The relationship between impulsivity and Internet addiction in a sample of Chinese adolescents
The relationship between impulsivity and Internet addiction in a sample of Chinese adolescents
Previous studies regarding Internet addiction have investigated associated psychological variables such as shyness, loneliness, self-consciousness, anxiety, depression and interpersonal relations. Few studies about the relationship between Internet Addiction and impulsivity have been done. This study aimed to assess whether Internet addiction is related to impulsivity among Chinese adolescents.
This study was performed in two stages. We screened for the presence of Internet Addiction among 2620 high school students(age ranging from 12 years to 18 years) from four high schools of Changsha City using Diagnostic Questionnaire for Internet Addiction (YDQ). According to the modified YDQ criteria by Beard, 64 students were diagnosed as Internet addiction. Excluding current psychiatric comorbidity, 50 students who were diagnosed as Internet Addiction (mean age, 14.8 ± 1.4 years) and 50 normal students in Internet usage(mean age, 14.5 ± 1.8 years) were included in a case control study. The two groups were assessed using Barratt Impulsiveness Scale 11 (BIS-11) and behavioral measure of impulsivity (GoStop Impulsivity Paradigm).
Sixty-four students met the modified YDQ criteria by Beard, of whom 14 students suffered from comorbid psychiatric disorders, especially comorbid ADHD. The Internet Addiction group had significantly higher scores on the BIS-11 subscales of Attentional key, Motor key, and Total scores than the control group (P < 0.05). The Internet Addiction group scored higher than the control group on the failure to inhibit responses of GoStop Impulsivity Paradigm (P < 0.05). There was a significant positive correlation between YDQ scores and BIS-11subscales and the number of failure to inhibit responses of GoStop Impulsivity Paradigm.
This study suggests that adolescents with Internet addiction exhibit more impulsivity than controls and have various comorbid psychiatric disorders, which could be associated with the psychopathology of Internet addiction.
Keywords: Internet; Disorders of environmental origin; Behavior; Addiction; Impulsivity; Adolescents
Abbreviations: YDQ, Diagnostic Questionnaire for Internet Addiction; BIS-11, Barratt Impulsiveness Scale 11; GoStop, GoStop Impulsivity Paradigm
The use of the Internet has increased considerably over the last few years. Data from China Internet Network Information Center (CNNIC), as of June 30, 2006, showed that 123 million people had gone online, of which 14.9% were teenagers below 18 years old. With this soaring number of Internet users, the problem of Internet addiction has attracted high attention from psychiatrists, educators, and the public. Internet addiction is currently becoming a serious mental health problem among Chinese adolescents. Chou and Hsiao reported that the incidence rate of Internet Addiction among Taiwan college students was 5.9% . Wu and Zhu identified 10.6% of Chinese college students as Internet addiction .
Internet addiction, also described as pathological Internet use, is defined as an individual’s inability to control his or her use of the Internet, which eventually causes psychological, social, school, and/or work difficulties in a person’s life  and . The description of Internet addiction has been based on the definition for substance dependence or pathological gambling. It shares characteristics like preoccupation, mood modification, tolerance, withdrawal, and functional impairment  and .
Noticeably, Internet addiction is also a problem that has been observed in different cultures. Yoo et al. conducted a study in which 535 Korean children were assessed. This study found significance associations between attention deficit hyperactivity disorder (ADHD) symptoms and the severity of Internet addiction . A survey carried out by Morahan-Martin and Schumacher reported that among 277 U.S. college students pathological Internet users were more likely to be males and to use online games as well as technologically sophisticated sites . With a Chinese Internet-related addictive behavior Inventory version II and Diagnostic Questionnaire for Internet Addiction (YDQ), Chou and Hsiao explored Internet addiction in 910 Taiwanese college students and identified the high communication pleasure score as a high predictor for Internet dependence .
Since the initial recognition of Internet addiction, several assessment instruments have been developed. Brenner developed an Internet Related Addictive Behavior Inventory (IRABI) , which has 32 true–false questions that assess users’ Internet experiences. Morahan Martin and Schumacker introduced their scale PIUS (Pathological Internet Use Scale) , with 13 questions to assess whether heavy Internet use negatively affects academic and other work, interpersonal relations, individual stress levels, social withdrawal, and mood alteration. In 2002, Caplan published the Generalized Problematic Internet Use Scale (GPIUS) . In addition, a widely-used eight-item Internet Addiction Diagnostic Questionnaire (YDQ) was developed by Young , partly adapted from DSM-IV criteria for pathological gambling. Young also created a 20-item questionnaire, called the Internet Addiction Test (IAT) . Up to now, there exists yet no Internet Addiction diagnosis in the DSM system.
