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NAZARBAYEV UNIVERSITY SCHOOL OF MEDICINE MASTER OF PUBLIC HEALTH PROGRAM

Prevalence of internet addiction and its association with mood and sleep disorders among young adults in Astana, Kazakhstan

Master of Public Health Thesis project Utilizing Professional Publication Framework

Akbota Tolegenova, MPH Candidate

Advisors: Raushan Alibekova, MD, PhD Byron Crape, MSPH, PhD

Astana, Kazakhstan 2018

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TABLE OF CONTENTS

LIST OF TABLES………. ii

ABSTRACT………... iii

INTRODUCTION………... 1

METHODS AND MATERIALS………... 3

Ethical considerations……… 3

Survey procedure and sampling………. 3

Data collection……… 3

Sample size………. 3

Measures……… 4

Statistical analysis……….. 6

RESULTS……….. 7

Socio-demographic characteristics of the participants………... 7

Bivariate analysis……….. 10

Multivariate analysis………. 13

DISCUSSION……… 16

CONCLUSION………. 19

REFERENCES……….. 20

APPENDICES……… 26

Appendix A. DASS21 cut-off points………. 26

Appendix B. Survey questions………... 27

Appendix C. Informed consent form………. 42

Appendix D. Conference presentation and journals for potential publication…………... 45

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LIST OF TABLES

Table 1. Socio-demographic characteristics of students in Astana city in 2018 (n=400)…... 7 Table 2. Prevalence of internet addiction, mood and sleep disorders among young

adults in Astana city in 2018 (n=400)………... 9 Table 3. Bivariate analysis of the relationships between potential internet addiction

and students’ characteristics: insomnia, depression, anxiety, and stress…….. 10 Table 4. Bivariate analysis of the relationships between potential internet addiction

and students’ characteristics: gender, siblings, activity, moderate and

vigorous physical activity……….. 11

Table 5. Internet addiction and Depression, multivariate logistic regression analysis………. 13 Table 6. Internet addiction and Anxiety, multivariate logistic regression analysis………….. 14 Table 7. Internet addiction and Stress, multivariate logistic regression analysis……… 15

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Abstract

Background: In recent years, the Internet has become the most essential tool in both studying and entertainment, by giving the fast and easy access to all the information for students.

However, uncontrolled excessive use of internet negatively influences a person’s life, leading him to “Internet addiction” (IA) or “Problematic Internet use”. Despite the fact, that more than half of Kazakhstani population (55.8 %), which is approximately 10 million, uses internet on a daily base, there is a lack of studies on internet addiction in Kazakhstan. Therefore, our study aims were: (1) to estimate the prevalence of IA among university and college students; (2) to explore the association of IA with various factors, related to socio-demographics, family relationships, mood and sleep disorders, self-esteem, physical and social activities, and academic performance of the students.

Methods: The cross-sectional study sample comprised 400 students of Astana city. Students completed a structured questionnaire comprised of a Young’s 20-item internet addiction scale, Depression Anxiety Stress Scales (DASS 21), Insomnia severity index scale, and questions about socio-demographic characteristics. Univariate, bivariate and multivariate analysis were used to analyze the obtained data.

Results: Potential IA prevalence rate was 19.75% and it was significantly different between males and females (p-value = 0.000); males had higher prevalence (65.82%) than females (34.18%).

Significant associations were found between potential IA and, stress, anxiety, depression, social and physical activity (p-value < 0.05).

Conclusion: These study findings have revealed a high prevalence of internet addiction among students. Association of internet addiction with mood disorders underlies the need for improving mental health services for adolescents.

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Introduction

In recent years, the Internet has become the most essential tool in both studying and entertainment, by giving the fast and easy access to all the information for students. The number of internet users in 2016 was 3.5 billion, the half of the world population (ICT "Facts and Figures 2016", 2016). No doubt, introduction of the internet has been found to bring great benefits for the organizations, governments and overall for people all over the world. However, its uncontrolled excessive use negatively impacting person’s emotional stability, relationships and daily functions.

Today, this phenomenon is known as “Internet addiction” or “Problematic Internet use” (C.H. Ko, et.al. 2012). 6% of worldwide population was found to be pathologically addicted to the Internet, in which Asia accounts for 7.1%, and West and North Europe accounts for 2.6% (Uddin et al., 2016).

A systematic review about problematic internet use reported that the prevalence among United States youth was 26.3% (Moreno, M. A., et.al. 2011). With the increasing number of internet users, the number of addicted ones is also increasing year by year by thousands.

According to the Beard (2005), person can be defined as addicted if he is preoccupied with the Internet, continuously needs to use the Internet with increased amount of time, has made unsuccessful efforts to control, and stop using Internet, and feel depressed during this period, and finally, stays online longer than originally intended (Beard, 2005).

