1. Introduction
Problematic internet use, also commonly named compulsive internet use, internet addiction, pathological internet use, or internet use disorder, refers to any type of use of the internet that is not well controlled and leads to difficulties in daily functioning [1]. These difficulties include but are not limited to decline in educational or professional achievements, decreased sleeping time, reduced quality of meals, and negative effects on familial or partner relationships [2]. The addictive potential of the internet has been recognized for more than three decades now; however, the exact terminology has still been disputed and the term ‘problematic internet use’ is now often used as it does not include the pathology and allows further detailed examination of the phenomenon [3].
1.1. Concept of Problematic Internet Use
Problematic internet use has been described as a maladaptive coping mechanism for meeting the psychosocial needs unmet in the ‘offline’ environment, for overcoming the lack of perceived social support and/or coping with negative affect [3]. Although problematic internet use has been thoroughly examined in the past decade along with its inconsistent terminology, there has been an inconsistency in the definitions of problematic internet use, criteria for its diagnosis, and the modalities of assessment [4]. Currently, problematic internet use is defined in the behavioral addiction spectrum as a repeated online behavior that leads to harm, and the person is not able to control the online behavior [5]. Additionally, it is important to note that this behavior persists over a significant time period, that it is associated with functional impairments, and interferes with relationships, work or education, and mental health [5]. It can be differentiated into two types: general and specific (online gaming, social media addiction, etc.) [5].
1.2. Factors Associated with Problematic Internet Use
As with other psychological issues, problematic internet use is associated with socio-demographic factors, such as gender, age, or socio-economic status [6,7]. Although women and girls tend to use the internet more, some studies have found that problematic internet use is more common and more severe among males [8,9]. However, others did not find this association, and the specific problematic internet use may be more common among females, unlike the generalized one [8], meaning that if different types of problematic internet use are examined, the differences between genders disappear [8,10]. Problematic internet use is also associated with younger age groups, as these groups tend to use the internet more and use it more easily [11]. Lower socio-economic status has also been associated with problematic internet use, and it is speculated that those with better socio-economic status tend to choose leisure activities such as going out, visiting cinemas, or fitness clubs [12]. Obesity has been a growing public health issue in the past decades globally, especially among young adults. The increasing body-mass index (BMI) has been associated with higher levels of internet addiction [13,14]. The association may be bidirectional as young adults with high BMI may tend to spend more time in sedentary activities, and on the opposite side, more time spent in sedentary activities may be associated with higher BMI [14]. Consequently, problematic internet use is also associated with low physical activity [13].
Studies examining personality traits and problematic internet use found somewhat conflicting results [11,15]. Extraversion as a personality characteristic has been shown to both increase and decrease the likelihood of developing problematic internet use, as it can, on one hand, increase social interactions and, therefore, decrease the likelihood of problematic internet use [15]. On the other, extraversion could be associated with the overuse of social media and be associated with this type of specific problematic internet use [11]. Certain symptoms of mental health problems seem to be more common among those with more intensive internet use, especially anxiety and mood disorders [2,16], and may present the pre-existing pathology leading to problematic internet use [3]. It is also associated with loneliness, low social support, low self-esteem, and difficulties in social interactions [16], as well as lifestyle issues like smoking, sedentary behaviors, and obesity [6]. Substance use, alcohol use, and cannabis use have also been associated with problematic internet use previously. The model on risky behavior among youth states that one risky behavior is the risk factor for another risky behavior. These behaviors can present symptoms of psychological issues that already exist, such as anxiety or depression, but could also be explained with a common neurobiological basis of substance use and behavioral addictions [17,18,19]. Problematic internet use can also be associated with a lower risk for alcohol and cannabis use, as it can decrease social interactions and the occasions for trying alcohol or other substances and can also stimulate the dopaminergic circuit and, in that way, lower the need for trying the substances [19].
The associations between problematic internet use and loneliness could be bidirectional. On the one hand, online communications replacing face-to-face communications may lead to lower levels of intimacy, while on the other, those who feel lonely may try to increase social interactions through online communications [20]. Loneliness is also associated with symptoms of depression and anxiety, shyness, and social support, all associated with problematic internet use, as well [21]. These feelings have been augmented during the pandemic, associated with more time spent online and being at risk of being exposed to cyberbullying [22]. Being single was also previously associated with problematic internet use [13], as single college students may look for companionship on the internet, especially through online dating sites.