By using the DSM-IV criteria, some authors suggest Internet addiction is an impulse disorder or at least related to impulse control  and . Several studies have revealed a correlation between impulsivity and pathological gambling, substance abuse, and alcohol abuse. Barnes et al. found that impulsivity was a significant predictor of alcohol misuse for females and delinquency for males . Vitaro et al. used a prospective-longitudinal design to investigate whether impulsivity measured in 12–14-year-olds could predict problem gambling in late adolescence. They validated “a self-report measure of impulsiveness and a card-sorting task that significantly predicted problem gambling (even after controlling for socio-demographic variables), early gambling behavior and other personality variables such as aggressiveness and anxiety” . Moelle et al. found that impulsivity is a significant predictor of cocaine use and treatment retention . Cavedini et al. also explored the relationship between the ventromedial orbitofrontal circuits and pathological gambling. The results suggest the existence of a link between pathological gambling and drug addiction, all having diminished ability to consider future consequences, which may be explained, at least in part, by abnormal functioning of the orbitofrontal cortex .
Research into impulsivity found that pathological gambling, drug addiction, and alcohol abuse have similarities in neuropsychology and personality characteristic. If Internet addiction is related to impulsivity, research is likely to demonstrate that neuropsychological characteristics may be similar to other disorders.
Previous studies regarding adolescent Internet addiction have investigated associated psychological variables such as shyness, loneliness, self-consciousness, anxiety, depression and interpersonal relations. However, few studies about the relationship between Internet Addiction and impulsivity have been done.
The objective of this study was to assess whether Internet addiction is related to impulsivity.
This study was performed in two stages. First we screened for the presence of Internet addiction within a nonclinical group using the Diagnostic Questionnaire for Internet Addiction (YDQ) that has been commonly used. According to the modified YDQ criteria by Beard and structured clinical interview (excluding current psychiatric comorbidity), 50 students who were diagnosed as Internet addicted (mean age, 14.8 ± 1.4 years) and 50 students on normal Internet usage (mean age, 14.5 ± 1.8 years) were included in a case–control study. The two groups were assessed using Barratt Impulsiveness Scale-11 (BIS-11) and behavioral measure of impulsivity (GoStop Impulsivity Paradigm).
Four high schools were selected randomly in Changsha City (the capital of Hunan, in South-central China, a city with a population of over 6 million). Over all four schools, we then adopted a 2-stage sampling method to select 3 classes respectively from four grades, namely, 1st grade (mean age, 13.1 years) and 2nd grade (mean age, 14.3 years) in junior high schools and 1st grade (mean age, 16.5 years) and 2nd grade (mean age: 17.1 years) in senior high schools. From the selected classes, all students (a total of 2787 students) participated in this study and completed the self-report questionnaires in class after the researchers had explained the procedures and requirements. Questionnaires were collected immediately after they were completed. A final 2620 eligible questionnaires remained. The response rate was 94%.
Among the 2620 students, 100% were aged between 12 and 18 years, with the average age being 15.2 ± 3.5 years. A summary of the participant characteristics is given in Table 1.
School approval and parental consent were obtained before participation in the study. Investigators visited schools, explained the purpose of the study to students and teachers, and also informed the parents the objective of the study, a guarantee of confidentiality, and a contact telephone number of the prime investigator for any questions and concerns by sending a letter. All parents were assured that they were free to refuse if they did not agree with the objective of the study. The research project has been approved by Ethics Committee of the Second Xiangya Hospital.
2.2. Group selection
Internet addicted individuals who met the modified YDQ criteria were administered a structured clinical interview (K-SADS) for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Participants who evidenced any DSM-IV axis I disorder were not included. The diagnosis was made on the basis of clinical observation and history provided by the parents and the patients themselves. Finally, 50 students who were diagnosed as Internet addicted were included in the case group. Fifty normal students were matched by gender, age and educational levels from among the lists of students with the same age, gender, level of education and class. Independent t-tests showed that age and educational levels did not differ between the Internet Addiction group and the control group (P > 0.05). Chi-square test indicated no differences between the groups in distribution of gender (P > 0.05).