Many research findings have noted that several mental disorders accompany internet addiction (IA). However, there is a debate regarding the order, which comes first, the internet addiction or the disorder (Kratzer & Hegerl, 2007). A longitudinal study conducted by Dong et.al.

tried to clarify this question. They reported that IA results in higher scores for depression, anxiety, hostility, interpersonal sensitivity, and psychoticism (Dong, Lu, Zhou & Zhao, 2011). Ineme and his team (Ineme et.al, 2017) reported an opposite relationship, where the increase in depression as an independent variable, significantly related with increase in internet addiction as a dependent variable.

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Severe addiction to the internet was found to be associated additionally, with low self- esteem (Naseri, Mohamadi, Sayehmiri & Azizpoor, 2015), poor physical activity and sleep deprivation (Kim et al., 2010), impulsivity (Lee et al., 2012) and finally, suicide (Lin et al., 2014).

Demographic factors, such as age, gender, marital status, place of living and so on, can also affect to the prevalence of IA (Pujazon-Zazik & Park, 2010). Researchers, Frangos, Frangos and Kiohos (2010), reported that the males were more likely to be addicted than the females, similarly divorced more addicted than married. Moreover, students with poor academic grades had potential internet addiction than those with good academic standings. Surveys also found that the most vulnerable population is the young people between the ages of 12 and 24 (Ineme et.al, 2017).

According to the annual report of International Telecommunication Union (ITU), in Kazakhstan, in 2010 the number of internet users was more than 5 million ("ICT Facts and Figures 2017", 2017). In 2016, this number had increased by 5 million and became approximately 10 million. This means that 55.8 % of the population daily uses internet ("ICT Facts and Figures 2017", 2017). This confirms that the number of population who uses the internet in Kazakhstan is increasing each year. To the best of the author’s knowledge, there is no official published studies in Kazakhstan, that reported prevalence of addiction.

Therefore, the purpose of this study was to examine the prevalence of internet addiction and its association with mood and sleep disorders among young adults in Astana, Kazakhstan. The specific study objectives were: 1) to determine the prevalence of internet addiction among 18-24 age-old students in Astana city; 2) to assess the relationship between potential internet addiction and mood disorders, such as depression, anxiety, and stress; 3) to determine the relationship between internet addiction and insomnia; 4) to find the relationship between internet addiction and self-esteem, and 5) to identify the prevalence of internet addiction by purpose of time spent online such as, usage of internet for a study, entertainment, to play video games or for chatting and blogging.

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Methods and Materials Ethical considerations

The Research Ethics Committee of Nazarbayev University School of Medicine (NUSOM REC) approved the protocol of the study. Verbal informed consent was taken to assure the anonymity of the study from all the participants before they started filling the survey.

Survey procedure and sampling

The cross-sectional survey was carried out in randomly selected colleges and universities in the city of Astana during the period of December 2017– February 2018. Inclusion criteria were students between age 18-24, who agreed to participate in the study. Exclusion criteria were: age under 18 years and above 24.

Data collection

Data were collected using self-administered standardized anonymous questionnaires in two languages: Kazakh and Russian. Questionnaires consisted of demographic, family status, physical activity, social activity, academic performance sections and four widely validated scales, such as Internet Addiction Test (IAT) by Kimberly Young (Young, 1996), the Insomnia Severity Index (Bastien, 2001), the Depression Anxiety Stress Scales (DASS 21) (Henry & Crawford, 2005) and the Rosenberg Self Esteem Scale (RSES) (Petersen, 1965). It took 10-15 minutes to participants to fill the questionnaire.

Sample size

Sample size calculation was performed by using StatCalc in Epi Info 7.2.2.2. It was based on findings of an exploratory cross-sectional study among Chinese adolescents (CHEUNG &

WONG, 2010). The study found, that out of the 719 participants, 60.4% were male and 39.6% were females; and 42.10 % of non-internet addicts were depressed while 58.9% of addicts were depressed. Furthermore, 51.70% of addicts were insomniacs and 26.30% of non-addicts had an

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insomnia (CHEUNG & WONG, 2010). Based on these prevalences, the calculated sample size for our study was found to be 650. . After the data collection, our study sample was comprised of 413 participants. The questionnaires with incomplete responses were excluded, and overall 400 of questionnaires were included into the final analysis.

Measures

Socio-demographics

Questions about age, gender, year of study, marital status, number of siblings, monthly income of the family, family status, living place, social activity, physical activity, the reason using the internet, and questions about academic performance were asked in the socio-demographic section of the survey.

The Young Internet Addiction Test (YIAT)

The Young Internet Addiction Test (YIAT) is widely used and validated reliable tool to assess the internet addiction among adolescents and adults developed by Kimberly Young (Younes et al., 2016). A self-reporting scale consisted of 20 questions measuring firstly, seven questions about overall patterns of Internet usage, then 3 questions about participant’s productivity at home, school, or work, next, three questions about their social behaviors, and finally seven questions about emotional connection to and response from using the internet. Students answer to the 20 YIAT items on a 6-point Likert measure, where zero means “does not apply” and five - “always”. An overall score will vary between 0 and 100. Total scores range from 0 to 30 points are contemplated as a normal level of Internet usage; scores from 31 to 49 showed the incidence of a mild level of Internet addiction; 50 to 79 indicated a moderate level; and scores of 80 to 100 reflected the presence of a severe dependence on the Internet (Young & Abreu, 2010). Then, for multivariate logistic analysis, this variable was dichotomized and the cut-off point of 50 was used, with scores 0–49 indicating normal internet use, and scores equal to 50 or more - potential internet addiction (Younes et al., 2016).

