The Relationship Between Internet Addiction and Body Mass Index in Turkish Adolescents

Abstract

The purpose of this study was to investigate Internet use patterns and Internet addiction among adolescents and to examine the correlation between Internet addiction and eating attitudes and body mass index (BMI). The study was conducted among 1,938 students, aged between 14 and 18 years. The Internet Addiction Test (IAT), the Eating Attitudes Test (EAT), and a sociodemographic query form were used to collect data. According to the IAT, 12.4% of the study sample met the criteria for Internet addiction. A significant positive correlation between BMI and the IAT (r=0.307; p<0.01) and weekly Internet use (r=0.215; p<0.01) was found. Nine students with Internet addiction (3.8%) and 90 with average Internet use (5.3%) were found to have a possible eating disorder (p>0.05). No relationship was found between the EAT and the IAT and duration of weekly Internet use. Linear regression analysis revealed a significant independent association of the IAT with BMI (r=0.235; p<0.001). These results indicate an association between Internet addiction and BMI. Further studies are needed to describe the causality of this association.

Introduction

Obesity is a major public health problem globally, and the prevalence of obesity is increasing in developed and developing countries.^1,2 Increases in the prevalence of adolescent overweight in recent decades are associated with health and social consequences (e.g., educational attainment, marital status, household income).^3–5 The rapid rise in prevalence suggests environmental factors are responsible.⁶ Physical activity has declined, sedentary activities have increased, and widespread changes in dietary patterns have taken place. Increased use of information and communication technology is also reported to be associated with increased rates of obesity.^7,8 Internet use leads to less physical activity, which, in turn, leads to obesity.⁹ Moreover, high levels of disordered eating have been reported among individuals with Internet addiction compared to controls.¹⁰ Changes in eating attitudes caused by disproportionate Internet use may indirectly cause obesity.

Excessive use of the Internet has been a topic of discussion in the academic literature for more than a decade. Although the debate over whether Internet use–related problems should be classified as an addiction, an impulse-control disorder, or an obsessive–compulsive disorder continues,¹¹ there is strong overlap of the symptoms mainly associated with behavioral addictions,¹² and neurological similarities with other addictions.¹³ A considerable amount of literature published on Internet addiction reveals that it is a worldwide phenomenon.¹⁴ Prevalence statistics of Internet addiction in adolescents vary widely from 2%¹⁵ to 20%¹⁶ across cultures and societies. Although a standardized definition has not been uniformly agreed upon, it is generally recognized that Internet addiction involves an individual's inability to control his or her use of the Internet, negative consequences (e.g., failing in school, decreased productivity), and marked distress and/or functional impairment.^17,18 Individuals with an Internet addiction are reported to neglect not only household or work responsibilities but also physical activity.¹⁹ The diminished physical activity caused by Internet use may result in an increase in weight. Given wide recognition of the potential misuse of the Internet, determining whether Internet addiction causes obesity is valid.²⁰

Many studies have analyzed the impact of television viewing on obesity.^21,22 For example, in a community-based longitudinal cohort study,²³ offspring who watched TV for ≥3 hours per day at 14 years had a greater body mass index (BMI) at 21 years. Nonetheless, the association of excessive Internet use with BMI remains largely unknown. A cross-sectional study⁸ (n=6,515) and a 1-year longitudinal cohort study²⁴ (n=5,502) showed that adolescents who spent more recreational time on the Internet were more likely to be overweight. Interestingly, adults with high leisure-time Internet use were reported to be more likely to be overweight or obese even if they were highly active during their leisure time, compared to participants who did not use the Internet.²⁵ These studies evaluated the relationship between BMI and the duration of Internet use rather than Internet addiction. However, common hedonic mechanisms may underlie obesity and addiction.²⁶ Furthermore, neurobiological research has shown similarities in brain reward processes between obesity and substance abuse disorders.²⁷ Thus, the relationship between Internet use and BMI should be examined from the addiction perspective as well.

