INTRODUCTION

The concept of internet addiction (IA) was first described academically by Young (1), proposing symptoms such as excessive occupation with the internet, a feeling of need to use the internet, repeated attempts at stopping internet use, a loss of importance of times spent away from the internet, spending more time than planned on the internet, feeling excessively uneasy when away from the internet, potentially creating social problems. A number of terms are used to describe negative effects of internet use, such as problematic internet use (2), compulsive internet use (3), pathologic internet use (4), internet-related problems (5), and IA (1). In chapter 3 of the DSM-5, which includes conditions requiring further clinical research and experience, internet gaming disorder has been included (6). In this article, we will use the term IA.

While internet use is spreading rapidly, IA can lead to serious problems in children and adolescents whose physical and psychological development is not yet complete (7). According to data from the Turkish Statistical Institute (TUIK) for the year 2013, among persons between 16 and 74 years of age, 39.5% describe themselves as regular internet users. TUIK reports that males in all age groups are using the internet more, and the most frequent users are individuals between 16 and 24 years of age (8). The prevalence of IA among adolescents in Europe (1-9%) and the Middle East (1-12%) is at a similar level, while for Asia (2-18%) a higher level has been reported (9). According to studies carried out in Turkey, IA prevalence among young persons ranges between 1.2 and 24.2% (10-15).

In more than half the adolescent IA cases, another psychiatric disorder is reported as comorbidity (16). In young individuals, IA is often seen together with other psychiatric disorders such as attention deficit hyperactivity disorder (ADHD), depression, social anxiety disorder, pathologic addiction to gambling, and substance use disorder (9,17,18). Many studies show an association between IA and ADHD in children and adolescents (17-20).

The association of IA with tobacco, alcohol, and substance use is another topic being dwelled upon (21,22). According to problem-behavior theory, social environment, perceived environment, and psychological predispositions such as personality and behavior are components of the psychosocial framework influencing the development of problems such as alcohol, tobacco, and substance use (22). It has been emphasized that from this theoretical point of view, IA can be grouped together with other problem behaviors (22).

In the trilemma of IA, ADHD, and addiction, some questions in important areas such as the cause-effect relation, specific risk groups, risk factors or protective measures are still waiting to be answered. This study examines the relations between IA, ADHD, and tobacco/alcohol use/experimentation one by one. Our basic hypothesis is that in cases with IA, ADHD and alcohol/tobacco use/experimentation will be more common. This is the first study researching the association of IA, ADHD, and tobacco/alcohol use/experimentation among adolescents living in the province of Zonguldak. It is also the first study in Turkey to use the Chen Internet Addiction Scale in the assessment of IA and the first one to assess the relations between IA, ADHD, and tobacco/alcohol use/experimentation individually.

METHOD

To recruit the study sample, a total of three high schools in the province were determined, 2 out of the 9 with admission by exam and 1 out of the 3 not requiring an entrance exam, using the stratified cluster sampling method. Targeted were all 796 students enrolled in grades 9, 10, and 11. The Provincial Directorate for National Education decided that it would not be appropriate to include students from the last grade preparing for the university entrance exam. Participation in the study was entirely voluntary. As 25 students (3.1%) refused to participate, the sample consisted of 771 students. The study was carried out in April 2011. Approval from the university ethics committee and the Provincial Directorate for National Education was obtained. All students were informed about study method and scales to be applied; they gave their written assent with a consent form. A sociodemographic data form prepared by the researchers, the Chen Internet Addiction Scale (CIAS) (24-26), and the Adult Attention Deficit Disorder/Attention Deficit Hyperactivity Disorder (ADD/ADHD) Diagnostic and Assessment Inventory based on the DSM-IV (27) were administered to the participants in a single session in randomized order. In the classroom environment, completing all forms and scales took around 30-35 minutes. Of the students who had agreed to participate, 771 (100.0%) filled in the CIAS, 764 (99.1%) the Adult ADD/ADHD Diagnostic and Assessment Inventory based on the DSM-IV completely; hence, scores of these cases were used for the analyses.