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Insomnia Severity Index Test

The Insomnia Severity Index Test (ISIT) is a 7-item, also self-report tool that assess the severity of the insomnia. It measures the problems with waking up in the morning, sleep onset, dissatisfaction with sleeping, maintenance of sleep, problems with daytime functioning and distress due to sleep difficulties and its perception. Similarly, it is a Likert scale from 0 to 5, where 0 means no problem and 4 indicates a very severe problem. The total score 28 was expounded as following:

0 to 7 normal; 8 to 14 mild; of 15 to 21 moderate; and from 22 to 28 severe insomnia (Gagnon, Belanger, Ivers & Morin, 2013).

Rosenberg Self Esteem Scale

A Rosenberg Self Esteem Scale (RSRS) measures the global self-worth by determining both positive and negative feelings about the self. It consists of 10 questions, where 4-point Likert scale format, ranging from strongly agree to strongly disagree is used. Items 1, 2, 4, 6 and 7 were rated as normal and for items 3, 5, 8, 9 and 10 opposite rating was used, that results in total score of 0 to 30.

The cut-off point was as following: from 0 to 15 low self-esteem, and from 16 to 30 high self- esteem. (Petersen, 1965)

DASS21

This self-reported DASS 21 test evaluates the severity of behavioral and emotional symptoms that are correlated with depression, anxiety disorder and stress by providing a mild, moderate or severe result. There are 20 questions, where 1) Depression symptoms related items: 3, 5, 10, 13, 16, 17, 21; 2) Anxiety disorder related items: 2, 4, 7, 9, 15, 19, 20; and 3) Stress related items: 1, 6, 8, 11, 12, 14, and 18. All the questions are valued from zero to three, thus each of the axes gives partial scores from 0 to 18-24 depending on the number of questions assigned. The cut- off points are presented in Supplementary Table 1 (Appendix 1) (Henry & Crawford, 2005).

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Statistical analysis

STATA 12 (STATA Corporation, USA, Texas, 2012) was used to conduct the analysis of the data. Univariate analysis was done to summarize the data and analyze the pattern. Pearson’s Chi-square test and Fisher’s exact test were conducted to do bivariate analysis. Internet addiction (<50, ≥50) was dichotomized as the dependent variable and grouped as normal internet users and potential internet addiction. Variables that showed statistically significant (p<0.05) associations with dependent variable of IA in bivariate analysis, were candidates for the multivariate model.

Multivariate analysis was carried to control the impact of multiple explanatory variables presented concurrently and to find which of them has an independent effect on the internet addiction.

Since an anxiety, stress and depression were highly correlated with each other, they were not entered in the same model. As a result, three multivariate logistic regression analyses were done with the independent variables such as gender, siblings, social activity, moderate and vigorous physical activities, ISI score, RSES score and the DAS scores for depression, anxiety and stress separately, as independent variables.

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Results

Socio-demographic characteristics of the participants

Sociodemographic characteristics of the total sample of 400 students are presented in Table 1. The study population comprised of 178 (44.50%) male and 222 (55.50%) female, whose age ranged from 18 to 24 years with a mean of 19.02 ± 1.39 years. The majority of the participants were of Kazakh ethnicity (92.25%) and only 5.75% were representatives of other ethnicities. The data included 125 (31.25%) 1st year students, 132 (33%) 2nd year, 27(6.75%) and 116 (29%) 3rd and 4th year students, respectively.

Table 1. Socio-demographic characteristics of students in Astana city in 2018 (n=400).

96.75% of the participants were single and remaining 3.25% were married. The most the students came from families with 1-2 (38%) or 3-4 (33.25%) children. 33.75% of students came from families with a monthly income less than 100 000 tenge. Majority had parents, mother and father (73.75%). In addition, 35.50% of students lived in a house that was rented with their family.

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Table 1. Socio-demographic characteristics of students in Astana city in 2018 (n=400) (continued)

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It was found out that 119 (29.75%) of 400 students do not attend any type of social activity, and also 49.50% (198) practice moderate physical activities, such as walking fast, playing tennis, slow bicycling and etc., 2-4 times per week; while 51.25% (205) do only once or even do not do vigorous physical activities per week. One third of the students indicated that they use internet for studying and doing homework (32%), whereas another third of students used it for more than one reason (33.50%).

Table 2. Prevalence of internet addiction, mood and sleep disorders among young adults in Astana city in 2018 (n=400).

