Preprint
Article
Factors Related to Internet Addiction in Serbian Adolescents: A Cross-Sectional Study
Altmetrics
Downloads
117
Views
80
Comments
0
supplementary.pdf (31.07KB )
This version is not peer-reviewed
Future Internet Architecture: Difficulties and Opportunities
Submitted:
22 February 2024
Posted:
23 February 2024
You are already at the latest version
Alerts
Abstract
Exponential increase in the number of individuals using internet has been observed in last ten years. The term "internet addiction" (IA) describes a compulsive behavior associated with any online activity that disrupts everyday social interactions. Main aim of this research was to determine variables and development predictors of Internet addiction. A total of 1,669 respondents participated in this research; 1,040 of them (62.3%) were female, 590 (35.4%) were male, and 39 (2.3%) did not want to declare their gender. The average age of the respondents was 15.09±1.757. Data were collected using an online form consisted of demographic data, questions related to the use of the Internet, the Internet Addiction Scale, and the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-auto questionnaire version (TEMPS-A) for adolescents. According to the findings of the present study, the relationship between Internet addiction and age, addictive substance use frequency, purpose, time spent on the internet, and type of temperament has been proven. Analysis results indicate that the risk of developing internet addiction is higher if an adolescent spends 1-3 hours and more than 3 hours daily (2.8 and 8.2 times, respectively). Increasing age numbers for 1 unit (year), the risk of internet addiction developing is decreasing by 11.3%. Hyperthymic temperament type decreases the risk of 70.9% of internet addiction developing. It is important to highlight importance of our results since practitioners can utilize our findings to create targeted treatments and prevent internet addiction in adolescents.
Keywords:
Subject: Medicine and Pharmacology - Psychiatry and Mental Health
Introduction
In recent years, the time spent on the Internet by adolescents has increased rapidly. Not only do schools and the educational system rely more and more on Internet activities, but the social lives of adolescents are increasingly conducted through activities on social networks and other online applications (Bickham, 2021). The activities of young people on the Internet during the COVID-19 pandemic have significantly intensified due to the transition of the educational system to online teaching (Lin, 2020). The term "internet addiction" (IA) describes a compulsive behavior that is associated with any online activity and that disrupts everyday life and social interactions. Tolerance, mobility symptoms, time spent online longer than expected, a persistent tendency to impulse control behavior, spending time on online activities, reduced social activity and employment, enjoyment of the effects of internet use, and continued use are among the seven diagnostic criteria (at least three criteria over a two-month period) that the American Psychiatric Association defines for IA (Fitzpatrick, 2008). Internet addiction occurs as a result of excessive use of the internet. As stated by Shek et al. (2013), "problematic internet use" is a synonym for internet addiction, which is defined as an individual's inability to maintain control over their internet use. It has become widely recognized as a serious health concern worldwide. One in eight Americans reports having trouble using the internet (Young & de Abreu, 2011), 2.4% of Chinese people (Cao & Su, 2007), 10.4% of Taiwanese people (Wu et al., 2015), 1.5% and 8.2% of Americans and Europeans (Weinstein & Lejoyeux, 2010), and 3.2% of UK citizens (Kuss et al., 2013) said they were addicted to the internet. According to previous research, the possible causes of excessive Internet use are dysfunctional family, degree of parental circumcision, parental control over the adolescent's free time, the adolescent's personality type (level of impulsivity, degree of self-control), aggressiveness, and neuroticism (Weinstein & Joyeux, 2010). It has also been proven that the effects of excessive use of the Internet on the central nervous system are very similar to the effects of drug addicts, i.e., the response of the brain is very similar and is reflected in the increased secretion of dopamine. Depression, anxiety, attention deficit hyperactivity disorder, and autism are cited as the main consequences of untreated Internet addiction in research, so treatment and therapy are usually reduced to cognitive behavioral therapy, pharmacological preparations, as well as group or family therapy (Kuss & Lopez-Fernandez, 2016). Adolescents are often shown to be the most vulnerable group in terms of developing internet addiction; therefore, it is invaluable to examine the predictors and factors that influence the appearance and outcomes of this "new" form of addiction that has a negative impact on health, especially mental health.
Purpose of the Present Study
In the present study internet addiction has been examined in adolescents in Serbia, and correlation to differences in sex, place of residence, parent’s education, psychoactive substances use, as well as temperament type.
Methods
Sample
The sample of our research consisted of 1,669 adolescents aged 11 to 19 attending primary and secondary schools in Serbia. More detailed information on the sample structure will be provided in the Results section.
