the score of each domain to range from 4 to 20. Previous studies have reported that the Taiwan version of the WHOQOL-Bref, a culturally specific adaptation of the brief form, is a reliable and valid instrument in clinical studies in Taiwan.37 The Cronbach’s alpha values for each questionnaire were: 0.96 for CIAS, 0.71 for PSQI, 0.89 for BDI-II, 0.93 for BAI, and 0.88 for WHOQOL-Bref. Questionnaire to Assess Self-Harm/Suicidal Behavior This questionnaire assessed the subjects’ experiences of selfharm/suicidal attempts during the previous 6 months. The items include the first time and the reasons for the behavior, as well as the frequency of this behavior during this period. For example, “Have you had self-harm or suicidal behavior in the past 6 months?” This question elicited a “yes” or “no” answer. Suicidal attempts were defined as self-injurious acts committed with the intention of killing one’s self. Self-harm was defined as intentional self-injurious behavior not related to the wish to die but severe enough to be noticeable by others. When the respondents reported either suicidal attempt(s) or self-harm behavior, they were counted as “yes.” Statistical Analyses All statistical analyses were performed using the SPSS software package v 17 (SPSS Inc, Chicago, Illinois). To determine the effect of Internet addiction on the development of suicidality among these students, respondents with no history of selfharm/suicidal attempts in the initial assessment were classified as the “noncase” cohort and were selected for statistical analysis. Logistic regression analysis was conducted to examine the predictability of newly developed self-harm/suicidal behavior with Internet addiction. Unadjusted incidence rate ratios (IRRs) of the outcomes in this prospective study and the corresponding 95% CIs for all variables of interest at the initial assessment were calculated. Here we use the term “incidence rate ratio” rather than “odds ratio” based on the assumption that our results with a low-prevalence outcome gave a very close approximation of the rate ratio. Multivariate logistic regression analysis was then used to explore the adjusted relationships between Internet addiction at baseline screening and suicidality 1 year later, after adjustment for potential confounding variables. Statistical significance was set at P < .05.
Results A total of 1861 participants completed all inventories at baseline; of these, 126 (6.8%) reported having previous self-harm/ suicidal behavior, and 1735 (93.2%) were classified as having no history of self-harm/suicidal attempts. Of these 1735 students, 1507 (1241 male, 266 female) also completed the followup assessments and were included in the incidence analysis (follow-up rate 86.9%). Comparisons of the respondents and no respondents at follow-up showed that a higher propor-
tion of male students (c2 = 7.221, P = .007) and those with Internet addiction (c2 = 4.041, P = .044) were lost to follow-up. The study cohort consisted of first-year high school students who were predominantly male (82.4%) with a mean (SD) age of 15.93 (0.73) years at baseline. In terms of mental health status at the initial assessment, most students (n = 676; 53.8%) reported having poor sleep quality (PSQI ≥5), 250 (18.6%) had at least mild depression, and 22 (1.6%) had severe depression. Symptoms of at least mild anxiety were noted in 23.9% of the students. The quality of life scores in each domain of the WHOQOL-Bref were rated between 13 and 14, which ranged between the 56 and 63 percentiles. The scores were lower than the 70-80 percentiles as the standard level of subjective well-being in the general population.38 With regard to Internet addiction, the prevalence rate was high, with 331 (23.0%) classified as being pathologic users of the Internet. In the followup assessments, 59 students (3.9%) stated that they had newly developed self-harm/suicidal behavior. The mental health profiles and outcomes of the respondents are shown in Table I. The results of logistic regression analysis for the bivariate relationships between self-harm/suicidal behavior and Internet addiction, sex, age, and baseline variables of mental health status are shown in Table II. The results showed that Internet addiction was significantly associated with the incidence of self-destructive behavior, unadjusted for other variables. The students with Internet addiction at baseline were more than twice as likely to have self-harm/suicidal behavior in the subsequent year (IRR = 2.63; 95% CI 1.53-4.53, P < .001) compared with those without Internet addiction. Other potential confounding factors associated with the development of selfharm/suicidal behavior in this study included sex (male to female, IRR = 0.51; 95% CI 0.29-0.92, P = .024), poor sleep quality (IRR = 2.28; 95% CI 1.22-4.27, P = .01), depressive symptoms (IRR = 2.73; 95% CI 1.52-4.90, P = .001), and psychological health as measured by the WHOQOL-Bref (IRR = 0.90; 95% CI 0.81-1.00, P = .044). Table III shows the results of multivariate logistic regression analyses to further examine the effects of potential confounding variables. The results showed that Internet addiction was still significantly associated with the development of selfharm/suicidal behavior. After adjustments for sex, age, poor sleep quality, depression, anxiety, and quality of life, those with Internet addiction had a greater relative risk for newly emerged self-destructive behavior (IRR = 2.41; 95% CI 1.16-4.99, P = .018) than those without Internet addiction. Variables were compared between subjects with completed data and those with any missing data to investigate the role of missing data in our conclusions. The results of comparison showed that there was no statistically significant difference in all the variables between the 2 groups.