Personality characteristics are also associated with a higher likelihood of the development of problematic internet use. Problematic internet use is more common in those unable to resist temptation, urges, or impulses, and the association between problematic internet use and state impulsivity has been shown repeatedly [6,23,24], and impulsivity may mediate the relationship between the family functioning and problematic internet use [25]. Young adults may develop problematic internet use as a response to negative emotions associated with family conflicts or helicopter parenting, similar to the development of other risky behaviors such as gambling, illegal drug use, or cigarette smoking, as it may be associated with problems with self-regulation [26,27].
The genetic component of problematic internet use has also been examined, and the twin studies have shown the genetic bases of problematic internet use in up to 70% of cases, although different genes have been examined regarding this, including the gene for regulation of serotonergic neurotransmission (5HTTLPR), the gene for monoamine-oxidase-A gene, and catechol-O-methyltransferase (COMT), which is the enzyme for embolism of dopamine [28]. The genetic component seems to be more significant in adolescents than in adults, showing that it can vary over time [28].
1.3. Epidemiology of Problematic Internet Use
The exact prevalence of problematic internet use is in most countries still not determined, as there are still not enough studies on problematic internet use on the representative samples, and the majority of the studies so far have examined school-aged children and adolescents [29]. The prevalence among adolescents and young adults varies between 1% and 18.7% in different countries [30,31]. The prevalence among adults in Slovenia was 3.1% in 2016 [16], and problematic internet use was associated with younger age, education, and being single [16].
1.4. Problematic Internet Use Among Youths and Students
As young adults could still be considered as developmentally not fully matured, they have a heightened likelihood for problematic internet use [29,30]; this is aggravated by the lack of parental control and living away from family members [31,32]. Additionally, the recent literature review, which included total of a 23 studies, showed that young adults may turn to problematic internet use as a coping mechanism to compensate for emotional regulation deficits [33]. Therefore, problematic internet use has slowly become an important issue among the university students with an estimated prevalence between 6% and 19% [31,34]. The prevalence of PIU among young adults in the US in an online survey among 6000 participants was 53.58% [35]. On the one hand, there has been significant improvement in the learning process with the introduction of internet services in universities, but on the other, students use the internet for entertainment, communication, shopping, or even gambling [31]. Lower academic performance can be a first indication of problematic internet use, followed by depression, social anxiety, sleep disturbances, and daytime sleepiness [31,35,36,37]. Medical students, as future physicians, should be educated in the healthy use of the internet and the characteristics of problematic internet use in order to help them understand and recognize this emerging issue and to educate them for their future work [7]. The population of young adults should be thoroughly screened for possible problematic internet use, and the proper preventive strategies should be developed. In order to achieve this, there is a need for wide agreement on the conceptualization of PIU and possibilities of its assessment with valid, reliable instruments for the assessment of problematic internet use and determination of factors that are associated with its use, and the protective factors that could be promoted to prevent its occurrence [38].
1.5. Instruments for the Assessment of Problematic Internet Use
Numerous questionnaires have been developed with the aim to examine problematic internet use so far [2,39,40,41]. Some of them are based on the criteria for diagnosing pathological gambling or substance abuse in accordance with criteria in the Diagnostic and Statistical Manual IV, but in general, these scales differ in the theoretical background use, criteria for diagnosis, and cutoffs used. The most commonly used are the Internet Addiction Test (IAT), Problematic Internet Use Questionnaire (PIUQ), and Compulsive Internet Use Scale. The Problematic Internet Use Questionnaire developed by Demetrovics et al. in 2008 is the most commonly used and describes PIU in three factors: obsession, neglect, and loss of control [1,4]. The lack of agreement on the criteria and the factors of PIU in questionnaires have been associated with the varying prevalence of PIU shown in studies so far [42] even among the same populations, such as adolescents, in which the prevalence varies between 1% and 9% [42].
The research questions in this study were as follows: Is the Problematic Internet Use Questionnaire Short Form (PIUQ-SF-6) a valid instrument for the assessment of problematic internet use among students in Serbia? What is the prevalence of problematic internet use among medical students in Serbia, and are there any social, lifestyle, and mental health characteristics associated with problematic internet use among medical students in Serbia?