Instruments
For the purposes of this study, we created an online form that consisted of five parts. First part was related to the demographic data of the respondents (age, gender, class, level of professional education of the parents, success in the current and previous semester, use of psychoactive substances). Second part was related to the use of the Internet: from which device they connect to the Internet most often, how much time a day they spend on the Internet for school duties and extracurriculars, which group of applications they devote the most time to (social networks, games, viewing media (movies, music, etc.), whether their parents control them in this, and to what extent. Third part consisted of the Internet Addiction Scale, a scale validated on the Croatian adolescent population was used to diagnose Internet addiction, with the author's permission and consent (Černja et al., 2019). This scale consists of 20 questions, which can be answered with 5 answers: 0-never, 1-rarely, 2-occasionally, 3-often, 4-very-often, and 5-always. The test taker can score 0-100 points. The classification of the points obtained is as follows: 0–19 = no signs of Internet addiction; 20–39 = low level of addiction; 40–69 = medium level; and 70–100 = high level of signs—existence of Internet addiction. Fourth part of the questionnaire consisted of the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-auto questionnaire version (TEMPS-A) for adolescents (Akiskal et al., 2005) for personality assessment and as a predictor of pathological behavior and temperament. This scale consists of 41 questions with T (true) and N (not true) options. Constituent subscales and items were formulated based on diagnostic criteria for affective temperaments that distinguish five types: cyclothymic, depressed, irritable, hyperthymic, and anxious (Jović et al., 2019). Fifth part of the questionnaire for adolescents consisted of the Depression Anxiety Stress Scale - DASS 21 (Lovibond, 1995) for the assessment of mental health, but these indicators were not included in the current study.
Design and Procedure
In this research, a cross-sectional epidemiological study was used. Schools were contacted randomly by emails and then, after principal approval, a form link was forwarded to students. For minors (below 18 years) parents signed informed consent, previously approved by University Ethics committee (approval number: 01-6816). Responses were instantly available after form completion and presented in an Excel spread sheet. Every response triggered a notification email in the researcher's mailbox. The protocols used in this study followed the guidelines of the Declaration of Helsinki (World Medical Association, 2013).
Statistical Analyses
Statistical data processing was performed using the standard SPSS software package, version 19.0. (The Statistical Package for Social Sciences software (SPSS Inc, version 19.0, Chicago, IL)). Descriptive statistics measures were used in this research: arithmetic mean, standard deviation, median, quartiles, frequencies, and percentages. The reliability of the measuring scales was tested using the Kronbach coefficient. The normal distribution of numerical variables was checked using the Shapiro-Vilk test and the Kolmogorov-Smirnov test. The correlation of category variables was examined using the Chi-square diagram for the contingent tables. Univariate and multivariate factors analysis was used for predicting internet addiction.
Results
A total of 1,669 respondents participated in this research; 1,040 of them (62.3%) were female, 590 (35.4%) were male, and 39 (2.3%) did not want to declare their gender. The average age of the respondents was 15.09±1.757, with the youngest respondent being 11 and the oldest being 19. Other sociodemographic data about the respondents can be found in Table 1.
Out of the total number of respondents, 1618 (96.9%) respondents use the Internet daily.
The distribution of answers to questions related to Internet use is presented in Table 2.
According to TEMPS-A scale all participants were categorized in one out of five temperament types, whereas majority of those 1018 (61.0%) was categorized as hyperthymic type. As for other temperament types, 518 (31.0%) of them was categorized as depressive, 98 (5.9%) as cyclothymic, 29 (1.7%) as irritable, following with fewest participant categorized as anxious type - 6 (0.4%). After totaling/summing of single Internet Addiction scale questions, IAS score for each participant was obtained. Average value of Internet Addiction Score (IAS) was 28.90±15.210, where minimal score was 0 and maximum was 99. According to this IAS score, all participants were divided categories as follows: 514 (30.8%) with no signs of internet addiction, 773 (46.3%) low level of internet addiction, 361 (21.6%) medium level of internet addiction and 21 (1.3%) high level of internet addiction. Sex (Chi square=20.7. p= .002), attendance to primary or secondary school, (Chi square=21.4, p< .001) and city of residence (Chi square=33.3, p= .004) were sociodemographic characteristics which were significantly related to internet addiction level. Regarding mother’s education, significantly more participants without internet addiction (39.3%), with low internet addiction signs (42.3%) and with medium internet addiction signs (34.8) had a mother with university education (high education level) in relation to the ones with high internet addiction signs (14.3%) (Chi square=20.7, p=0.014). Similar result was observed with relation to father’s education- responders with high level of internet addiction had the least percentage of highly educated fathers comparing to other groups (23.8%) (Chi square=12.1, p= .028). Also, representation of students with excellent (A) grades was the least in the high internet addiction level group (33.3%) relating to other groups (Chi square=42.3, p< .001). Usage of psychoactive substances that may result in developing addiction (Chi square=85.7, p< .001) same as frequency of substances usage was significantly related to internet addiction level (Chi square=58.2, p< .001) as described in Table 3.