Number Percentage (%) IAT

Normal 198 49.50

Mild 123 30.75

Moderate 55 13.75

Severe 24 6.00

Mean± SD 34.8725± 21.84099

Depression

Normal 228 57.00

Mild 51 12.75

Moderate 88 22.00

Severe 22 5.50

Extremely Severe 11 2.75

Mean± SD 4.595± 3.857223

Anxiety

Normal 200 50.00

Mild 60 15.00

Moderate 46 11.50

Severe 54 13.50

Extremely Severe 40 10.00

Mean± SD 4.4375± 3.778463

Stress

Normal 296 74.00

Mild 56 14.00

Moderate 31 7.75

Severe 17 4.25

Mean± SD 5.125±3.656885

ISI

Normal 203 50.75

Mild 149 37.25

Moderate 42 10.50

Severe 6 1.50

Mean± SD 8.275± 4.997681

RSES

Low 118 29.50

High 282 70.50

Mean± SD 20.34±6.16

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The average Internet addiction test score was found to be 34.87 ± 21.84. The prevalence of extremely severe depression among students was 2.75% however, for anxiety it was revealed to be 10%. The percentage of students with severe stress was 4.25%. 1.50% of participants had severe insomnia whereas 29.50% had low self-esteem. The insomnia presented mean score of 8.27±4.99, self-esteem 20.34±6.16. Furthermore, the mean score for depression was 4.59±3.86, anxiety 4.44±3.78, and stress 5.13±3.66 (Table 2).

Bivariate analysis

The potential internet addiction prevalence rate was found to be equal to 19.75% (Table 3).

The bivariate analysis revealed that the prevalence of potential internet addiction was statistically significantly different between males and females with p=0.000. Males had higher prevalence (65.82%) than females (34.18%).

Table 3. Bivariate analysis of the relationships between potential internet addiction and students’

characteristics: insomnia, depression, anxiety, and stress

Normal n (%) Potential Addicts n (%) Pearson chi2 and P-value

Total 400 321 (80.25) 79 (19.75)

ISI Pearson chi2(3) = 6.3934 Pr = 0.094

Normal 164(51.09) 39(49.37)

Mild 124 (38.63) 25 (31.65)

Moderate 30 (9.35) 12 (15.19)

Severe 3 (0.93) 3 (3.80)

Depression Pearson chi2(4) = 30.7900 Pr = 0.000

Normal 202 (62.93) 26 (32.91)

Mild 41 (12.77) 10 (12.66)

Moderate 59 (18.38) 29 (36.71)

Severe 12 (3.74) 10 (12.66)

Extremely Severe 7 (2.18) 4 (5.06)

Anxiety Pearson chi2(4) = 37.8831 Pr = 0.000

Normal 179 (55.76) 21 (26.58)

Mild 51 (15.89) 9 (11.39)

Moderate 35 (10.90) 11 (13.92)

Severe 31 (9.66) 23 (29.11)

Extremely Severe 25 (7.79) 15 (18.99)

Stress Pearson chi2(3) = 32.7516 Pr = 0.000

Normal 254 (79.13) 42 (53.16)

Mild 42 (13.08) 14 (17.72)

Moderate 18 (5.61) 13 (16.46)

Severe 7 (2.18) 10 (12.66)

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Moreover, it was found that, DAS, number of siblings, social activity, moderate and vigorous physical activities had statistically significant relationship with potential internet addiction. However, surprisingly ISI and RSES were not significantly related to the internet addiction, as it was expected.

Specifically, higher rate of potential internet addiction, (3.80% vs 0.93%; p=0.000). The rate of students with internet addiction in depressed students was higher than among students without depression (show combined percentages of IA vs non-IA among moderate, severe and extremely severely depressed; p=0.000). The highest rate of potential internet addiction was detected in participants with extremely severe anxiety, 18.99%, while the normal users’ percentage found to be 7.79%. Furthermore, of severely stressed students 12.66% had potential internet addiction, and 2.18% were normal users.

Table 4. Bivariate analysis of the relationships between potential internet addiction and students’

characteristics: gender, siblings, activity, moderate and vigorous physical activity

Normal n (% ) Potential Addicts n (% ) Pearson chi2 and P-value

Gender Pearson chi2(1) = 18.1224 Pr = 0.000

Male 126 (39.25) 52 (65.82)

Female 195 (60.75) 27 (34.18)

Siblings Pearson chi2(3) = 18.5248 Pr = 0.000

None 26 (8.10) 20 (25.32

1-2 127 (39.56) 25 (31.65)

3-4 110 (34.37 23 (29.11)

more than 5 58 (18.07) 11 (13.92)

Activity Pearson chi2(4) = 15.3108 Pr = 0.004

Regularly do any type of sports 89 (27.73) 13 (16.46) Regularly sing and/or dance 63 (19.63) 12 (15.19) Regularly attend other student clubs 39 (12.15) 22 (27.85)

Inactive 93 (28.97) 26 (32.91)

More than one 37 (11.53) 6 (7.59)