Discussion The results demonstrated that Internet addiction was predictive of self-destructive behavior after 1 year of follow-up independently of sex, age, self-reported depressive, anxiety, and insomnia, as well as quality of life. These findings suggest that
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Table I. Outcomes of newly developed self-harm/suicidal behavior at follow-up, and Internet addiction status, characteristics of demographics, and mental health status at baseline Presence of self-harm/suicidal behavior (n = 59)
The entire sample (n = 1507) Variables (No. of missing data) Outcomes Self-harm/suicidal behavior at follow-up Yes Exposure Internet addiction (67) Yes Demographics Age (4) Years , mean ± SD Sex* Male Mental health status at baseline Poor sleep quality (250)* Yes Depressive symptoms (164)* Minimal depression Mild depression Moderate depression Severe depression Anxiety symptoms (42) Minimal anxiety Mild anxiety Moderate anxiety Severe anxiety Quality of life, mean ± SD (8) Physical health Psychological* Social relationships Environment
Frequency, No.
%
59
3.92
331
22.99
Frequency, No.
%
Absence of self-harm/suicidal behavior (n = 1448) Frequency, No.
59
24
15.93 ± 0.73
%
0
40.68
307
16.03 ± 0.68
21.20
15.92 ± 0.73
1241
82.35
42
71.19
1199
82.80
676
53.78
36
61.12
640
44.20
1093 153 75 22
81.38 11.39 5.58 1.64
32 12 4 3
54.24 20.34 6.78 5.08
1061 141 71 19
73.27 9.74 4.90 1.31
1115 167 139 44
76.11 11.40 9.49 3.00
35 8 8 3
59.32 13.56 13.56 5.08
1080 159 131 41
74.59 10.98 9.05 2.83
13.82 ± 2.34 13.67 ± 2.64 13.80 ± 2.51 13.87 ± 2.39
13.83 ± 2.36 13.00 ± 2.96 14.01 ± 2.57 13.61 ± 2.49
13.82 ± 2.34 13.69 ± 2.62 13.79 ± 2.51 13.89 ± 2.39
*The result of c2/independent t tests showed significant difference between the 2 groups of presence and absence of self-harm/suicidal behavior at follow-up.
Table II. Unadjusted IRRs of self-harm/suicidal behavior for baseline Internet addiction, demographics, and mental health status Variables Exposure Internet addiction (reference: no addiction) Demographics Sex Male (reference: female) Age Mental health status at baseline Poor sleep quality (reference: no poor sleep quality) Depressive symptoms Mild, moderate, severe depression (reference: minimal depression) Anxiety symptoms Mild, moderate, severe anxiety (reference: minimal anxiety) Quality of life Physical health Psychological Social relationships Environment *P < .05, **P < .01, ***P < .001.
Unadjusted IRR
Variables
1.53
4.53
0.51* 1.00
0.29 1.00
0.92 1.00
2.28*
1.22
4.27
1.77
1.00 0.90* 1.04 0.95
Adjusted IRR
95% CI
95% CI
2.63***
2.73**
Table III. Adjusted IRRs of self-harm/suicidal behavior for baseline Internet addiction among the adolescents
1.52
1.00
0.90 0.81 0.93 0.85
4.90
3.14
1.12 1.00 1.15 1.06
Exposure Internet addiction (reference: no addiction) Adjusted variables Sex Male (reference: female) Age Poor sleep quality (reference: no poor sleep quality) Depressive symptoms Mild, moderate, severe depression (reference: minimal depression) Anxiety symptoms Mild, moderate, severe anxiety (reference: minimal anxiety) Quality of life Physical health Psychological Social relationships Environment
2.41*
1.16
4.99
0.62 1.00 1.66
0.28 1.00 0.77
1.36 1.00 3.60
2.09
0.91
4.78
0.89
0.40
1.99
1.19 0.86 1.53 1.01
1.00 0.71 0.94 0.84
1.42 1.05 1.38 1.22
Adjusted for sex, age, poor sleep quality, depressive symptoms, anxiety symptoms, and quality of life. *P < .05.