Our first goal was to determine the frequency of Internet addiction in an adolescent sample. Second, we examined the relationship among Internet addiction, eating attitudes, and BMI in a large nonclinical population, namely, Turkish high school students.

Methods

Participants

This study is part of a research project that aims to investigate Internet addiction prevalence and its relationship with sociodemographic and psychological variables among Turkish high school students. A cross-sectional design was applied for the purposes of the study. The target population was all high school studens in Duzce city center (2,216 students). The study was carried out at 10 high schools (eight public and two private schools). A total of 2,029 high school students from grades 9 to 11 in 87 classes participated voluntarily as participants for the study. After oral information and consent, all participants were asked to complete the Internet Addiction Test, Eating Attitudes Test, and sociodemographic information form while at a lecture room in school. The students provided their responses while in a counseling course class. Ninety-one students were excluded from the study because of missing data resulting in an available sample of 1,938 students (87.5% of the total sample).

The study was approved by the Committee of Duzce University Duzce Medical School Local Ethical Committee for Clinical and Laboratory Studies and by the Local National Educational Committee of Duzce Governorship.

Measures

Sociodemographic Information Form

We developed a 14-item sociodemographic questionnaire with items pertaining to age, sex, grade, height (cm), weight (kg), average monthly household income, regular exercising, and extent and type of Internet use (e.g., “How many hours do you spend online per week?”).

Body mass index

BMI (weight in kilograms/height in meters squared) was calculated from self-reported weight and height measurements. BMI categories employed in analyses were underweight (<18.5 kg/m²), normal weight (20–25 kg/m²); overweight (25–30 kg/m²); and obese (>30 kg/m²).²⁸

Internet Addiction Test (IAT)

The IAT, which is one of the first standardized tests for the assessment of disturbed Internet use, has been validated among adolescents and adults and is used globally.²⁹ It contains 20 items that ask respondents to rate how often they show such symptoms of problematic Internet use as excessive time spent online, neglect of daily routine tasks, disruption of academic or job performance, concealment of online time and behaviors from others, loss of sleep, social isolation, depressive feelings if usage is restricted, and failed attempts to cut down Internet use. Each response is measured on a 6-point Likert scale (not at all, rarely, occasionally, often, always, and does not apply). The possible total score for each respondent could range from 20 to 100, with higher scores indicating greater problems associated with Internet use. Scores of 20 to 39 represent “average” users; scores of 50 to 69 represent “occasional or frequent problems due to Internet”; scores greater than 70 are classified as “significant problems.”²⁹ The IAT has been shown to have a good validity and reliability with Turkish elementary and high school students (Cronbach's α=0.82).³⁰

Eating Attitudes Test (EAT)

The EAT is the most widely used self-report instrument for screening large populations for attitudes and symptoms characteristic of eating disorders.³¹ It contains 40 items with six possible answers for each statement ranging from “never” to “always.”³² The recommended cutoff is 30, and scores higher than 30 are frequently associated with abnormal eating attitudes and behavior.³³ The EAT was found to be a highly reliable measure with an internal consistency of 0.94 for a pooled sample of participants with anorexia nervosa and those in control groups. Savasir and Erol³⁴ demonstrated the validity of the Turkish version of the questionnaire in distinguishing eating disordered patients with other psychopathology groups and healthy controls. Factorial validity was shown in the population sample, and reliability coefficients of the Turkish version of the test were found to be high.

Statistical analysis

Statistical analysis was performed using the SPSS package (SPSS v16 for Windows; SPSS Inc., Chicago, IL). Student's two-tailed t test was used to compare the significance of the differences between two groups (e.g., females and males). Groups (according to IAT and BMI) were compared with a one-way ANOVA and between-group comparisons were performed using post hoc contrasts with a Bonferroni adjustment for multiple comparisons. The chi-square analyses were used to compare categorical variables. Pearson correlation analysis was used to determine the correlation coefficients between the variables. Linear regression analysis was utilized to assess the contributions of age, gender, average monthly income, regular exercising, Internet addiction, and eating attitude to BMI. The significance level was accepted as p<0.05 throughout the analyses.