Moderate Physical Activity (non- exhausting exercises such as fast

walking, baseball, tennis, slow

bicycling, etc.) Pearson chi2(2) = 43.7445 Pr = 0.000

Low (0–1 times/ week) 112 (34.98) 22 (27.85)

Medium (2–4 times/week) 174 (54.21) 24 (30.38)

High (5 or more times/week) 35 (10.90) 33 (41.77)

Vigorous Physical Activity (exhausting exercises such as running,

jogging, football, soccer, basketball,rollerblading,skateboarding,

etc.) Pearson chi2(2) = 61.0492 Pr = 0.000

Low (0–1 times/ week) 180 (56.07) 25 (31.65)

Medium (2–4 times/week) 115 (35.83) 20 (25.32)

High (5 or more times/week) 26 (8.10) 34 (43.04)

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Number of siblings was also a predictor of internet addiction among students. The higher rate of internet addiction was found among students who had no siblings (25.32% vs 8.10), compared to those who had more than 5 siblings (13.92 vs 18.07), p=0.000. Next, the students who attend to more than one social activity in a week had a low rate of potential internet addiction, which is 7.59%, while socially inactive students had higher percentage, 32.91%. Remarkably, there was a statistically significant association between internet addiction and moderate physical activity.

Nonetheless, participants who do moderate sports 5 or more times per week had greater rate of Internet addiction (41.77%) than ones who do only once or even do not do moderate activity per week(27.85%), p=0.000. Similarly, high vigorously physically active students were more potential addicts, than low physically active students were (43.04% vs 32.65%; p=0.000) (Table 4).

Finally, variables that were statistically significant were shown in the Table 3 and 4. These variables were included to the multivariate logistic regression analysis in the next step.

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Multivariate Logistic regression, multivariate analysis

The multivariate logistic regression model included gender, number of siblings, social activity, moderate and vigorous physical activity, insomnia, depression, anxiety and stress as predictor variables that were statistically significant in the previous bivariate analysis, and the internet addiction as a dependent variable. Moreover, the predictors were categorized. As it was found that depression, stress and anxiety were highly correlated, three different, separate models were done.

Table 5. Internet addiction and Depression, multivariate logistic regression analysis

Variables Adj.

OR

P-value 95% CI

Gender: Female 0.291 0.001 0.137 - 0.616

Siblings: 1 or 2 0.363 0.050 0.132 - 0 .998

Vigorous Physical Activity: High

11.382 0.000 3.586 - 36.124

Activity: Inactive 5.347 0.002 1.891- 15.118

Depression

Mild 4.156 0.007 1.474 - 11.721

Moderate 8.513 0.000 3.699 - 19.591

Severe 13.862 0.000 4.369 - 43.978

Extremely Severe 27.919 0.000 5.957 - 130.851

The table 5 demonstrates the first model with depression variable. Once the explicative variables were controlled for in multivariate analysis, the gender still remained to be the statistically significant predictor of the internet addiction (odds ratio =0.29, p=0.001). While, only having 1 or 2 siblings remained to be significant predictor (odds ratio=0.36, p= 0.050). Similarly, being inactive become also a statistically significant predictor of being pathological addict (odds ratio= 5.34, p=0.002). However, moderate physical activity become no longer significantly associated (p>0.05), whereas high vigorous physical activity remained to be the predictor with high odds ratio (odds ratio=11.38, p=0.000). Lastly, the depression also found to be statistically significantly associated

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with the internet addiction, where the extremely severe depression had the highest odds ratio, 27.92 with a p=0.000.

Table 6. Internet addiction and Anxiety, multivariate logistic regression analysis

The model done with anxiety variable showed the similar results as depression. First of all, the gender: female also was a significant predictor of internet addiction with odds ratio of 0.28 and p=0.002, less than male’s odds ratio. Next, the number of siblings that are between 1 and 2, persisted to be a predictor (odds ratio=0.35, p=0.044) that was less than not having any siblings.

Social inactivity also remained to be the statistically significant variable on internet addiction (odds ratio=5.32, p= 0.002). Moderate physical activity became likewise in previous model, no more significantly associated (p>0.05). Conversely, vigorous physical activity found to be significantly associated (p<0.05). Finally, the anxiety found to be a statistically significant associated with internet addiction, and alike depression, the highest odds ratio resulted by extremely severe anxiety (odds ratio=21.29, p=0.000) (Table 6).