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adolescents who are initially free of a history of self-harm/ suicidal behavior but use the Internet pathologically are twice as likely to subsequently develop self-harm/suicidal behavior. Clinicians working with adolescents need to be aware of the relationship between Internet addiction and self-harm/ suicidal behavior. These data may also inform future interventions for adolescents with Internet addictions. Internet addiction among adolescents has been associated with decreased self-esteem39 and lower tolerance to frustration and emotional discomfort.40 Adolescents with Internet addiction also showed less efficiency in information processing and poorer impulse control than their peers.41 When facing stressful situations, they tended to use inflexible and avoidant coping strategies. 42,43 In addition, adolescents with Internet addiction exhibited a propensity for risky decisionmaking, which was associated with their insensitivity to disadvantageous outcomes and altered brain function in cognitive control.44,45 Those neuropsychological deficits may account for the increased risk of self-harm/suicidal behaviors among adolescents with Internet addiction. Several studies have suggested that students with Internet addiction experience more culturally defined failures in real life46 and that they are more likely to perceive an inadequate availability of social support.47,48 They have also been reported to have lower academic achievements15,16 and poorer interpersonal relationships, both with family members and their peers.16,49-53 The development of social-emotional processing across adolescence coupled with psychosocial stress and maladaptive cognitive schemas may then contribute to their vulnerability to suicidality as well as self-injurious coping strategies.54-56 There are some limitations to this study. First, participants were from only 1 vocational high school with predominantly male students, affecting generalizability. Second, adolescent use of the Internet has evolved tremendously over the past decade. For example, smartphones allow adolescents to be continuously online. Third, the prevalence of reported self-harm/ suicidal behavior during the follow-up period was very low, potentially because of self-report of a stigmatizing behavior. Future research may benefit from a more strict measure of selfharm. Fourth, unmeasured factors may have confounded results, including biological vulnerability, socioeconomic status, alcohol and drug misuse, family problems, psychiatric disorders, victimization, and social contagion.56 Because there may be a complex interplay between these factors and Internet addiction, such as social withdrawal and generalized addiction risk, further studies are needed to explore the possibility of Internet addiction as a mediator in the relationship between these identified risk factors and suicidality. Sixth, there is still some debate about whether “Internet addiction” can be characterized as a definable psychiatric diagnosis.57 From clinical and public health perspectives, paying more attention and providing suitable early interventions for adolescents with Internet addiction may potentially reduce the incidence of self-harm/suicidal behavior in this population. In addition, future investigations are warranted to investigate the mechanism underpinning vulnerability to self-destructive be-
havior among adolescents with Internet addiction to help develop effective preventive strategies. ■ We thank the participants, parents, and teachers who took part in this study. Submitted for publication Aug 7, 2017; last revision received Nov 14, 2017; accepted Jan 12, 2018
References 1. Young KS. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav 1998;1:237-44. 2. Christakis DA, Moreno MA. Trapped in the net: will Internet addiction become a 21st-century epidemic? Arch Pediatr Adolesc Med 2009;163:95960. 3. Beard KW, Wolf EM. Modification in the proposed diagnostic criteria for Internet addiction. Cyberpsychol Behav 2001;4:377-83. 4. Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: a systematic review of epidemiological research for the last decade. Curr Pharm Des 2014;20:4026-52. 5. Carli V, Durkee T, Wasserman D, Hadlaczky G, Despalins R, Kramarz E, et al. The association between pathological Internet use and comorbid psychopathology: a systematic review. Psychopathology 2013;46:1-13. 6. Ko CH, Yen JY, Yen CF, Chen CS, Chen CC. The association between Internet addiction and psychiatric disorder: a review of the literature. Eur Psychiatry 2012;27:1-8. 7. Lam LT. Risk factors of Internet addiction and the health effect of Internet addiction on adolescents: a systematic review of longitudinal and prospective studies. Curr Psychiatry Rep 2014;16:508. 8. Kuss DJ, Griffiths MD. Internet gaming addiction: a systematic review of empirical research. Int J Ment Health Addict 2012;10:278-96. 