Results

The sample consisted of 1,007 female students (52%) and 931 male students (48%) with a mean age of 16.05±1.01 years (range 14–18 years). Overall, 12.4% of the adolescents scored in the “occasional or frequent problems” (8.9%) or “significant problems” (3.5%) range on the IAT. The mean time spent online was 4.5±5.18 hours per week. Having a computer with an Internet connection was associated with increased BMI (t=2.752, p<0.01). However, having a computer without an Internet connection was not associated with BMI (t=1.088, p>0.05).

Sixty-two (6.2%) females and 111 (11.9%) males had occasional problems due to Internet use. In addition, 28 (2.8%) females and 39 (4.2%) males had significant problems, and the differences were statistically significant (p<0.001). According to the EAT, 53 females (5.3%) and 46 (4.9%) males may have had an eating disorder (p>0.05). A comparison of other variables between genders is presented in Table 1.

Table 1.

Mean Scores of Sociodemographic Variables, IAT, BMI, and EAT Between Genders

	Female (n=1,007) mean±SD	Male (n=931) mean±SD	p Value
Age (years)	16.0±1.0	16.1±1.0	0.100
Household income (TL/Month)	1,057.7±742.6	1,143.6±869.3	0.052
Weekly Internet use (hours)	3.8±4.9	5.1±5.3	0.000
IAT	28.3±14.5	32.3±16.6	0.000
BMI	20.1±2.5	21.1±2.7	0.000
EAT	12.1±8.3	11.3±8.7	0.018

BMI, body mass index; EAT, Eating Attitude Test; IAT, Internet Addiction Test; SD, standard deviation; TL, Turkish Lira.

As shown in Table 2, average monthly household income, duration of weekly Internet use, and BMI were statistically different in terms of the severity of Internet addiction.

Table 2.

Characteristics of the Study Population According to the Degree of Internet Addiction (n=1,938)

	Average user (n=1,698) mean±SD	Occasional problems (n=173) mean±SD	Significant problems (n=67) mean±SD	p Value
Age (years)	16.0±1.0	16.1±0.9	16.3±0.9	0.152
Household income (TL/Month)	1,066.2±736.9^a	1,413.3±938.6^b	1121.1±533.6	0.000
Weekly Internet use (hours)	3.2±2.6^c	11.3±5.4^b	20.6±10.2^d	0.000
IAT	25.3±7.9^c	57.1±6.1^b	84.3±8.4^d	0.000
BMI	20.4±2.6^c	22.0±2.8^b	22.9±3.5^b	0.000
EAT	11.7±8.6	11.8±7.8	12.8±8.2	0.574

Pairwise comparisons: ^ap<0.05 versus occasional problems; ^bp<0.05 versus average user; ^cp<0.05 versus occasional problems and significant problems; ^dp<0.05 versus avarage user and occasional problems.

Among 1,938 students, 16 (0.8%) were obese, 75 (3.9%) were overweight, 1,574 (81.2%) were normal weight, and 273 (14.1%) were underweight. As shown in Table 3, average monthly household income, duration of weekly Internet use, and IAT and EAT scores were statistically different in terms of BMI categories.

Table 3.

Characteristics of the Study Sample by Body Mass Index Category (n=1,938)

	Underweight^† (n=273) mean±SD	Normal^† (n=1,574) mean±SD	Overweight^† (n=75) mean±SD	Obese^† (n=16) mean±SD	p Value
Age (years)	15.9±0.9	16.1±1.0	16.2±0.9	15.8±0.9	0.058
Household income (TL/Month)	1,003.8±526.8^a	1,088.23±786.0^a	1,520.18±1,381.8^b	1,682.1±1,675.4^b	0.000
Weekly Internet use (hours)	4.1±4.8^a	4.3±4.8^a	9.7±9.2^b	6.4±4.5^b	0.000
IAT	27.9±13.6^a	29.6±14.8^a	49.5±25.0^b	39.8±20.1^b	0.000
BMI	16.9±0.3	20.8±1.9	26.3±1.1	31.2±15.5	0.000
EAT	11.6±8.6^c	11.6±8.5^c	14.6±7.4^b	15.56±9.6	0.006

†

18.5>BMI, underweight; 18.5<BMI<25, normal; 25<BMI<30, overweight; 30<BMI, obese.