Variables Adj.OR P-value 95% CI

Gender: Female 0.283 0.002 0.129 - 0.617

Siblings: 1 or 2 0.353 0.044 0.128 - 0.971

Vigorous Physical Activity: High 16.124 0.000 4.743 - 54.815

Activity: Inactive 5.319 0.002 1.868 - 15.147

Anxiety

Mild 3.600 0.019 1.238 -10.463

Moderate 6.828 0.001 2.199 - 21.202

Severe 14.134 0.000 5.439 - 36.727

Extremely Severe 21.290 0.000 7.412 - 61.151

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Table 7. Internet addiction and Stress, multivariate logistic regression analysis

Variables Adj.OR P-value 95% CI

Gender: Female 0.338 0.004 0.161 - 0.711

Moderate Physical Activity: Medium 0.406 0.040 0.172 - 0.959 Vigorous Physical Activity: High 19.979 0.000 6.000 - 66.530

Activity: Inactive 5.449 0.001 1.968 - 15.081

Stress

Mild 4.053 0.002 1.693 - 9.698

Moderate 13.352 0.001 4.867 - 36.631

Severe 15.231 0.000 7.088 - 83.04

Finally, a model with a stress variable demonstrated different results (Table 7). Firstly, there was no longer association between number of siblings and potential internet addiction, due to the high p-value. The inactive social activity remained the same, meaning it also significantly associated in this model (odds ratio= 5.45, p=0.001). Surprisingly, moderate activity 2-3 times per week became a statistically significant predictor of pathological internet addiction (odds ratio=0.406, p-value=0.040). Vigorous physical activity stayed the same (p<0.05), with a high level of activity with high odds ratio (odds ratio =19.98). As well as, all levels of stress became also statistically significantly associated with pathological internet addiction with p value less than 0.05.

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Discussion:

It is important to study internet addiction among the university and college students, observing the percussion growth in internet use in this subset of population, especially in developing countries, as Kazakhstan. University and college students are a predominantly vulnerable group because of the time they spend on the internet. This study is an initial step toward understanding the magnitude of pathological internet addiction among students in Kazakhstan.

The main purpose of this study was to find the prevalence of pathological internet addiction among university and college students in Astana city, and to evaluate the association of IA with students’ characteristics, such as socio-demographics, social and physical activeness, and with mood disorders (depression, anxiety, and stress), insomnia and their self-esteem.

It was found out, that the prevalence of pathological IA among students is 19.75%. This rate is higher than the rate of entire Asian countries, which was 7.1%, and West and North Europe countries 2.6% (Uddin et al., 2016). However, this finding is similar to the prevalence reported by research of Younes et al. ,16.8% at the university in USA (Younes et al., 2016). This high rate raises concern to address pathological internet addiction as a substantial emerging mental health issue among young adults. We found that 49.50% students were normal users while 30.75% cases have mild addiction, 13.75% students have moderate addiction and 6.00% of them have severe addiction to internet. Moderate and severe levels were combined and accounted as a pathological internet addicts.

In this study, males were observed to be more addicted than females, 65.82% and 34.18%

respectively. A study among professional students in India, and other two more studies, reported the similar result (Sharma, Sahu, Kasar & Sharma, 2014; Anusha Prabhakaran, Patel, Ganjiwale &

Nimbalkar, 2016; Grover, Basu & Chakraborty, 2010). This difference is explained by the fact that males are more likely to use internet in online games, gambling and watching pornography, that have all been associated with pathological internet addiction (Morahan-Martin & Schumacher, 2000). Nonetheless, in this study 32% indicated that they use internet for studying and doing

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homework, whereas 33.50%use for more than one reason, such as entertainment, blogging and chatting, and studying. On the other hand, other studies did not find any difference between genders (Vigna-Taglianti et al., 2017).

The mean age of the participants was 19.02 ± 1.39 years, and there was no statistically significant in internet addiction between different age groups. One of the studies done by Mashhor Al-hantoushi et al. also stated no difference between age groups, with a mean age of 17 years (Alhantoushi & Alabdullateef, 2014).

Equally important, there were found many factors statistically significantly associated with potential internet addiction. Regarding the number of siblings, 31.65% of students had 1-2 siblings and were addicts. The multivariate logistic regression showed that the odds ratio of it is less than one in all three models, which means that with an increase of number of siblings the internet addiction decreases. This can be also seen from the bivariate analysis, where the rate decreases from 31.65% (1-2 siblings) to 13.92% (more than 5 siblings). One of the Chinese research reported that being only child increases the likelihood of developing an internet addiction (He et al., 2016). In a like manner, social activeness became a significant predictor of internet addiction. 29.75% of students indicated themselves as socially inactive, and after conducting multivariate analysis, it was found that the increase of inactiveness accrete internet addition. This finding was consistent with the an European findings, where agroups with highest rates in Compulsive internet use scale scores and time spent in the internet had the lowest social participation(Rumpf et al., 2013). Besides social activity, physical activeness also found to be the statistical predictor of pathological internet addiction. In all three model, high vigorous physical activity remained to be statistically significant, which mean that increase of physical activity can increase the pathological internet addiction. This happens, because after tiring exercise, students relax by surfing on the internet, developing addiction. Nonetheless, researches showed that Internet addiction was higher in students lacking physical activity as compared to those with regular physical activity (Khan, Shabbir & Rajput, 2017).

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The prevalence of participants suffering from severe insomnia found to be 1.50%, and there is a statistically significant association between potential internet addiction and insomnia in bivariate analysis. The similar finding was stated by the research done by Younes et al. (Younes et al., 2016). However, the prevalence is less than other findings (Choueiry et al., 2016; Jiang et al., 2015). However, the insomnia became no more predictor of internet addiction after controlling the variables in multivariate logistic regression.