Significant relatedness/connection in all characteristics of internet use and internet addiction level sings, was observed (p< .001), as described in Table 4.
All responders with high level of internet addiction signs declared to use internet each day (Chi square=15.4, p= .002), same as usage of internet more than 3 hours per day (Chi square=250.1, p< .001). 71.4% responders in total with high level of internet addiction signs stated that parents don’t control the time they spend on internet, whereas other groups showed significantly low percentage (no signs 38.5%, low level of signs 42.3% and medium level of internet addiction signs 44.3%) (Chi square=14.9, p= .021). Temperament type was statistically significant associated to internet addiction level as described in Table 5. (Chi square=181.9, p< .001).
Responders without or with low level of internet addiction mostly showed as hyperthymic temperament type (79.8% и 60.0%, respectively). In participants with medium internet addiction level, the most represented temperament type was depressive one (50.1%), which was also the case with high level of internet addiction (57.1%).
According to level of internet addiction signs all responders were classified as follows: group without signs of internet addiction: 514 (30.8%) and group with signs of internet addiction signs (low, medium, and high level) 1155 (69.2%). After univariant logistic regression is done, all variables which showed significancy (p< .05) were integrated into multivariant model. This model clarifies 17.6-24.8% variance of dependent variable and has good predictive power according to results of Hosmer and Lemeshow test (p> .05). Accuracy of this model was 73.8%. Significant factors in this model were age, addictive substances use frequency, purpose and time spent on the internet and temperament type. (p< .05) (Table 6).
Other variables that were significant in univariable model, lost their significancy in multivariable model after impact assessment of other variables. Analysis results indicate that risk for internet addiction developing is higher if adolescent is spending 1-3h and more than 3h daily (2.8 and 8.2 times, respectively). Increasing age numbers for 1 unit (year), risk of internet addiction developing is decreased for 11.3%. Hyperthymic temperament type decrease risk for 70.9% from internet addiction developing sings rather than depressive temperament type. Other statistically significant variables are described in the Table 6.
Discussion
This study is focused on examining correlations of Internet addiction in a large sample of adolescents in Serbia, with sociodemographic variables and temperament type. The main aim is determining which variables included in the research have the greatest predictive power when it comes to the development of Internet addiction. The last ten years have seen an exponential increase in the number of individuals using and having access to internet, particularly young people, and adolescents. In this research, it was shown that 96.9% of the surveyed adolescents use the Internet daily, which is in line with previous research conducted around the world (Jhala & Sharma, 2016). Affective temperaments were classified by Akiskal into five categories: hyperthymic, cyclothymic, irritable, anxious, and depressive (Akiskal et al., 2005). In this research, the largest number of respondents belonged to the hyperthymic type (1018 or 61.0%) and to the depressive type of temperament (518 or 31.0%), which is in accordance with the results of the research conducted on a sample of students in Poland (Bartosik et al., 2023).
Based on the IAS score, adolescents in our research were divided into the following groups: 30.8% had no signs of Internet addiction, 46.3% had a low level of addiction, 21.6% had a moderate level of addiction, and 1.3% had a high level of signs of Internet addiction. These findings are consistent with those obtained on a population of 352 students in Croatia, where the results showed that 3.4% of high school students reported high levels of internet addiction, while 35.4% of respondents reported some signs of addiction. It is important to note that the identical instrument used in our study was used for the assessment (Černja et al., 2019).
Results for 31 nations were given in a meta-analysis of 164 independent samples (N = 89,281) by authors Cheng and Li (2014). According to the findings of their research, the Middle East has the highest incidence of high internet addiction (10.9%). The countries of North and West Europe have the lowest prevalence of internet addiction (2.6%), followed by Southeast European countries with a frequency of 6.1% (Cheng & Li, 2014).
Our findings are consistent with the results of a study conducted on a sample of 303 teenagers in Turkey, where it was shown that having a computer at home and using the Internet for more than two years is associated with higher scores on the IAS. A positive and highly significant correlation was found between the results of Internet addiction and the results of depressive, cyclothymic, hyperthymic, irritable, and anxious temperaments. Furthermore, it was found that the frequency of anxious temperament is significantly higher in students with Internet addiction than in those without. Furthermore, emotional, and behavioral problems are more common in adolescents with problematic Internet use (Ozturk et al., 2013).