Measures

Data were collected using a sociodemographic data form, CIAS, and the Adult ADD/ADHD Diagnostic and Assessment Inventory based on the DSM-IV.

Sociodemographic Data Form: The form prepared by the researchers included questions about the student’s age, sex, grade, parents’ education, availability of internet access at student’s home, and the student’s tobacco/alcohol experimentation/use status.

Chen Internet Addiction Scale (CIAS): The original version of the scale was prepared for a study by Chen et al. (24), based on DSM-IV scales for substance abuse and addiction. It consists of 26 items and uses a Likert-type scale with 1-4 points. Total score ranges between 26 and 84 points. The CIAS includes five subscales, namely, compulsive use, withdrawal, tolerance, time management problems, and interpersonal and health problems (25). It has been reported that the CIAS is a useful scale to measure the intensity of IA in adolescents. Cut-off point in the original version was 57/58 for screening and 63/64 for diagnosis (25). A study to confirm the validity and reliability of the Turkish scale was undertaken by Kesici and Sahin (26).

Adult ADD/ADHD Diagnostic and Assessment Inventory based on the DSM-IV: The Adult ADD/ADHD Diagnostic and Assessment Inventory based on the DSM-IV was developed by Turgay. It consists of three subsections: Attention deficit (AD), Hyperactivity (HA), and attributes and problems relating to ADHD. In assessing the scale, 0 and 1 points are valued as negative, 2 and 3 points as positive. In the assessment made with this inventory, cases meeting at least 6 criteria in the AD and/or the HA section were counted as meeting the diagnostic criteria for ADHD. Higher scores demonstrate a more severe psychopathology. Adult ADHD Scale was developed by A. Turgay in Canada in 1995 and was also translated to Turkish by him. Validity and reliability of the scale in Turkish were confirmed in a study by Gunay et al. (27). Parent attitude was assessed, based on questions in the scale, under the 4 categories of autocratic-oppressive, democratic, over-protective, and indifferent.

Statistical Analysis

Data obtained in the study were statistically analyzed using the SPSS 17 for Windows package. According to the Kolmogorov-Smirnov test, not all variables were normally distributed; hence non-parametric statistical methods were used. For the comparison of quantitative data, Mann-Whitney U (MW-U) test was used. The Chi-square test was used to establish if there were inter-group differences regarding categorical variables. Spearman correlation analysis was carried out to determine relations between scales. In the tables, numerical variables are expressed as mean±standard deviation (mean±SD) and median (minimum-maximum), categorical variables are given as number of observations (n) and percentage (%). Significance level was set at p<0.05.

RESULTS

Our study included 771 students from three different high schools, with a mean age of 16.9±0.9 (14-19), 311 (40.3%) male, 460 (59.7%) female. Of these, 296 (38.4%) were enrolled in the 1st grade of senior high school, 272 (35.3%) in the 2nd grade, and 203 (26.3%) in the 3rd grade. Maternal education level was found to be primary school and below in 332 cases (43.3%), while paternal education level in 354 cases (46.8%) reached middle school or high school. Parent attitude was defined as democratic in 500 cases (66.8%), over-protective in 162 cases (21.7%), autocratic-oppressive in 78 cases (10.4%), and indifferent in 8 cases (1.1%). 576 cases (74.9%) stated that they had internet access at home. Tobacco use/experimentation was found in 167 cases (21.7%), alcohol use/experimentation in 98 cases (12.7%).

In the comparative analyses, cases were categorized into two groups, those above the cut-off point specified in the original CIAS (57/58) classed as addicted, those below as non-addicted. 61 cases (7.9%) were included in the “addicted” group. Table 1 shows the sociodemographic variables according to the presence of IA. In the addicted group, male sex is dominant, and the ratio of those with internet access at home is higher than among those with no internet access (Table 1). In the addicted group, autocratic-oppressive, indifferent, and over-protective parent attitudes were found at a higher rate than in the non-addicted group (Table 1). The education level of the parents did not differ between the groups.