Furthermore, multivariate logistic regression demonstrated that depression, anxiety and stress (DAS) are statistically significant predictors of potential internet addiction. In addition, the odds ratios of DAS were more than one, which means that more the level of DAS more the person becomes addicted. A systematic review done by Carli et al., found that among 20 studies 75%

reported there were significant positive correlations of pathological internet addiction with depression, and 57% with anxiety (Carli et al., 2013).

Lastly, the prevalence of students with low self-esteem was found to be 29.50%. However, there was no statistically significant association between self-esteem and internet addiction, thus it was not included as a predictor variable.

This research was one of the first studies done about IA and its risk factors in Kazakhstan, which can give a start to further investigations. The study was conducted using the validated self- reported questionnaires, which reflects the interviewee’s own perspective, making it more suitable for reporting subjective disorders. However, this may also cause a recall bias. Moreover, this was a cross-sectional research, and therefore our findings should be interpreted cautiously in relation to causality of reported associations. . Further longitudinal investigations needed to explore the direction of the association between mood disorders and internet addiction. Other potential risk factors that we were not able to account for, should be investigated in future studies with a bigger sample size.

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Conclusion

With an increase of internet usage, the internet addiction issues among young adults also emerging rapidly in a world. Due to the high prevalence rate of pathological internet addiction and its associations with mood and other disorders, which are correspondingly correlated with suicide, interventions for early detection and prevention of mental disorders among adolescents and young adults need to be developed.

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APPENDICES

Appendix A. The cut-off points of DASS21

Level/Disorder Depression Anxiety Stress

Normal 0 - 4 0 - 3 0 - 7

Mild 5 - 6 4 - 5 8 - 9

Moderate 7 - 10 6 - 7 10 - 12

Severe 11 - 13 8 - 9 13 - 16

Extremely severe ≥ 14 ≥ 10 ≥ 17

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Appendix B. Survey questions

(English version) Questionnaire Demographic questions:

1.What is your age? (please write in) ______________________________

2. What is your gender?

1. Male 2. Female

3. What is your ethnicity?

1. Kazakh 2. Russian

3. Other (Please, specify)__________________

4. Year of study?

1. 1st year 2. 2nd year 3. 3rd year 4. 4th year

5. Marital status 1. Married 2. Single 3. Divorced

6. How many siblings do you have?

1. none 2. 1-2 3. 3-4

4. more than 5

7. What is your average family monthly income (in tenge)?

1. Less than 100 000 2. 100 000 – 199 000 3. 200 000 – 299 000 4. 300 000 –399 000 5. 400 000 – 499 000 6. 500 000 and above

8. Family background situation:

1. Living with two married parents 2. Living with mother only

3. Living with father only 4. Living with no parents 9. Living place

1. Family home 2. Rent

3. Dormitory 4. Personal home

10. During your leisure time what kind of social activities do attend?

1. Regularly do any type of sports

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28 2. Regularly sing and/or dance

3. Regularly attend other student clubs (ex. debate club, cooking club etc.) 4. Regularly attend to out of university activities (volunteering, meetings ) 5. Inactive

11. How often do you do Physical activity?

Type of Physical activity/Times Per Week Low (0–1 times/week)

Medium (2–4 times/week)

High (5 or more times/week)

Moderate Physical Activity

(non-exhausting exercises such as fast walking, baseball, tennis, slow bicycling, etc.)

1 2 3

Vigorous Physical Activity

(exhausting exercises such as running, jogging, football, soccer, basketball, rollerblading,skateboarding, etc.)

1 2 3

12. Do you use Internet?

1. Yes

2. No (If ‘No’, please go to the question # 15)

13. Main reason using the internet 1. Study, doing homework 2. Watch video, entertainment 3. Playing video games

4. Social network, chatting,blogging

14. How well do you believe you are performing at college?

1. Excellent 2. Good 3. Mediocre 4. Poor 5. Very poor

To begin, answer the following questions from 15-34 by using this scale:

0 Does not apply

1 Rarely 2 Occasionally 3 Frequently 4 Often 5 Always

Question Scale

15 How often do you find that you stay online longer than you intended? 0 1 2 3 4 5 16 How often do you neglect household chores to spend more time on-line? 0 1 2 3 4 5 17 How often do you prefer the excitement of the Internet to intimacy with your partner? 0 1 2 3 4 5

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18 How often do you form new relationships with fellow online users? 0 1 2 3 4 5 19 How often do others in your life complain to you about the amount of time you spend online? 0 1 2 3 4 5

20 How often do your grades or school work suffers because of the amount of time you spend online?

0 1 2 3 4 5

21 How often do you check your email before something else that you need to do? 0 1 2 3 4 5 22 How often does your job performance or productivity suffer because of the Internet? 0 1 2 3 4 5 23 How often do you become defensive or secretive when anyone asks you what you do online? 0 1 2 3 4 5