Based on these questions, the main hypotheses in our study were as follows: The Problematic Internet Use Questionnaire Short Form (PIUQ-SF-6) is valid and reliable. The prevalence of problematic internet use among medical students in Serbia is similar to the prevalence described previously in the student population. Sex, age, and lifestyles characteristics like smoking, alcohol consumption, and low physical activity are associated with problematic internet use, along with mental health characteristics such as anxiety and depression.
The aim of this study was to examine the validity and reliability of the Problematic Internet Use Questionnaire Short Form (PIUQ-SF-6) among the Serbian medical students and to examine the prevalence of students at risk of problematic internet use and social, lifestyle, and mental health characteristics associated with it.
2. Materials and Methods
This study was conducted as a cross-sectional study that all fifth-year medical students at five universities in Serbia that have Faculty of Medicine (Belgrade, Kosovska Mitrovica, Kragujevac, Novi Sad, and Nis). We included a total of 735 medical students from five universities in Serbia. Most students were from the University of Belgrade, 431 (57.1%), followed by the University of Novi Sad, 164 (21.7%), the University of Nis, 71 (9.4%), Kosovska Mitrovica, 48 (6.4%), and the University of Kragujevac, 41 (5.4%). All students who had exercises in Social Medicine at the time of the research (school year 2023/2024) were given information about the study, its processes, and aims and gave oral consent for participation in the study. The Ethical Committees of all faculties included in the study approved the research (No. 17/X-15). A total of 248 students overall were not present in classes during the time of research, with the response rate of the entire population of fifth-year medical students at 74.8%. Slovin’s formula was used to calculate the sample size. The minimal calculated sample size was 609 participants, and we added 20% as we predicted that there would be incomplete data in some questionnaires. The total predicted sample size was 731 participants, and a total of 735 participants were invited to participate in the study and filled in the questionnaire.
Measures
The study instrument was a questionnaire that consisted of eight sections: (1) social and lifestyle characteristics; (2) study engagement; (3) physical activity; (4) social support; (5) Zung anxiety scale; (6) Patient Health Questionnaire (PHQ-9); (7) state impulsivity scale; (8) problematic internet use questionnaire. Social and lifestyle characteristics were examined using the questions from the previous similar studies and included type of residence (urban vs. rural), sex (male vs. female), age in years, height in centimeters, weight in kilograms, relationship status (in a relationship/single), self-perceived financial status (poor, average, good), family relations (poor, average, good), self-rated health (poor, average, good), alcohol use (yes in a previous month/yes in a previous year, but not in a previous month/no), cannabis use (yes/no), use of anti-anxiety medications in the past 12 months (yes/no), use of any tobacco product (yes/no) [43,44].
Study engagement was examined using the study engagement scale, a nine-item scale measuring vigor, dedication, and absorption in association with studying. All items on this scale were rated on a six-point scale ranging from 1—completely disagree to 6 strongly agree [45]. The Cronbach’s alpha for the study engagement scale was α = 0.507. Physical activity was examined using the International Physical Activity Questionnaire Short Form—IPAQ SF [46,47]. This questionnaire allows the calculations of the total weekly energy expenditure and allows the classification of participants into three groups: low physical activity (<600 MET-min/week); moderate physical activity (601–3000 MET-min/week), and vigorous physical activity (>3000 MET-min/week). The Cronbach’s alpha for the IPAQ-SF was α = 0.438.
Social support was examined using the multidimensional scale of perceived social support (MSPSP) [48,49], a self-rating instrument that contains 12 items in three subscales: support from family, friends, and partner. The answers on this scale are provided on a 7-point Likert scale ranging from 1—completely disagree to 7—completely agree. The Cronbach’s alpha for the multidimensional scale of perceived social support was α = 0.785.
Anxiety was examined using the Zung anxiety scale, a 20-item scale with answers varying from 1—never to 4—all of the time. The scores on the Zung anxiety scale vary from 20 to 80 [50]. The Cronbach’s alpha for the Zung anxiety scale was α = 0.845.
Depressive symptoms were examined using the PHQ-9, a nine-item, self-rated scale, with answers provided on a four-point Likert scale, ranging from 0—not at all to 3— nearly every day [51]. The Cronbach’s alpha for the PHQ-9 scale was α = 0.862.
Impulsivity was examined using the state impulsivity scale [52], a 20-item questionnaire, with answers provided on a four-point Likert scale, ranging from 0—almost never to 3—almost always/always. The Cronbach’s alpha for the state impulsivity scale was α = 0.934.