9. Griffiths M. A ‘components’ model of addiction within a biopsychosocial framework. J Subst Use 2005;10:191-7. 10. Kaptsis D, King DL, Delfabbro PH, Gradisar M. Withdrawal symptoms in Internet gaming disorder: a systematic review. Clin Psychol Rev 2016;43:58-66. 11. Ko CH, Yen JY, Liu SC, Huang CF, Yen CF. The associations between aggressive behaviors and Internet addiction and online activities in adolescents. J Adolesc Health 2009;44:598-605. 12. Ko CH, Yen JY, Chen CS, Yeh YC, Yen CF. Predictive values of psychiatric symptoms for Internet addiction in adolescents: a 2-year prospective study. Arch Pediatr Adolesc Med 2009;163:937-43. 13. Peters CS, Malesky LA. Problematic usage among highly-engaged players of massively multiplayer online role playing games. Cyberpsychol Behav 2008;11:481-4. 14. Allison SE, von Wahlde L, Shockley T, Gabbard GO. The development of the self in the era of the Internet and role-playing fantasy games. Am J Psychiatry 2006;163:381-5. 15. Xu J, Shen LX, Yan CH, Hu H, Yang F, Wang L, et al. Personal characteristics related to the risk of adolescent Internet addiction: a survey in Shanghai, China. BMC Public Health 2012;12:1106. 16. Mythily S, Qiu S, Winslow M. Prevalence and correlates of excessive Internet use among youth in Singapore. Ann Acad Med Singapore 2008;37:914. 17. Lam LT. Internet gaming addiction, problematic use of the Internet, and sleep problems: a systematic review. Curr Psychiatry Rep 2014;16:444. 18. Ko CH, Liu GC, Hsiao S, Yen JY, Yang MJ, Lin WC, et al. Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res 2009;43:739-47. 19. 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;17:807-25. 20. Castellanos FX, Tannock R. Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes. Nat Rev Neurosci 2002;3:61728.
Internet Addiction among Adolescents May Predict Self-Harm/Suicidal Behavior: A Prospective Study FLA 5.5.0 DTD ■ YMPD9752_proof ■ March 15, 2018
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THE JOURNAL OF PEDIATRICS • www.jpeds.com 21. Lee YS, Han DH, Yang KC, Daniels MA, Na C, Kee BS, et al. Depression like characteristics of 5HTTLPR polymorphism and temperament in excessive Internet users. J Affect Disord 2008;109:165-9. 22. Pani PP, Maremmani I, Trogu E, Gessa GL, Ruiz P, Akiskal HS. Delineating the psychic structure of substance abuse and addictions: should anxiety, mood and impulse-control dysregulation be included? J Affect Disord 2010;122:185-97. 23. Mueser KT, Drake RE, Wallach MA. Dual diagnosis: a review of etiological theories. Addict Behav 1998;23:717-34. 24. Lin IH, Ko CH, Chang YP, Liu TL, Wang PW, Lin HC, et al. The association between suicidality and Internet addiction and activities in Taiwanese adolescents. Compr Psychiatry 2014;55:504-10. 25. Kim K, Ryu E, Chon MY, Yeun EJ, Choi SY, Seo JS, et al. Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey. Int J Nurs Stud 2006;43:185-92. 26. Lam LT, Peng Z, Mai J, Jing J. The association between Internet addiction and self-injurious behavior among adolescents. Inj Prev 2009;15:4038. 27. Kaess M, Durkee T, Brunner R, Carli V, Parzer P, Wasserman C, et al. Pathological Internet use among European adolescents: psychopathology and self-destructive behaviors. Eur Child Adolesc Psychiatry 2014;23:1093-102. 28. Fu KW, Chan WS, Wong PW, Yip PS. Internet addiction: prevalence, discriminant validity and correlates among adolescents in Hong Kong. Br J Psychiatry 2010;196:486-92. 29. Chen SH, Weng LJ, Su YJ, Wu HM, Yang PF. Development of a Chinese internet addiction scale and its psychometric Study. Chin J Psychol 2003;45:279-94. 30. Ko CH, Yen JY, Yen CF, Chen CC, Yen CN, Chen SH. Screening for Internet addiction: an empirical study on cut-off points for the Chen Internet Addiction Scale. Kaohsiung J Med Sci 2005;21:545-51. 31. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989;28:193-213. 32. Tsai PS, Wang SY, Wang MY, Su CT, Yang TT, Huang CJ, et al. Psychometric evaluation of the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) in primary insomnia and control subjects. Qual Life Res 2005;14:1943-52. 33. Megdal SP, Schernhammer ES. Correlates for poor sleepers in a Los Angeles high school. Sleep Med 2007;9:60-3. 34. Beck AT, Steer RA, Brown GK. BDI-II: beck depression inventory manual. 2nd ed. San Antonio: Psychological Corporation; 1996. 35. Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol 1988;56:8937. 36. Development of the World Health Organization WHOQOL-BREF quality of life assessment. The WHOQOL Group. Psychol Med 1998;28:551-8. 37. Yao G, Chung CW, Yu CF, Wang JD. Development and verification of validity and reliability of the WHOQOL-BREF Taiwan version. J Formos Med Assoc 2002;101:342-51. 38. Cummins RA. On the trail of the gold standard for subjective wellbeing. Soc Indic Res 1995;35:179-200.