Pairwise comparisons: ^ap<0.05 versus overweight and obese, ^bp<0.05 versus normal and underweight, ^cp<0.05 versus overweight.

According to the EAT, 99 (5.1%) of the participants had a possible eating disorder. Nine students with Internet addiction (3.8%) and 90 with average Internet use (5.3%) had disordered eating (p>0.05). The relationship between Internet addiction and BMI categories and presence of a possible eating disorder is presented in Table 4.

Table 4.

The Comparison of Internet Addiction with BMI Categories and Presence of a Possible Eating Disorder According to the EAT

		Average user (n=1,698)	Occasional problems (n=173)	Significant problems (n=67)	p
BMI	Underweight (n=273)	252 (14.8%)	14 (8.1%)	7 (10.4%)	0.000^*
	Normal (n=1,574)	1395 (82.2%)	138 (79.7%)	41 (61.2%)
	Overweight (n=75)	39 (2.3%)	19 (11%)	17 (25.4%)
	Obese (n=16)	12 (0.7%)	2 (1.2%)	2 (3%)
Eating disorder	Yes (n=99)	90 (5.3%)	5 (2.9%)	4 (6%)	0.370^*
	No (n=1,839)	1608 (94.7%)	168 (97.1%)	63 (94%)

Chi-square test.

The results indicated that Web surfing (t=2.816), watching videos online (t=3.355), talking in chatrooms and Internet messaging (t=3.918), and playing online games (t=3.969) were significantly associated with increased BMI (p<0.05). In contrast, using the Internet for academic activities was associated with reduced BMI (t=−2.213, p<0.05). Other Internet activities such as checking e-mail (t=1.112), reading online news (t=−0.613), and shopping (t=0.719) were not associated with BMI (p>0.05).

A correlation matrix showing the relationships between age, BMI, average monthly household income, weekly Internet use, and IAT and EAT scores is presented in Table 5. Duration of weekly Internet use and IAT scores were positively related to BMI (p<0.01). Linear regression analysis (Table 6) revealed the IAT had the strongest independent association with BMI (partial correlation coefficient: 0.235; p<0.001).

Table 5.

Pearson's Correlation Coefficient Analysis for IAT, Age, Average Monthly Household Income, Weekly Internet Use, BMI, and EAT Scores

	Age	Household income	Weekly Internet use	BMI	EAT
IAT	0.035	0.117^*	0.777^*	0.307^*	0.038
Age	1	−0.019	0.076^*	0.061^*	−0.164^*
Household income		1	0.100^*	0.121^*	−0.029
Weekly Internet use			1	0.215^*	−0.005
BMI				1	0.078^*
EAT					1

Correlation is significant at the 0.01 level (two-tailed).

Table 6.

The Effect of Age, Gender, Average Monthly Household Income, Regular Exercising, Internet Addiction, and Eating Attitude on BMI Assessed by Linear Regression Analysis

	B	t	p
Age	0.081	3.007	0.003
Gender	0.152	5.469	0.000
Household income	0.086	3.260	0.001
Regular exercising	−0.063	−2.282	0.023
IAT	0.235	8.834	0.000
EAT	0.121	4.489	0.000

Discussion

We aimed to determine the frequency of Internet addiction and evaluate the relationship among Internet addiction, eating attitudes, and BMI in Turkish adolescents. In the present study, 12.4% of the Turkish adolescents met the criteria for Internet addiction (defined as the “occasional to frequent” or “significant problems” categories). This rate is similar to those previously reported from Turkey.^31,35,36 A significant, independent, and positive relationship was found between Internet addiction and BMI. Furthermore, the frequency of being obese and overweight was higher among adolescents with Internet addiction compared to average users. Previous studies that surveyed the relationship between Internet use and BMI focused on the duration of Internet use. In our study, in addition to the duration of Internet use, the relationship between Internet addiction and BMI was addressed. Although Internet addiction is also related to duration of Internet use, addiction is recognized as a distinct entity. Thus, this study is the first to examine the relationship between Internet addiction and BMI.