Problematic internet use was examined using the Problematic Internet Use Questionnaire Short Form—PIUQ-SF-6 [16], derived from the 18-item Problematic Internet Use Questionnaire [2,53]. The PIUQ-SF-6 examines three dimensions of problematic behavior associated with internet use: obsession, neglect, and control disorder (two questions for each dimension). The answers are provided on a five-point Likert scale, ranging from 1—never to 5—always/almost always [16,54]. The scores of 15 or above are considered problematic internet use [16].
We analyzed a total of 19 variables. These were problematic internet use, type of residence, sex, age, body-mass index, relationship status, self-perceived financial status, family relationships, self-rated health, alcohol use, cannabis use, use of anti-anxiety medications, use of any tobacco product, PHQ-9 score, Zung anxiety scale score, state impulsivity scale score, study engagement, social support, and physical activity.
The statistical analyses were conducted using the methods of descriptive and analytical statistics. The reliability of the PIUQ-SF-6 scale and the subscales (obsession, neglect, control disorder) was examined using Cronbach’s alpha. For the factor analyses, the sample was randomly divided in two samples of approximately 50% of the size of the total sample. Exploratory factor analysis was conducted in the first half; confirmatory factor analysis was conducted in the second half. Exploratory factor analysis was conducted to explore the factor structure based on the original construct. The extraction of factors was conducted using the Promax rotation, as the hypothesis was that the factors are correlated. Goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), and root mean error of approximation (RMSEA) were used to assess the construct validity. The cut-off values for the assessment of the model were GFI ≥ 0.90, AGFI ≥ 0.90, CFI ≥ 0.95, and RMSEA of <0.10 [55,56,57]. The differences between groups were examined using the Chi-square test for categorical variables. The differences in numerical variables with the normal distribution were examined using the t-test, and without normal distribution were examined using the Mann–Whitney U-test. The normality was examined using the Shapiro–Wilk and Kolmogorov–Smirnov tests. All variables that were shown to be significant were entered in the multivariate logistic regression model with problematic internet use as an outcome variable. All analyses were conducted using the Statistical Package for Social Sciences SPSS 22.0 (IBM, Armonk, NY, USA).
3. Results
The average age of the participants was 23.90 ± 1.47. The majority of students were female, 502 (66.8%). More than half of our students assessed their financial status as good (54.5%), almost 80% assessed their family relationship as good, and their health status as good (83.2%).
The average score on the PIUQ 18 scale was 33.54 ± 12.86, and on the PIUQ 6 scale, it was 11.05 ± 4.54. The correlation coefficient for the two scores was r = 0.953. A total of 160 students (21.8%) were in the problematic internet use group, and 575 were in non-problematic internet use group.
The scatter plot graph for the two scales is presented in Figure 1.
Median scores on each item of PIUQ 6 ranged from 1 to 3, means ranged from 1.42 ± 0.93 to 2.58 ± 1.19, and skewness varied significantly from 0.10, indicating symmetric distribution to 1.56, indicating highly skewed distribution. The median score, mean score, and skewness on each item of the PIUQ6 questionnaire are presented in Table 1.
Cronbach’s alpha for the entire PIUQ6 scale was α = 0.813, for the obsession scale was α = 0.780, for the neglect subscale was α = 0.580, and for the control disorder subscale, Cronbach’s alpha was α = 0.640.
Barlett’s test of sphericity was statistically significant, p < 0.001, and Keiser Mayer Olkin’s measure of sampling adequacy was 0.817. Exploratory factor analysis showed one factor that explained 52.93% of the total variance. Factor loading varied between 0.632 and 0.786. The lowest factor loading was for the factor ‘How often do you spend time online when you’d rather sleep?’ and the highest was for the factor ‘How often does it happen to you that you feel depressed, moody, or nervous when you are not on the Internet and these feelings stop once you are back online?’. Factor loadings for each of the items are presented in Table 2.
Confirmatory factor analysis for the one-factor model showed GFI = 0.944, AGFI = 0.869, CFI = 0.911, and RMSEA = 0.14 (95% CI: 0.12–0.16). Confirmatory factor analysis for the literature three-factor model showed GFI = 0.979, AGFI = 0.928, CFI = 0.970, and RMSEA = 0.09 (95% CI: 0.07–0.12). The results of the confirmatory factor analyses are presented in Figure 2.