24 How often do you block out disturbing thoughts about your life with soothing thoughts of the Internet?

0 1 2 3 4 5

25 How often do you find yourself anticipating when you will go online again? 0 1 2 3 4 5

26 How often do you fear that life without the Internet would be boring, empty, and joyless?

0 1 2 3 4 5

27 How often do you snap, yell, or act annoyed if someone bothers you while you are on-line? 0 1 2 3 4 5

28 How often do you lose sleep due to late-night log-ins? 0 1 2 3 4 5

29 How often do you feel preoccupied with the Internet when offline, or fantasize about being on-line?

0 1 2 3 4 5

30 How often do you find yourself saying “just a few more minutes” when online? 0 1 2 3 4 5

31 How often do you try to cut down the amount of time you spend online and fail?

0 1 2 3 4 5

32 How often do you try to hide how long you’ve been on-line? 0 1 2 3 4 5

33 How often do you choose to spend more time online over going out with others?

0 1 2 3 4 5

34 How often do you feel depressed, moody or nervous when you are offline, which goes away once you are back online?

0 1 2 3 4 5

For questions 35-54, please rate your agreement with following statements by using this scale:

0 Did not apply to me at all

1 Applied to me to some degree, or some of the time

2 Applied to me to a considerable degree or a good part of time 3 Applied to me very much or most of the time

Question Scale

35 I found it hard to wind down 0 1 2 3

36 I was aware of dryness of my mouth 0 1 2 3

37 I couldn’t seem to experience any positive feeling at all 0 1 2 3

38 I experienced breathing difficulty (e.g. excessively rapid breathing, breathlessness in the absence of physical exertion)

0 1 2 3

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39 I found it difficult to work up the initiative to do things 0 1 2 3

40 I tended to overreact to situations 0 1 2 3

41 I experienced trembling (e.g. in the hands) 0 1 2 3

42 I felt that I was using a lot of nervous energy 0 1 2 3

43 I was worried about situations in which I might panic and make a fool of myself 0 1 2 3

44 I felt that I had nothing to look forward to 0 1 2 3

45 I found myself getting agitated 0 1 2 3

46 I found it difficult to relax 0 1 2 3

47 I felt downhearted and blue 0 1 2 3

48 I felt I was close to panic 0 1 2 3

49 I was unable to become enthusiastic about anything 0 1 2 3

50 I felt I wasn’t worth much as a person 0 1 2 3

51 I felt that I was rather touchy 0 1 2 3

52 I was aware of the action of my heart in the absence of physical exertion (e.g. sense of heart rate increase, heart missing a beat)

0 1 2 3

53 I felt scared without any good reason 0 1 2 3

54 I felt that life was meaningless 0 1 2 3

For questions 55-57, please rate your agreement with following statements:

None Mild Moderate Severe Very Severe

55. Difficulty falling asleep 0 1 2 3 4

56.Difficulty staying asleep 0 1 2 3 4

57. Problem waking up too early 0 1 2 3 4

58. How Satisfied/Dissatisfied are you with your sleep pattern?

0. Very Satisfied 1. Satisfied

2. Moderately Satisfied 3. Dissatisfied

4. Very Dissatisfied

59. How Noticeable to others do you think your sleep problem is in terms of impairing the quality of your life?

0. Not at all Noticeable 1. A Little

2. Somewhat 3. Much

4. Very Much Noticeable

60. How Worried/Distressed are you about your current sleep problem?

0. Not at all Worried

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31 1. A Little

2. Somewhat 3. Much

4. Very Much Worried

61. To what extent do you consider your sleep problem to interfere with your daily functioning (e.g. daytime fatigue, mood, ability to function at work/daily chores, concentration, memory, mood, etc.)?

0. Not at all Interfering 1. A Little

2. Somewhat 3. Much

4. Very Much Interfering

For questions 35-54, please rate your agreement with following statements by using this scale:

1.Strongly disagree 2.Disagree

3.Agree

4.Strongly agree from

Question Scale

62 On the whole I am satisfied with myself. 1 2 3 4 63 At times I think I'm no good at all. 1 2 3 4 64 I think that I have a number of good qualities. 1 2 3 4 65 I am able to do things as well as most other people.1 2 3 4 66 I feel I do not have much to be proud of. 1 2 3 4 67 I certainly feel useless at times. 1 2 3 4

68 I feel that I am a person of worth, at least on an equal plane with others.

1 2 3 4

69 I wish I could have more respect for myself. 1 2 3 4 70 All in all, I am inclined to feel that I am a failure. 1 2 3 4 71 I take a positive attitude toward myself. 1 2 3 4

Сурет

Table 1. Socio-demographic characteristics of students in Astana city in 2018 (n=400)
Table 1. Socio-demographic characteristics of students in Astana city in 2018 (n=400) (continued)
Table 2.  Prevalence of internet addiction, mood and sleep disorders among young adults in Astana  city in 2018 (n=400)
Table  3.  Bivariate  analysis  of  the  relationships  between  potential  internet  addiction  and  students’
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