In the assessment made with the Adult ADD/ADHD Diagnostic and Assessment Inventory based on the DSM-IV, cases meeting at least 6 criteria in the AD and/or the HA section were counted as meeting the diagnostic criteria for ADHD. According to this inventory, ADHD was found in 90 cases (11.7%). The scale scores according to the gender are shown in Table 2. Of the girls with ADHD, 9 (40.9%) fell into the IA group, of the boys 13 (59.1%) (p=0.179). The mean CIAS score of the ADHD cases (44.8±15.9) was significantly higher than the score of the non-ADHD cases (37.3±10.6) (p<0.001).

ADHD and alcohol/tobacco use according to the presence of IA is shown in Table 3. Mean CIAS score in cases with a history of tobacco use/experimentation (41.0±12.5) was significantly higher than in cases without such a history (37.4±11.2) (p<0.001). Mean CIAS score in cases with alcohol use/experimentation history (43.4±12.3) was higher than in cases without such a history (37.6±11.4) (p<0.001).

Between ADHD scale total score and CIAS total score, Spearman correlation analysis established a significant positive correlation (r=0.38, p<0.001). In girls and boys, Spearman correlation analysis established a significant positive correlation in girls and boys (r=0.36, p<0.0001; r=0.45, p<0.001, respectively).

DISCUSSION

While the result of our study examining IA alongside ADHD and tobacco-alcohol use/experimentation supports our hypothesis by showing a significantly elevated rate of ADHD in the presence of IA, it is worth noting that the same correlation was not found for tobacco and alcohol use/experimentation.

In our high school sample, we found an IA rate of 7.9%, ADHD 11.7%, tobacco use/experimentation 21.7%, and alcohol use/experimentation 12.7%. Studies carried out with adolescents report various IA rates, depending on the methods of measurement applied. Studies applying CIAS determined IA rates in adolescents between 7.5 and 19.4% (28,29). Two studies conducted with high school samples in Turkey found IA rates of 6.6% and 24.2%, respectively (13,30). A study done among adolescents in seven European countries found that 13.9% displayed non-functional internet use (31). The IA rate in our sample was lower than in the Far Eastern societies where the scale was developed but close to the rates found in Western countries.

While the majority of studies reports IA to be more common in males (13,32), there are also studies that have not found any difference in IA by gender (33). Our results support findings suggesting that IA is found more commonly in males.

Many studies have shown the association of IA with individual, familial, and environmental risk factors (9,29). Our finding that IA rates are higher in cases with internet access from home show parallels with publications stating that internet availability is a risk factor for IA (29,30). In the modality of children’s use of internet and other media, families are playing an important role. Parents’ knowledge about the risks of abusing media tools is the most determining factor for the prevention of addiction (34). While there are studies that, similar to our results, did not find a correlation between the parents’ level of education and IA (13,33), there are other studies that found a lower education level of fathers in IA cases (35) or established a high paternal education level as a risk factor for IA (36). These contradictory results suggest that, rather than the high level of education, the level of knowledge specific to the issue may be more predictive. It can be expected that once knowledge and awareness have been achieved at a societal level, in societies where parents are generally sensitive to the IA risk and to protection from this danger, parent information can increasingly save children and adolescents from IA more effectively. On the other hand, we should not forget that in developing societies like in Turkey, this balance may be tipped in the other direction. Therefore, more comprehensive studies are needed to assess parents’ awareness levels and their attitude to children’s use of media tools in more detail.

In studies determining parents’ attitudes in IA cases, expressions such as “strict, inflexible” (37), “punitive, interfering-rejecting” (38), “over-controlling, inconsistent” (39), “less supportive, cold” (36), or “protective” (38) are frequently found. The fact that our study found unhealthy parent attitudes such as autocratic-oppressive, uncaring, and over-protective to be elevated in the IA group parallels results from other relevant studies. When assessing our findings, we need to consider that data about parent attitude is based on a single question, attitudes of the two parents have not been assessed individually, and the adolescents’ perception of their parents may not entirely reflect reality.