In this study, a significant correlation was observed in all characteristics of Internet use with the degree of Internet addiction, because all respondents with a high level of signs of Internet addiction stated that they use the Internet every day or use the Internet for more than 3 hours a day, while 71.4% of respondents with a high level of signs of Internet addiction stated that their parents do not control the time they spend on the Internet. Our findings are consistent with research conducted on a sample of 426 students in Saudi Arabia. The same instrument as in our study (20-item IAT) was used to measure Internet addiction, and it was shown that 40.8% of respondents used the Internet 5-7 hours a day, mainly for social networking (88.5%) and to download media files. Approximately 6% were classified as Internet addicts, and 42% had occasional problems. Internet addiction was significantly higher in those who used the Internet for more than 10 hours a day (Kolaib et al., 2020). Also, in a sample of Turkish adolescents, it was shown that having a computer at home and using the Internet for more than two years is associated with higher scores on the IAS (Ozturk et al., 2013).
In our research, after conducting univariate logistic regression, the following were identified as significant factors predicting addiction to the Internet: age, frequency of use of addictive substances, purpose of spending time on the Internet, time spent on the Internet, and type of temperament, which is in agreement with the results of other research (Ozturk et al., 2013; Karacic & Oreskovic, 2017). The connection between Internet addiction and the frequency of use of addictive substances is expected because the characteristic features of both physical and psychological addictions, including mental rumination, fluctuating emotions, tolerance, withdrawal, interpersonal conflict, and relapse, are evident in behavioral addictions (Alavi et al, 2011; Jorgenson et al., 2016). The "self-medication hypothesis" states that patients typically utilize drugs to manage their cognitive deficiencies, lessen excruciating anxiety, and alter undesirable temperamental positions (Durkee et al., 2012). This may be seen in Internet addiction, which is a behavioral addiction, as well as in other behavioral issues that young people face, such as substance abuse.
The previously discussed variables that showed significance in the univariable model lost it in the multivariable model after assessing the influence of other variables, and in the final addiction prediction model, the results of the analysis showed that the risk of developing addiction to the Internet is greater if the child spends more than 1-3 hours and more than 3 hours a day (2.8 and 8.2 times, respectively), that with an increase in the number of years by 1, the risk of Internet addiction decreases by 11.3%, and that the hyperthymic type of temperament decreases the risk of Internet addiction by 70.9% compared to the depressive type of temperament. These findings are expected and in accordance with the research conducted by Kolaib et al. (2020) and Rathi et al. (2022). It has long been known that there is a significant positive correlation between depression and time spent using the Internet. Adolescents with depression and low self-esteem, consequently, may not be able to control their online activity, as it is an escape into an online fantasy and a way of creating an ideal persona that results in more Internet use (Rathi et al., 2022). The preventative efforts ought to center on helping adolescents make good use of their own leisure time. The areas of emotional and social competence, responsible use of media content, and current technology should be the focus of prevention initiatives as well as treatment for youth with high levels of internet dependence. Early program interventions on appropriate and safe internet use are essential to lower the likelihood of high levels of internet addiction during the adolescent years.
Limitation and Future Directions
The limitation of this study is its cross-sectional design, which limits its ability to establish a cause-effect relationship between the outcome and the independent variables. Despite this, it is important to note that our results are invaluable because practitioners can utilize our findings to create targeted treatments and avoid internet addiction in adolescents. It would be interesting to investigate the relationship between the prevalence of internet addiction and the emergence of other problematic behaviors in young people, such as problematic behavior and other behavioral issues common in the youth population using prospective and longitudinal follow-up studies.
Conclusion
Girls, adolescents surfing on internet via smartphones, the ones from larger cities will develop internet addiction more likely. Adolescents with internet addiction will have decreased grades in school more likely. Use of psychoactive substances was not significant predictor of developing Internet addiction, but temperament type was observed as significant Internet addiction predictor.
According to the findings of the present study, the relationship between Internet addiction and age, addictive substance use frequency, purpose, time spent on the internet, and type of temperament has been proven. Addiction to the Internet is a relatively common phenomenon among adolescents, and in our research, the largest number of respondents had a moderate addiction to the Internet. That's why those who show indicators of mild internet addiction are a population that requires extra attention in terms of prevention. More research in this area should be conducted using a systematic, multivariate, psycho-social conceptual framework.
Author Note: This research received no external funding therefore with no conflicts of interest to disclose. The authors do not have any conflicts of interest to report.
References
- Akiskal, H. S., Mendlowicz, M. V., Jean-Louis, G., Rapaport, M. H., Kelsoe, J. R., Gillin, J. C., & Smith, T. L. (2005). TEMPS-A: validation of a short version of a self-rated instrument designed to measure variations in temperament. Journal of affective disorders, 85(1-2), 45-52. [CrossRef]
- Alavi, S. S., Maracy, M. R., Jannatifard, F., & Eslami, M. (2011). The effect of psychiatric symptoms on the internet addiction disorder in Isfahan's University students. Journal of research in medical sciences: the official journal of Isfahan University of Medical Sciences, 16(6), 793.