Until now, few studies in the English language literature have mentioned the relationship between BMI and Internet addiction. In the first study that examined the relationship between Internet addiction and BMI among adolescents, Lajunen et al.³⁷ found that having a computer at home constituted a high risk of being overweight and was related to increased BMI. However, the results indicated that there was no relationship between having a computer with an Internet connection at home and BMI. In contrast, in our study, although having a computer with an Internet connection was related to BMI, no relationship was observed between having a computer without an Internet connection and BMI. According to this finding, not only spending time on the computer but also being occupied specifically with Internet activities is related to increased BMI.

In a cross-sectional study of adolescent females,⁸ those who spent more time on the computer for e-mail, writing, and surfing the Internet were more likely to be overweight. In addition, in a recently published study, Yen et al.³⁸ surveyed 9,278 Taiwanese adolescents and found a relationship between television viewing and duration of Internet use and BMI, but not between duration of cellular phone use and BMI. They also found that among Internet activities, visiting erotic Web sites, watching films online, and reading online news were positively related to BMI, whereas instant messaging was negatively related to BMI. Our results were partly similar to these findings, as web surfing, watching videos online, using chatrooms and Internet messaging, and playing online games were significantly associated with increased BMI, whereas using the Internet for academic activities was associated with reduced BMI. According to these patterns, not only Internet use but also the type of Internet activities are significant in terms of obesity. Yen et al. also showed that exercising had a moderating effect on the relationship between BMI and television viewing.³⁸ Nevertheless, the researchers did not address the effects of exercising on the relationship between Internet use and BMI. In our study, linear regression analysis results showed that Internet addiction was related to BMI independently from regular exercising.

In a study conducted in Qatar,³⁹ 2,467 students aged between 6 and 18 years were surveyed, and the overweight and obesity rates were higher among students who used the Internet for 3 hours or more per a day than among those whose daily Internet use lasted less than 3 hours. Similarly, in our study, a significant positive relationship was found between duration of weekly Internet use and BMI.

In a very recent study evaluating female patients with eating disorders (n=60),⁴⁰ the prevalence of compulsive Internet use was 11.7%, which is similar to the prevalence of Internet addiction found in the general population of our study. Tao and Liu¹⁰ showed that students with Internet addiction (n=54) rated themselves with significantly higher symptomatic aspects of eating disorders than students without Internet addiction (n=1145). Furthermore, Rodgers et al.⁴¹ evaluated 392 French young adults and reported that Internet addiction and time spent online was correlated with disordered eating among women (not among men). However, in the present study, no relationship was found between the EAT and the IAT and duration of weekly Internet use. According to our findings, the relationship between Internet addiction and BMI does not appear to be affected by eating disorders. This finding points out the complexity of the relationship between Internet use and obesity.

The most important limitation of our study is that we were not able to show the causality of the relationship between Internet addiction and BMI because of the cross-sectional structure. Information such as students' height and weight and duration of weekly Internet use was collected from students. Thus, the accuracy of this information is in doubt because it was self-reported and not measured by the researchers. Furthermore, the students were asked whether they exercised regularly, but the type, frequency, and intensity of the exercise could not be determined. Therefore, the effects of exercise on BMI may not have been shown accurately. Moreover, the risk factors for obesity (e.g., genetic predisposition, parental overweight, etc.) were not assessed. Additionally, depression, anxiety, impulse control disorders, body dissatisfaction, and diet style might affect the relationship between Internet use and BMI. However, these were not evaluated in the present study. Last, students' other technology-related habits such as television viewing and cellular phone use were not investigated.