Volume ■■ 39. Wang L, Luo J, Bai Y, Kong J, Luo J, Gao W, et al. Internet addiction of adolescents in China: prevalence, predictors, and association with wellbeing. Addict Res Theory 2012;21:62-9. 40. Ko CH, Yen JY, Yen CF, Chen CS, Wang SY. The association between Internet addiction and belief of frustration intolerance: the gender difference. Cyberpsychol Behav 2008;11:273-8. 41. Dong G, Zhou H, Zhao X. Impulse inhibition in people with Internet addiction disorder: electrophysiological evidence from a Go/NoGo study. Neurosci Lett 2010;485:138-42. 42. Cheng C, Sun P, Mak KK. Internet addiction and psychosocial maladjustment: avoidant coping and coping inflexibility as psychological mechanisms. Cyberpsychol Behav Soc Netw 2015;18:539-46. 43. Li H, Wang J, Wang L. A survey on the generalized problematic internet use in chinese college students and its relations to stressful life events and coping style. Int J Ment Health Addict 2009;7:333-46. 44. Yao YW, Chen PR, Li S, Wang LJ, Zhang JT, Yip SW, et al. Decisionmaking for risky gains and losses among college students with Internet gaming disorder. PLoS ONE 2015;10:e0116471. 45. Seok JW, Lee KH, Sohn S, Sohn JH. Neural substrates of risky decision making in individuals with Internet addiction. Aust N Z J Psychiatry 2015;49:923-32. 46. Yee N. Motivations for play in online games. Cyberpsychol Behav 2006;9:772-5. 47. Ozcan NK, Buzlu S. Internet use and its relation with the psychosocial situation for a sample of university students. Cyberpsychol Behav 2007;10:767-72. 48. Casale S, Fioravanti G. Psychosocial correlates of Internet use among Italian students. Int J Psychol 2011;46:288-98. 49. Yen JY, Yen CF, Chen CC, Chen SH, Ko CH. Family factors of Internet addiction and substance use experience in Taiwanese adolescents. Cyberpsychol Behav 2007;10:323-9. 50. Wang H, Zhou X, Lu C, Wu J, Deng X, Hong L. Problematic Internet Use in high school students in Guangdong Province, China. PLoS ONE 2011;6:e19660. 51. Lam LT, Peng ZW, Mai JC, Jing J. Factors associated with Internet addiction among adolescents. Cyberpsychol Behav 2009;12:551-5. 52. Yen CF, Ko CH, Yen JY, Chang YP, Cheng CP. Multi-dimensional discriminative factors for Internet addiction among adolescents regarding gender and age. Psychiatry Clin Neurosci 2009;63:357-64. 53. Milani L, Osualdella D, Di Blasio P. Quality of interpersonal relationships and problematic Internet use in adolescence. Cyberpsychol Behav 2009;12:681-4. 54. Platt B, Cohen Kadosh K, Lau JY. The role of peer rejection in adolescent depression. Depress Anxiety 2013;30:809-21. 55. Sebastian C, Viding E, Williams KD, Blakemore SJ. Social brain development and the affective consequences of ostracism in adolescence. Brain Cogn 2010;72:134-45. 56. Hawton K, Saunders KE, O’Connor RC. Self-harm and suicide in adolescents. Lancet 2012;379:2373-82. 57. Musetti A, Cattivelli R, Giacobbi M, Zuglian P, Ceccarini M, Capelli F, et al. Challenges in internet addiction disorder: is a diagnosis feasible or not? Front Psychol 2016;7:842.
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