- Bartosik, N. K., Frankowski, R., Kobierecki, M., Deska, K., Twarowski, A., Bąk, B., ... & Pietras, T. (2023). The association between affective temperaments and depressive symptoms in a population of medical university students, Poland. Frontiers in Psychiatry, 14, 1077940. [CrossRef]
- Bickham, D. S. (2021). Current research and viewpoints on internet addiction in adolescents. Current pediatrics reports, 9, 1-10. [CrossRef]
- Cao, F., & Su, L. (2007). Internet addiction among Chinese adolescents: prevalence and psychological features. Child: care, health, and development, 33(3), 275-281. [CrossRef]
- Černja, I., Vejmelka, L., & Rajter, M. (2019). Internet addiction test: Croatian preliminary study. BMC psychiatry, 19, 1-11. [CrossRef]
- Cheng, C., & Li, A. Y. L. (2014). Internet addiction prevalence and quality of (real) life: A meta-analysis of 31 nations across seven world regions. Cyberpsychology, behavior, and social networking, 17(12), 755-760. [CrossRef]
- Durkee, T., Kaess, M., Carli, V., Parzer, P., Wasserman, C., Floderus, B., ... & Wasserman, D. (2012). Prevalence of pathological internet use among adolescents in Europe: demographic and social factors. Addiction, 107(12), 2210-2222. 2210. [CrossRef]
- Fitzpatrick, J. J. (2008). Internet addiction: recognition and interventions. Archives of psychiatric nursing, 22(2), 59-60. Archives of psychiatric nursing. [CrossRef]
- Jhala, J., & Sharma, R. (2016). Internet use among adolescents. Journal of Indian Association for Child and Adolescent Mental Health, 12(1), 36-59.
- Jorgenson, A. G., Hsiao, R. C. J., & Yen, C. F. (2016). Internet addiction and other behavioral addictions. Child and Adolescent Psychiatric Clinics, 25(3), 509-520. [CrossRef]
- Jović, J., Hinić, D., Ćorac, A., S Akiskal, H., Akiskal, K., Maremmani, I., ... & Ristić-Ignjatović, D. (2019). The development of temperament evaluation of Memphis, Pisa, Paris, and San Diego-auto-questionnaire for adolescents (A-TEMPS-A) in a Serbian sample. Psychiatria Danubina, 31(3), 308-315. [CrossRef]
- Karacic, S., & Oreskovic, S. (2017). Internet addiction through the phase of adolescence: a questionnaire study. JMIR mental health, 4(2), e5537. [CrossRef]
- Kolaib, A. M. A., Alhazmi, A. H. H., & Kulaib, M. M. A. (2020). Prevalence of internet addiction and its associated factors among medical students at Taiba University, Saudi Arabia. Journal of family medicine and primary care, 9(9), 4797. [CrossRef]
- Kuss, D. J., & Lopez-Fernandez, O. (2016). Internet addiction and problematic Internet use: A systematic review of clinical research. World journal of psychiatry, 6(1), 143. [CrossRef]
- Kuss, D. J., Griffiths, M. D., & Binder, J. F. (2013). Internet addiction in students: Prevalence and risk factors. Computers in Human Behavior, 29(3), 959-966. [CrossRef]
- Lin, M. P. (2020). Prevalence of internet addiction during the COVID-19 outbreak and its risk factors among junior high school students in Taiwan. International journal of environmental research and public health, 17(22), 8547. [CrossRef]
- Lovibond, S. H. (1995). Manual for the depression anxiety stress scales. Sydney psychology foundation.
- Ozturk, F. O., Ekinci, M., Ozturk, O., & Canan, F. (2013). The relationship of affective temperament and emotional-behavioral difficulties to internet addiction in Turkish teenagers. International Scholarly Research Notices Psychiatry, 28, 1-6. [CrossRef]
- Rathi, M., Guha, P., & Neogi, R. (2022). Internet addiction in adolescents: Role of family, personality, and comorbid psychopathology in school children in Eastern India. Indian Journal of Psychiatry, 64(4), 408. [CrossRef]
- Shek, T. L., Sun, R. C. F., & Yu, L. (2013). Internet addiction. In Neuroscience in the 21st Century: From Basic to Clinical (pp. 2775-2811). Springer: New York.
- Weinstein, A., & Joyeux, M. (2010). Internet addiction or excessive internet use. The American journal of drug and alcohol abuse, 36(5), 277-283. [CrossRef]
- Weinstein, A., & Lejoyeux, M. (2010). Internet addiction or excessive internet use. The American journal of drug and alcohol abuse, 36(5), 277-283. [CrossRef]
- World Medical Association. (2013). World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. Jama, 310(20), 2191-2194. [CrossRef]
- Wu, C. Y., Lee, M. B., Liao, S. C., & Chang, L. R. (2015). Risk factors of internet addiction among internet users: an online questionnaire survey. PloS one, 10(10), e0137506. [CrossRef]
- Young, K. S., & de Abreu, C. N. (Eds.). (2011). Internet addiction: A handbook and guide to evaluation and treatment. John Wiley & Sons, Inc.