In the current study, Internet addiction was related to BMI among adolescents. Moreover, this relationship was independent from eating attitudes. It is remarkable that some Internet activities showed a stronger relationship with increased BMI than others. These results have clinical utility, as clinicians working with adolescents with Internet addiction may find it useful to develop strategies for reducing the amount of time spent using the Internet. An effective intervention plan that includes cognitive behavioral therapy and education for parents to develop managing skills is needed to accomplish this goal. The relationship between Internet addiction, the importance of which is increasing as Internet-related technology develops, and a vital health problem, obesity, should be examined with particular focus on the causality. Research about this relationship and taking possible precautions is important for the adolescent and young adult population who are more vulnerable to Internet addiction.

Author Disclosure Statement

No competing financial interests exist.

References

Reilly

. Obesity in childhood and adolescence: evidence based clinical and public health perspectives. Postgraduate Medical Journal, 2006; 82:429–437.

Chinn

, Rona

. Prevalence and trends in overweight and obesity in three cross sectional studies of British Children, 1974–94. BMJ, 2001; 322:24–26.

Ogden

, Carroll

, Curtin

, et al. Prevalence of overweight and obesity in the United States, 1999–2004. The Journal of the American Medical Association, 2006; 295:1549–1555.

Gortmaker

, Must

, Perrin

, et al. Social and economic consequences of overweight in adolescence and young adulthood. New England Journal of Medicine, 1993; 329:1008–1012.

Must

, Jacques

, Dallal

, et al. Long-term morbidity and mortality of overweight adolescents: a follow-up of the Harvard Growth Study of 1922 to 1935. New England Journal of Medicine, 1992; 327:1350–1355.

Hill

, Peters

. Environmental contributions to the obesity epidemic. Science, 1998; 280:1371–1374.

Dietz

, Gortmaker

. Do we fatten our children at the television set? Obesity and television viewing in children and adolescents. Pediatrics, 1985; 75:807–812.

Kautiainen

, Koivusilta

, Lintonen

, et al. Use of information and communication technology and prevalence of overweight and obesity among adolescents. International Journal of Obesity, 2005; 29:925–933.

Griffiths

. Does Internet and computer “addiction” exist? Some case study evidence. CyberPsychology & Behavior, 2000; 3:211–218.

10.

Tao

, Liu

. Is there a relationship between Internet dependance and eating disorders? A comparison study of Internet dependants and non-Internet dependants. Eating & Weight Disorders, 2009; 14:77–83.

11.

Pies

. Should DSM-V designate “Internet addiction” a mental disorder?. Psychiatry, 2009; 6:31–37.

12.

Griffiths

. Internet addiction—time to be taken seriously?. Addiction Research & Theory, 2000; 8:413–418.

13.

Thalemann

, Wolfling

, Grusser

. Specific cue reactivity on computer game-related cues in excessive gamers. Behavioral Neuroscience, 2007; 121:614–618.

14.

Shapira

, Lessig

, Goldsmith

, et al. Problematic Internet use: proposed classification and diagnostic criteria. Depression & Anxiety, 2003; 17:207–216.

15.

Johansson

, Götestam

. Internet addiction: characteristics of a questionnaire and prevalence in Norwegian youth (12–18 years). Scandinavian Journal of Psychology, 2004; 45:223–229.

16.

, Yoo

, Cho

, et al. Psychiatric comorbidity assessed in Korean children and adolescents who screen positive for Internet addiction. Journal of Clinical Psychiatry, 2006; 67:821–826.

17.

, Yen

, Chen

, et al. Psychiatric comorbidity of Internet addiction in college students: an interview study. CNS Spectrums, 2008; 13:147–153.

18.

Leung

. Net-Generation attributes and seductive properties of the Internet as predictors of online activities and Internet addiction. CyberPsychology & Behavior, 2004; 7:333–348.

19.

Grover

, Chakraborty

, Basu

. Pattern of Internet use among professionals in India: critical look at a surprising survey result. Indian Psychiatry Journal, 2010; 19:94–100.

20.