With regard to the frequency of addictive substance use reported in the literature, in conformity with our results, in developing countries tobacco is found in the first place (40), while in European countries alcohol is the leading substance (41). There are studies showing that alcohol use increases the IA risk (33) and that in a large proportion of IA cases substance use experimentation is present (21,42). A study with adolescents in Korea reported that alcohol use did not constitute a risk for IA in the entire cohort, but in girls it was found as a risk, while in males, tobacco use was a risk factor for IA (35). Uneri et al. (13), analyzing a high school sample in Turkey, did not find a statistically significant effect of adolescents’ smoking on IA scores. From the literature, it has become clear that there is a relationship between increased novelty-seeking behavior and adolescent substance use (43) and IA (42); at the same time, novelty/pleasure seeking and low reward dependence create a predisposition for IA (29). While in our study mean CIAS scores in cases with tobacco or alcohol use/experimentation were found to be elevated, the level of tobacco or alcohol use/experimentation rates in IA cases was not statistically significant. We think that our results may have been affected by the fact that alcohol and cigarette use are socially proscribed behaviors, inducing especially adolescents with problematic behavior to conceal their cigarette and alcohol use.

It has been reported that in ADHD cases IA is seen more frequently; that the presence of ADHD indicates a higher addiction score and, according to some studies, that the most commonly found comorbidity in IA is ADHD (17,18,20). While most studies on IA are cross-sectional, one research following adolescents for 2 years indicated that one of the strongest risk factors for IA is ADHD (44). One study made with a high school sample in Turkey found an association between attention deficit and hyperactivity symptoms and the IA score (30). In our research, the ADHD rate in increased scores in IA cases, the significant positive correlation between CIAS and ADHD scores and the elevated CIAS scores in ADHD cases are consistent with results from other studies assessing the association of ADHD and IA.

IA rates in girls and boys with ADHD were not different, and the significant positive correlation between CIAS and ADHD scores was valid for both sexes. Aside from studies reporting that the association of ADHD and IA is more prominent in female students (44), there are also studies stating, consistent with our results, that ADHD is correlated with both sexes (18).

Regarding subtypes of ADHD found in our IA cases, subtype HA and combined type were found at the same rate and more common than the type dominated by AD (Table 3). In a study made with adolescents in China, a significant positive correlation was found between IA scores and impulsivity and attention scores (19). In the literature, ADHD is often characterized by increased reward sensitivity, getting bored easily (45), preference for fast changing and quickly response-producing stimuli, orientation towards smaller, short-term rewards rather than long-term rewards, impulsivity (19) and disinhibition (46). It has been suggested that these characteristics are sufficiently catered to by internet use or that self-control deficits intrinsic to ADHD may increase the predisposition for IA (17,45). Furthermore, it has been proposed that IA might be a new subtype of impulse control disorder (47). Questions such as “is impulsivity a risk factor for IA or a result of IA, or can both hypotheses be valid?” still remain to be answered (48). A close association between IA and impulsivity is consistent with our findings that in IA cases ADHD subtype HA and combined type are more commonly found.

The most important limitations of our study are the exclusive reliance on self-report scales rather than clinical assessment and the undifferentiated assessment of tobacco-alcohol use and experimentation. Furthermore, when assessing IA, relevant factors such as duration, location, and purpose of use have not been considered. Given the cross-sectional design, causal relations between the variables included could not been assessed.

In the high school sample, assessment with CIAS showed, consistent with the literature, that IA is more prevalent in youths with ADHD. Though tobacco-alcohol use/experimentation rates were found to be statistically similar in youths with and without IA, CIAS scores in adolescents with tobacco-alcohol use/experimentation were higher than in those not using tobacco or alcohol. We maintain that our results are important for the joint assessment of IA, ADHD, and tobacco-alcohol use/experimentation and the establishment of a close association between each pair of these three states. There is a need for future follow-up studies to elucidate the mechanisms behind the association between IA, ADHD, and tobacco-alcohol use and the causality of these relations.

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