Table 1.
Sociodemographic characteristics.
| Question | Categories | N (%) |
| Do you go to primary or secondary school? | Primary school | 381(22.8) |
| Secondary school | 1288(77.2) | |
| Do you have brother or sister from same parents? | No | 285(17.1) |
| Yes | 1384(82.9) | |
| Which city is your place of residence (or closest)? | Belgrade | 670(40.1) |
| Sombor | 788(47.2) | |
| Kragujevac | 22(1.3) | |
| Krusevac | 82(4.9) | |
| Nis | 37(2.2) | |
| Other | 70(4.2) | |
| Your parents are? | Divorced or separated | 341(20.4) |
| Married | 1253(75.1) | |
| Live together | 75(4.5) | |
| Your mother’s education? | Primary school | 79(4.7) |
| Secondary school | 747(44.8) | |
| College | 188(11.3) | |
| Faculty | 655(39.2) | |
| Your father’s education? | Primary school | 99(5.9) |
| Secondary school | 833(49.9) | |
| College | 179(11.3) | |
| Faculty | 548(32.8) | |
| Your grades last school year were? | Acceptable (2, D) | 22(1.3) |
| Good (3, C) | 193(11.6) | |
| Very good (4, B) | 555(33.3) | |
| Excellent (5, A) | 899(53.9) | |
| Do you use any of these substances? | No, nothing | 1134(68.0) |
| E- cigars, alcohol | 217(13.0) | |
| tobacco | 100(6.0) | |
| alcohol | 198(11.9) | |
| Marihuana | 4(0.2) | |
| Sleeping pills | 13(0.8) | |
| other | 2(0.1) | |
| How often do you use above mentioned? | Never | 1087(65.1) |
| Rarely | 176(10.5) | |
| Only when I go out with my friends | 220(13.2) | |
| Every day | 186(11.1) |
Table 2.
Distribution of responses related to internet usage.
| Question | Categories | N (%) |
| Usually, I use? to go to internet | Computer | 126(7.5) |
| Smartphone | 1511(90.5) | |
| Laptop | 32(1.9) | |
| Internet Content mostly visited? | Gaming | 202(12.1) |
| Social networks (Facebook, Instagram, Snapchat, Tik Tok...) | 1060(63.5) | |
| Media content (YouTube, Spotify, Netflix, HBO…) | 371(22.2) | |
| School activities | 36(2.2) | |
| Do you use internet every day? | No | 51(3.1) |
| Yes | 1618(96.9) | |
| If every day used, approximately how long? | max 1h | 109(6.5) |
| 1h-3h | 617(37.0) | |
| More than 3h | 943(56.5) | |
| Does your parents control time you spend on internet? | No | 700(41.9) |
| Sometimes | 694(41.6) | |
| Yes | 275(16.5) | |
| Do you use internet for school activities? | Never | 81(4.9) |
| Sometimes | 748(44.8) | |
| Often | 840(50.3) |
Table 3.
Sociodemographic characteristics related to internet addiction presence.
| Question | No signs | Low level | Medium level | High level | Chi – Square/p value | |
| Sex | Female | 299(58.2) | 504(65.2) | 227(62.9) | 10(47.6) | 20.7/ .002 |
| Male | 204(39.7) | 253(32.7) | 125(34.6) | 8(38.1) | ||
| Undeclared | 11(2.1) | 16(2.1) | 9(2.5) | 3(14.3) | ||
| Do you attend primary or secondary school? | Primary | 152(29.6) | 152(19.7) | 70(19.4) | 7(33.3) | 21.4/< .001 |
| Secondary | 362(70.4) | 621(80.3) | 291(80.6) | 14(66.7) | ||
| Siblings from same parents? | No | 91(17.7) | 121(15.7) | 69(19.1) | 4(19.0) | 2.4/ .500 |
| Yes | 423(82.3) | 652(84.3) | 292(80.9) | 17(81.0) | ||
| Nearest city for residence? | Belgrade | 184(35.8) | 344(44.5) | 138(38.2) | 4(19.0) | 33.3/ .004 |
| Sombor | 265(51.6) | 329(42.6) | 180(49.9) | 14(66.7) | ||
| Kragujevac | 5(1.0) | 15(1.9) | 1(0.3) | 1(4.8) | ||
| Krusevac | 26(5.1) | 34(4.4) | 22(6.1) | 0(0.0) | ||
| Nis | 11(2.1) | 18(2.3) | 6(1.7) | 2(9.5) | ||
| Other cities | 23(4.5) | 33(4.3) | 14(3.9) | 0(0.0) | ||
| Your parents are? | Divorced or separated | 91(17.7) | 155(20.1) | 90(24.9) | 5(23.8) | 9.6/ .143 |