Matusitz

, McCormic

. Sedanterism: the effects of Internet use on human obesity in the United States. Social Work & Public Health, 2012; 27:250–269.

21.

Guran

, Bereket

. International epidemic of childhood obesity and television viewing. Minerva Pediatrica, 2011; 63:483–490.

22.

Wang

, Xu

, Zheng

, et al. Effects of television viewing on body fatness among Chinese children and adolescents. Chinese Medical Journal, 2012; 125:1500–1503.

23.

Mamun

, O'Callaghan

, Williams

, et al. Television watching from adolescence to adulthood and its association with BMI, waist circumference, waist-to-hip ratio and obesity: a longitudinal study. Public Health Nutrition, 2012; 12:1–11.

24.

Berkey

, Rockett

, Colditz

. Weight gain in older adolescent females: the Internet, sleep, coffee, and alcohol. Journal of Pediatrics, 2008; 153:635–639.

25.

Vandelanotte

, Sugiyama

, Gardiner

, et al. Associations of leisure-time Internet and computer use with overweight and obesity, physical activity and sedentary behaviors: cross-sectional study. Journal of Medical Internet Research, 2009; 11:e28.

26.

Simon

, Von Korff

, Saunders

, et al. Association between obesity and psychiatric disorders in the US adult population. Archieves of General Psychiatry, 2006; 63:824–830.

27.

Wilson

. Eating disorders, obesity and addiction. European Eating Disorders Review, 2010; 18:341–351.

28.

NHLBI Obesity Education Initiative Expert Panel the Identification, Evaluation, and Treatment of Obesity in Adults. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Report no. 98-4083. Bethesda, MD: National Heart, Lung, and Blood Institute; 1998.

29.

Young

. (1998) Caught in the net. New York: John Wiley, pp. 12–120.

30.

Bayraktar

. The role of Internet use in adolescent development. Master's thesis, Ege University Social Sciences Institute Psychology Department, 2001.

31.

Tahiroğlu

, Çelik

, Uzel

, et al. Internet use among Turkish adolescents. CyberPsychology & Behavior, 2008; 11:537–543.

32.

Garner

, Garfinkel

. Eating attitudes test: an index of the symptoms of anorexia nervosa. Psychological Medicine, 1979; 9:273–279.

33.

Button

, Whitehouse

. Subclinical anorexia nervosa. Psychological Medicine, 1981; 9:273–279.

34.

Savasir

, Erol

. Eating Attitude Scale: anorexia nervosa symptom index. Psikoloji Dergisi, 1989; 7:19–25 (in Turkish).

35.

Canan

, Ataoglu

, Nichols

, et al. Evaluation of psychometric properties of the Internet addiction scale in a sample of Turkish high school students. CyberPsychology Behavior & Social Networking, 2010; 13:317–320.

36.

Canan

, Ataoglu

, Ozcetin

, et al. The association between Internet addiction and dissociation among Turkish college students. Comprehensive Psychiatry, 2012; 53:422–426.

37.

Lajunen

, Keski-Rahkonen

, Pulkkinen

, et al. Are computer and cell phone use associated with body mass index and overweight? A population study among twin adolescents. BMC Public Health, 2007; 7:24.

38.

Yen

, Hsiao

, Ko

, et al. The relationships between body mass index and television viewing, Internet use and cellular phone use: the moderating effects of sociodemographic characteristics and exercise. International Journal of Eating Disorders, 2010; 43:565–571.

39.

Bener

, Al-Mahdi

, Ali

, et al. Obesity and low vision as a result of excessive Internet use and television viewing. International Journal of Food Sciences & Nutrition, 2011; 62:60–62.

40.

Claes

, Müller

, Norré

, et al. The relationship among compulsive buying, compulsive Internet use and temperament in a sample of female patients with eating disorders. European Eating Disorders Review, 2012; 20:126–131.

41.

Rodgers

, Melioli

, Laconi

, et al. Internet addiction symptoms, disordered eating, and body image avoidance. CyberPsychology Behavior & Social Networking, 2013; 16:56–60.