There were significant differences in the frequency of PIU between the different universities. The lowest prevalence was in Belgrade (18.0%) and the highest in Kragujevac (36.6%). There were also significant differences in the prevalence among students who reported the use of anti-anxiety medications (30.1%) compared to those who did not (18.5%). Students in the PIU group had significantly higher scores on the PHQ-9 scale (8.91 ± 5.97 vs. 4.95 ± 4.33), Zung anxiety scale scores (39.84 ± 9.36 vs. 34.05 ± 8.14), and impulsivity scale (21.10 ± 11.83 vs. 11.66 ± 9.92), and significantly lower scores on the study engagement scale (33.14 ± 23.68 vs. 34.43 ± 7.67) and social support scale (74.88 ± 8.91 vs. 72.35 ± 12.66), along with significantly lower physical activity (2690.00 ± 2512.58 MET-min/week vs. 2940.52 ± 2286.19 MET/min/week). Characteristics of the participants are presented in Table 3.
Multivariate logistic regression analysis with PIU as an outcome variable showed an association between the PIU and the PHQ-9 score (OR: 1.10, 95% CI: 1.03–1.16) and impulsivity (OR: 1.07, 95% CI: 1.05–1.10). Nagelkerke’s R square was 0.291. The results of the multivariate logistic regression analysis with PIU as an outcome variable are presented in Table 4.
4. Discussion
The aim of this study was to examine the validity and reliability of the Serbian version of the Problematic Internet Use Questionnaire Short Form-6 (PIUQ-SF-6) and to examine the prevalence and the social, lifestyle, and mental health characteristics associated with being at risk of problematic internet use among the medical students at all five universities in Serbia that have the Faculty of Medicine.
The reliability analysis showed that the Cronbach’s alpha for the entire PIUQSF6 scale was good and acceptable for all three subscales, similar to previously published studies [58,59]. The assumptions for the examination of Cronbach’s alpha were not fulfilled, as the Tau equivalence based on the factor loadings shown in the exploratory factor analysis may have been violated, and the distribution of items was not normal, which could be associated with the underestimation of reliability by up to 20% [60]. Confirmatory factor analysis showed acceptable goodness-of-fit indices for the literature three-factor model of the questionnaire, in line with the original study and other validation studies [54,61]. CFI, GFI, and AGFI all showed a good fit. However, RMSEA was not ideal. However, the 95% confidence interval for RMSEA showed the range from 0.07 to 0.12, and looking at the lower bound of the interval, the fit may be marginally acceptable.
The prevalence of being at risk of problematic internet use was 21.8% in our study, similar to the prevalence in other studies that examined university students [62,63] and significantly higher than in the general adult population [16]. However, it is lower than shown in the meta-analysis that examined the prevalence among medical students [64]. However, this meta-analysis itself showed significant differences in the prevalence of problematic internet use depending on the instrument used, as the pooled prevalence in the studies that used Chen’s internet addiction scale was 5.2%, and in the studies that used Young’s internet addiction test was 32.2%, once again showing the heterogeneity between the assessment instruments used [64]. Due to several characteristics, including living outside of the family for the first time, university obligations, and lifestyle, university students are at higher risk for problematic internet use [7,32], and this prevalence was expected. Even in the study that examined the prevalence of being at risk for problematic internet use on a representative sample of adults, young, unmarried adults have been found to be at a significantly higher risk [16].
We did not find an association between sex and problematic internet use. Previous studies have shown varying results regarding the association between sex and problematic internet use [8,9]. Online video game playing seems to be more common among males, and problematic social media use seems to be more common in females [8], but we examined only the use of internet services, which may be the main reason for the lack of association with gender in our study.
Our study showed the association between the depressive score and being at risk for problematic internet use, as well as a score on the impulsivity scale. The associations between problematic internet use, impulsivity, and depressive symptoms have been explored but are still debated [65]. More pronounced depressive symptoms indicated with higher depressive scores were associated with a higher likelihood of being at risk of problematic internet use in our study. This is in line with the previous studies that have shown the association between depressive symptoms and problematic internet use [62]. Commonly, this association is explained by the need for socializing online among students with depressive symptoms [62]. However, it is worth noting that there was no lower social support score among students at risk of problematic internet use in our study, although this was shown previously [63].