| Married | 403(78.4) | 583(75.4) | 253(70.1) | 14(66.7) | ||
| Live together | 20(3.9) | 35(4.5) | 18(5.0) | 2(9.5) | ||
| Mother’s education? | Primary school | 26(5.1) | 32(4.1) | 18(5.0) | 3(14.3) | 20.7/ .014 |
| Secondary school | 240(46.7) | 327(42.3) | 169(46.8) | 11(52.4) | ||
| College | 46(8.9) | 87(11.3) | 51(14.1) | 4(19.0) | ||
| University degree | 202(39.3) | 327(42.3) | 123(34.9) | 3(14.3) | ||
| Father’s education? | Primary school | 31(6.0) | 39(5.0) | 27(7.5) | 2(9.5) | 12.1/ .028 |
| Secondary school | 262(51.0) | 344(47.1) | 196(54.3) | 11(52.4) | ||
| College | 59(11.5) | 92(11.9) | 35(9.7) | 3(14.3) | ||
| University degree | 162(31.5) | 278(36.0) | 103(28.5) | 5(23.8) | ||
| School grades last year? | Acceptable (2, D) | 4(0.8) | 9(1.2) | 7(1.9) | 2(9.5) | 42.3/< .001 |
| Good (3, C) | 61(11.9) | 70(9.1) | 58(16.1) | 4(19.0) | ||
| Very good (4, B) | 161(31.3) | 245(31.7) | 141(39.1) | 8(38.1) | ||
| Excellent (5, A) | 288(56.0) | 449(58.1) | 155(42.9) | 7(33.3) | ||
| Do you use any of below mentioned substances? | No | 392(76.3) | 531(68.8) | 197(54.6) | 14(66.7) | 85.7/< .001 |
| e-cigars, alcohol | 39(7.6) | 88(11.4) | 85(23.5) | 5(23.8) | ||
| tobacco | 31(6.0) | 40(5.2) | 28(7.8) | 1(4.8) | ||
| alcohol | 49(9.5) | 103(13.3) | 46(2.7) | 0(0.0) | ||
| Marihuana | 2(0.4) | 2(0.3) | 0(0.0) | 0(0.0) | ||
| Sleeping pills | 1(0.2) | 8(1.0) | 3(0.8) | 1(4.8) | ||
| Other drugs | 0(0.0) | 0(0.0) | 2(0.6) | 0(0.0) | ||
| How often do you use above mentioned? | Never | 383(74.5) | 506(65.5) | 187(51.8) | 11(52.4) | 58.2/< .001 |
| Rarely | 45(8.8) | 82(10.6) | 48(13.3) | 1(4.8) | ||
| Only when I go out with friends | 43(8.4) | 107(13.8) | 67(18.6) | 3(14.3) | ||
| Every day | 43(8.4) | 78(10.1) | 59(16.3) | 6(28.6) | ||
Table 4.
Characteristics of internet usage associated with internet addiction.
| Question | No signs | Low level | Medium level | High level | Chi – Square / p value | |
| Usually, I visit internet via? | Computer | 43(8.4) | 41(5.3) | 40(11.1) | 2(9.5) | 20.1/ .003 |
| Smart phone | 457(88.9) | 724(93.7) | 311(86.1) | 19(90.5) | ||
| Laptop | 14(2.7) | 8(1.0) | 10(2.8) | 0(0.0) | ||
| Usual time spent on internet? | Gaming | 58(11.3) | 73(9.4) | 65(18.0) | 6(28.6) | 75.0/< .001 |
| Social networking (Facebook, Instagram, Snapchat, Tik Tok...) | 282(56.2) | 510(66.0) | 248(68.7) | 13(61.9) | ||
| Media (YouTube, Spotify, Netflix, HBO…) | 143(27.8) | 180(23.3) | 47(13.0) | 1(4.8) | ||
| For school activities | 24(4.7) | 10(1.3) | 1(0.3) | 1(4.8) | ||
| Do you use internet each day? | No | 28(5.4) | 18(2.3) | 5(1.4) | 0(0.0) | 15.4/ .002 |
| Yes | 486(94.6) | 755(97.7) | 356(98.6) | 21(100.0) | ||
| If used each day, what time approximately would that be? | 1h max | 76(14.8) | 28(3.6) | 5(1.4) | 0(0.0) | 250.1/< .001 |
| 1h-3h | 266(51.8) | 288(37.3) | 63(17.5) | 0(0.0) | ||
| More than 3h | 172(33.5) | 457(59.1) | 293(81.2) | 21(100.0) | ||
| Do your parents control the time you spend on internet? Do they warn you? | No | 198(38.5) | 327(42.3) | 160(44.3) | 15(71.4) | 14.9/ .021 |
| Sometimes | 214(41.6) | 331(42.8) | 145(40.2) | 4(19.0) | ||
| Yes | 102(19.8) | 115(14.9) | 56(15.5) | 2(9.5) | ||
| Do you use internet for school activities and learning? | Never | 22(4.3) | 31(4.0) | 24(6.6) | 4(19.0) | 29.9/< .001 |
| Sometimes | 206(40.1) | 344(44.5) | 187(51.8) | 11(52.4) | ||
| Often | 286(55.6) | 398(51.5) | 150(41.6) | 6(28.6) | ||
Table 5.