2.3.1. Basic Information Questionnaire
We used the Basic Information Questionnaire to collect demographic information such as age, gender, educational levels, and Internet experience.
2.3.2. Diagnostic Questionnaire for Internet Addiction (YDQ)
YDQ was adapted from DSM-IV criteria for pathological gambling by Young . YDQ consisting of eight “yes” or “no” questions was translated into Chinese. It includes the following questions: (1) Do you feel preoccupied with the Internet (think about previous online activity or anticipate next online session)? (2) Do you feel the need to use the Internet with increasing amounts of time in order to achieve satisfaction? (3) Have you repeatedly made unsuccessful efforts to control, cut back, or stop Internet use? (4) Do you feel restless, moody, depressed, or irritable when attempting to cut down or stop Internet use? (5) Do you stay online longer than originally intended? (6) Have you jeopardized or risked the loss of significant relationship, job, educational or career opportunity because of the Internet? (7) Have you lied to family members, therapist, or others to conceal the extent of involvement with the Internet? (8) Do you use the Internet as a way of escaping from problems or of relieving a dysphoric mood (e.g., feelings of helplessness, guilt, anxiety, or depression)? Young asserted that five or more yes responses to the eight questions indicate a dependent user. Beard modified the YDQ criteria. Respondents who answered “yes” to questions 1 through 5 and at least any one of the remaining three questions were classified as suffering from Internet addiction . Beard stated that the modification may help strengthen Young’s proposed criteria. In previous publications about YDQ, the split-half reliability was 0.729 and the Cronbach * was 0.713 . In our study, the calculation of a Spearman–Brown coefficient resulted in a split-half reliability of 0.719.The consistency of the YDQ was tested with Cronbach’s * 0.722. Thus, the YDQ has a good reliability and consistency.
2.3.3. Barratt Impulsiveness Scale 11 (BIS-11)
BIS-11 is a questionnaire on which participants rate their frequency of several common impulsive or nonimpulsive behaviors/traits on a scale from 1 (rarely/never) to 4 (almost always/always). The 11th version of the BIS consists of 30 items and can be divided into three subscales including attentional key, motor key, non-planning key, to determine overall impulsiveness scores, all items were summed, with higher scores indicating greater impulsivity . We used the translated version by Li et al.; its split-half reliability was 0.752, the test–retest reliability was 0.825, and the Cronbach * coefficient was 0.794 .
2.3.4. GoStop Impulsivity Paradigm (GoStop)
GoStop is a response disinhibition procedure for assessing the capacity to inhibit an already initiated response. For the GoStop, subjects are shown a series of 5-digit numbers in black on a computer screen. The randomly generated 5-digit numbers appear for 500 ms, once every 2 s (500 ms on, 1500 ms off). Participants are told to respond when the number they see is identical to the previous number; this is a target trial. Half of all target trials feature a target-stop trial, when the color of the matching target’s numerals changes from black to red at 50, 150, 250, or 350 ms after its presentation. Participants are instructed to respond to the identically matching numbers before the number disappears from the screen, but not to respond to a number that turns red. Target and target-stop trials each occur 25% of the time. The remaining 50% of the trials consist of numbers that differ randomly from the previous number. Within the target-stop trials, the interval the target remains black (go) before turning red (stop) has an equal probability of a 50, 150, 250, or 350 ms duration .
The failure to inhibit responses was used as an indicator of ability to withhold responses when presented with a stop-signal. The number of target-stop stimuli that the participant did not respond to was used to calculate the number of failure to inhibit responses from the total presentations for each delay condition.
A computer with a 14-inch monitor was used to administer GoStop. Participants were asked to use their dominant hand when responding on the task. They received standardized task instructions before the computer tasks. All subjects completed one session of GoStop task.
2.4. Statistical analysis
Group differences in demographic variables were computed using independent t-test and chi-square test. The differences in BIS-11 and GoStop scores between Internet addiction group and the control group were analyzed using independent t-test. The correlation between Internet addiction and BIS-11 and GoStop scores was assessed using Pearson’s correlations analysis. Statistical significances were defined at the 0.05 level, two-tailed.
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