Unlike for depressive symptoms, we did not find an association between problematic internet use and anxiety symptoms measured with the Zung anxiety scale, nor an association between problematic internet use and the use of anti-anxiety medications in the past year, although an association between anxiety and problematic internet use has already been established [66,67,68]. Our study was conducted on fifth-year students, who may have lower anxiety levels compared to the other study years, as they do not face the stress associated with the beginning years of medical faculty as the students from the first three years, but also do not face the uncertainties in the job market as the final-year students do. This is seen in the relatively low average score on the Zung anxiety scale in both groups.
This study has also shown that each point of a higher score in the state impulsivity scale was associated with an 8% increase in the likelihood of being at risk for problematic internet use. Previously, it was shown that there is an association between impulse control disorder and behavioral addictions [63]. The challenges adolescents and young adults experience today are mainly associated with the fact that not only are the risk behaviors expected, but they are also promoted in these populations [65]. The impulsivity was shown previously to have both a direct association with problematic internet use and an indirect association with problematic internet use through its association with depressive symptoms [69].
Problematic internet use was also shown to be associated with changes in lifestyle, including sleep deprivation and poorer general health, diminished time spent studying, and less time spent in physical activities. However, none of these associations was confirmed in our study, although the initial univariate analyses showed the differences between the groups [60].
This study has a few possible limitations. The first is in its design, as the cross-sectional design does not allow the establishment of a causal relationship between the variables, as the longitudinal design would allow for this. Additionally, we included only the fifth-year medical students in Serbia, and our results cannot be generalized. The reliability of the scale was shown to be low, and yields for further studies in Serbia. However, to the best of our knowledge, there were no large studies examining the prevalence and the social and lifestyle characteristics associated with problematic internet use in Serbia, and we therefore provide valuable insight in this area.
5. Conclusions
The prevalence of PIU in our study was relatively high and more than one-fifth of the students were in the PIU category. Our study showed the association between depressive symptoms and state impulsivity with being at risk for problematic internet use among medical students in Serbia. Future studies should focus on the follow-up of students with PIU and assessment of the possible long-term effects of PIU on mental health and academic performance, clinical skills, decision-making, and patient care. Additionally, the effects of different types of internet use on mental health should be evaluated. The development and implementation of health policies aiming to decrease internet use among students could be made a priority. The mental health of medical students, as future health professionals, should be promoted through tailored interventions that are widely accessible and long-lasting.
J.T. was involved in study design, data collection, data analysis, drafting the article, writing the article, and approving the final version of the article. Z.S. was involved in study supervision, study design, data gathering, data analysis, and writing the final version of the article. M.M. was involved in study design, study supervision, data analysis, writing the final version of the article, and approving the final version of the article. A.S. was involved in data gathering, data interpretation, writing the draft of the article, and approving the final version of the article. I.S. was involved in data gathering, data interpretation, writing the draft of the article, and approving the final version of the article. M.Z. was involved in data gathering, data interpretation, writing the draft of the article, and approving the final version of the article. I.R. was involved in data gathering, writing the draft of the article, and approving the final version of the article. D.M. was involved in data gathering, writing the draft of the article, and approving the final version of the article. T.T. was involved in data gathering, writing the draft of the article, and approving the final version of the article. S.K. was involved in data gathering, writing the draft of the article, and approving the final version of the article. A.A. was involved in data gathering, writing the draft of the article, and approving the final version of the article. R.M. was involved in data gathering, writing the draft of the article, and approving the final version of the article. S.G. was involved in data gathering, writing the draft of the article, and approving the final version of the article. D.N. was involved in data gathering, writing the draft of the article, and approving the final version of the article Z.T.-S. was involved in study supervision, data analysis, drafting the article, writing the article, and approving the final version of the article. All authors have read and agreed to the published version of the manuscript.
This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethical Committee of the Faculty of Medicine, University of Belgrade (protocol code 17/X-15, 24/10/2023).
Informed consent was obtained from all subjects involved in the study.
Data can be made available upon request.
The authors declare no conflicts of interest.
Footnotes
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.
Figure 1 Scatter dot plot.
Figure 2 Confirmatory factor analyses for one-factor and three-factor models.