Temperament type in association with internet addiction.
| No signs | Low level | Medium level | High level | Chi – Square / p value | ||
| Temperament type | Depressive | 74(14.4) | 251(32.5) | 181(50.1) | 12(57.1) | 181.9/<0.001 |
| Cyclothymic | 16(3.1) | 42(5.4) | 37(10.2) | 3(14.3) | ||
| Hyperthymic | 410(79.8) | 464(60.0) | 138(38.2) | 6(28.6) | ||
| Irritable | 11(2.1) | 13(1.7) | 5(1.4) | 0(0.0) | ||
| Anxious | 3(0.6) | 3(0.4) | 0(0.0) | 0(0.0) | ||
Table 6.
Univariant and multivariant factor analysis for internet addiction prediction.
| Variable | Univariant logistic regression | Multivariant logistic regression | |||
| Odds Ratio | p | Odds Ratio | p | ||
| Age | 11-19 years | 1.105(1.042-1.173) | .001 | 0.887(0.791-0.895) | .040 |
| Sex |
Female | 1 | ref | 1 | ref |
| Male | 0.764 (0.615-0.948) | .015 | 0.975(0.748-1.271) | .850 | |
| Primary or secondary school respondents? |
Primary | 1 | ref | 1 | ref |
| Secondary | 1.698 (1.338-2.155) | < .001 | 1.501(0.980-2.300) | 0.062 | |
|
Parents relation? |
Separated or divorced |
1 |
ref |
1 |
ref |
| Married | 0.768(0.587-0.965) | .05 | 0.899(0.665-1.215) | .487 | |
| City of residence? |
Belgrade | 1 | ref | 1 | ref |
| Sombor | 0.747 (0.597-0.936) | .011 | 0.807(0.600-2.138) | .157 | |
| Addictive substances use? | None | 1 | ref | 1 | ref |
| e-cigars, alcohol | 2.411(1.670-3.482) | < .001 | 0.657(0.295-1.461) | .303 | |
| Alcohol | 1.606(1.138-2.269) | .007 | 0.607 0.267-1.379() | .233 | |
| How often above mentioned? | Never | 1 | ref | 1 | ref |
| Rarely | 1.584(1.104-2.272) | .012 | 2.087(0.941-4.628) | .070 | |
| Only when go out with friends | 2.239(1.569-3.196) | < .001 | 2.490(1.106-5.605) | .028 | |
| Each day | 1.809(1.259-2.601) | .001 | 2.022(0.948-4.315) | .069 | |
| Internet content spending time often? | Games | 1 | ref | 1 | ref |
| Media (YouTube, Spotify, Netflix, HBO…) | 0.642(0.444-0.929) | .019 | 0.616(0.399-0.951) | .029 | |
| School activities | 0.201(0.094-0.429) | < .001 | 0.261(0.110-0.620) | .002 | |
| Do you use internet each day? | No | 1 | ref | 1 | ref |
| Yes | 2.836(1.617-4.973) | < .001 | 1.116(0.581-2.142) | .742 | |
| How much time do you spend on internet daily? | 1h max | 1 | ref | 1 | ref |
| 1h-3h | 3.039(1.960-4.712) | < .001 | 2.775(1.706-4.512) | < .001 | |
| More than 3h | 10.323(6.644-16.042) | < .001 | 8.205(4.962-13.568) | < .001 | |
| Does your parents control time you spend on internet? | No | 1 | ref | 1 | ref |
| Yes | 0.669(0.498-0.898) | .008 | 0.984(0.695-1.393) | .927 | |
| Temperament type | Depressive | 1 | ref | 1 | ref |
| Hyperthymic | 0.247(0.188-0.326) | < .001 | 0.291(0.216-0.392) | < .001 | |
| Irritable | 0.273(0.124-0.601) | .001 | 0.332(0.137-0.803) | .014 | |
| Anxious | 0.167(0.033-0.841) | .030 | 0.088(0.016-0.497) | .006 | |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
MDPI Initiatives
Important Links
© 2024 MDPI (Basel, Switzerland) unless otherwise stated