Item descriptive for PIUQ6 scale.
| Item | Median | Mean ± SD | Skewness |
|---|---|---|---|
| How often do you feel tense, irritated, or stressed if you cannot use the Internet for as long as you want to? | 1 | 1.71 ± 1.00 | 0.87 |
| How often does it happen to you that you feel depressed, moody, or nervous when you are not on the Internet, and these feelings stop once you are back online? | 1 | 1.50 ± 0.95 | 1.24 |
| How often do you spend time online when you’d rather sleep? | 3 | 2.58 ± 1.19 | 0.10 |
| How often do people in your life complain about you spending too much time online? | 1 | 1.53 ± 0.97 | 1.18 |
| How often does it happen to you that you wish to decrease the amount of time spent online, but you do not succeed? | 2 | 2.39 ± 1.23 | 0.30 |
| How often do you try to conceal the amount of time spent online? | 1 | 1.42 ± 0.93 | 1.56 |
Factor loadings for each item using the principal component.
| Factor | Factor Loadings |
|---|---|
| How often do you feel tense, irritated, or stressed if you cannot use the Internet for as long as you want to? | 0.769 |
| How often does it happen to you that you feel depressed, moody, or nervous when you are not on the Internet, and these feelings stop once you are back online? | 0.786 |
| How often do you spend time online when you’d rather sleep? | 0.632 |
| How often do people in your life complain about you spending too much time online? | 0.746 |
| How often does it happen to you that you wish to decrease the amount of time spent online but you do not succeed? | 0.694 |
| How often do you try to conceal the amount of time spent online? | 0.728 |
Characteristics of participants.
| Characteristics | PIU | No PIU | p-Value |
|---|---|---|---|
| Type of residence | |||
| Urban | 135 (21.5) | 493 (78.5) | |
| Rural | 21 (24.1) | 66 (75.9) | 0.576 |
| Sex | |||
| Male | 47 (19.3) | 196 (80.7) | |
| Female | 111 (22.7) | 377 (77.3) | 0.292 |
| Age in years X ± SD | 23.97 ± 1.48 | 23.89 ± 1.47 | 0.484 |
| BMI in kg/m2 X ± SD | 22.99 ± 3.74 | 22.65 ± 3.57 | 0.435 |
| Relationship status | |||
| In a relationship | 69 (18.7) | 300 (81.3) | |
| Single | 91 (24.9) | 275 (75.1) | 0.043 |
| Self-perceived financial status | |||
| Poor | 9 (31.0) | 20 (69.0) | |
| Average | 64 (21.0) | 241 (79.0) | |
| Good | 86 (21.7) | 311 (78.3) | 0.455 |
| Family relationships | |||
| Poor | 11 (34.4) | 21 (65.6) | |
| Average | 26 (23.0) | 87 (77.0) | |
| Good | 122 (20.8) | 465 (79.2) | 0.180 |
| Self-rated health | |||
| Poor | 7 (35.0) | 13 (65.0) | |
| Average | 39 (39.4) | 60 (60.6) | |
| Good | 109 (18.4) | 485 (81.6) | 0.001 |
| Alcohol use in the past year | |||
| No | 29 (24.0) | 92 (76.0) | |
| Yes, in the past year, not in the past month | 35 (22.7) | 119 (77.3) | |
| Yes, in the past month | 96 (21.0) | 362 (79.0) | 0.741 |
| Cannabis use | |||
| Yes | 15 (17.2) | 72 (82.8) | |
| No | 141 (22.4) | 489 (77.6) | 0.276 |
| Use of anti-anxiety medications in the past 12 months | |||
| Yes | 62 (30.1) | 144 (69.9) | |
| No | 98 (18.5) | 431 (81.5) | 0.001 |
| Use of any tobacco products | |||
| Yes | 42 (22.5) | 145 (77.5) | |
| No | 105 (22.2) | 369 (77.8) | 0.932 |
| PHQ-9 score X ± SD | 8.91 ± 5.97 | 4.95 ± 4.33 | 0.001 |
| Zung score X ± SD | 39.84 ± 9.36 | 34.05 ± 8.14 | 0.001 |
| Impulsivity X ± SD | 21.10 ± 11.83 | 11.66 ± 9.92 | 0.001 |
| Study engagement X ± SD | 33.14 ± 23.68 | 34.43 ± 7.67 | 0.001 |
| Social support X ± SD | 74.88 ± 8.91 | 72.35 ± 12.66 | 0.047 |
| Physical activity X ± SD | 2690.00 ± 2512.58 | 2940.52 ± 2286.19 | 0.021 |
Multivariate logistic regression analysis with PIU as an outcome variable.
| Characteristics | OR (95% CI) | p -Value |
| Relationship status | ||
| Single | 1.0 reference category | |
| In a relationship | 1.17 (0.72–1.90) | 0.531 |
| Self-rated health | ||
| Poor | 1.0 reference category | |
| Average | 1.25 (0.31–5.07) | 0.752 |
| Good | 0.95 (0.25–3.52) | 0.934 |
| Use of anti-anxiety medications in the past year (yes vs. no) | 1.16 (0.67–1.99) | 0.599 |
| PHQ-9 score | 1.10 (1.03–1.16) | 0.002 |
| Zung score | 1.00 (0.97–1.05) | 0.600 |
| Impulsivity | 1.07 (1.05–1.10) | 0.001 |
| Study engagement | 1.00 (0.98–1.02) | 0.915 |
| Social Support | 1.01 (0.98–1.03) | 0.507 |
| Physical activity in MET-minutes/week | 1.00 (1.00–1.00) | 0.763 |
1. Razum, J.; Glavak Tkalić, R.; Brkljačić, T.; Sučić, I.; Wertag, A. Current Issues in Conceptualisation, Measurement, and Research of Problematic Use of the Internet. Drus. Istraz.; 2021; 30, pp. 741-762. [DOI: https://dx.doi.org/10.5559/di.30.4.05]
2. Demetrovics, Z.; Szeredi, B.; Rozsa, S. The Three-Factor Model of Internet Addiction: The Development of the Problematic. Behav. Res. Methods; 2008; 40, pp. 563-574. [DOI: https://dx.doi.org/10.3758/BRM.40.2.563]
3. Chatterjee, D.; Rai, R. Behind the Screens: Proposing a Mentalization-Based Theoretical Model of Problematic Internet Use. Cyberpsychology; 2023; 17, 6. [DOI: https://dx.doi.org/10.5817/CP2023-5-6]
4. Kiraly, O.; Demetrovics, Z. Problematic Internet Use. Textbook of Addiction Treatment: International Perspectives; Springer: Cham, Switzerland, 2021; pp. 955-965.
5. Lopez-Fernandez, O.; Romo, L.; Kern, L.; Rousseau, A.; Lelonek-Kuleta, B.; Chwaszcz, J.; Männikkö, N.; Rumpf, H.J.; Bischof, A.; Király, O.
6. Marin, M.G.; Nuñez, X.; De Almeida, R.M.M. Internet Addiction and Attention in Adolescents: A Systematic Review. Cyberpsychol. Behav. Soc. Netw.; 2021; 24, pp. 237-249. [DOI: https://dx.doi.org/10.1089/cyber.2019.0698]
7. Balhara, Y.P.S.; Gupta, R.; Atilola, O.; Knez, R.; Mohorović, T.; Gajdhar, W.; Javed, A.O.; Lal, R. Problematic Internet Use and Its Correlates among Students from Three Medical Schools across Three Countries. Acad. Psychiatry; 2015; 39, pp. 634-638. [DOI: https://dx.doi.org/10.1007/s40596-015-0379-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26130501]
8. Baloğlu, M.; Şahin, R.; Arpaci, I. A Review of Recent Research in Problematic Internet Use: Gender and Cultural Differences. Curr. Opin. Psychol.; 2020; 36, pp. 124-129. [DOI: https://dx.doi.org/10.1016/j.copsyc.2020.05.008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32629412]
9. Niskier, S.R.; Snaychuk, L.A.; Kim, H.S.; da Silva, T.T.; de Souza Vitalle, M.S.; Tavares, H. Adolescent Screen Use: Problematic Internet Use and the Impact of Gender. Psychiatry Investig.; 2024; 21, pp. 18-27. [DOI: https://dx.doi.org/10.30773/pi.2023.0178]
10. Ioannidis, K.; Treder, M.S.; Chamberlain, S.R.; Kiraly, F.; Redden, S.A.; Stein, D.J.; Lochner, C.; Grant, J.E. Problematic Internet Use as an Age-Related Multifaceted Problem: Evidence from a Two-Site Survey. Addict. Behav.; 2018; 81, pp. 157-166. [DOI: https://dx.doi.org/10.1016/j.addbeh.2018.02.017]
11. Cárdenas Garza, S.A.; Janssen Aguilar, R.; Ruíz Chow, Á.A. Problematic Internet Use and Personality Traits: Results in Working Age Adults. Rev. Colomb. Psiquiatr.; 2024; 53, pp. 142-148. [DOI: https://dx.doi.org/10.1016/j.rcp.2022.03.002]
12. Kożybka, M.; Radlińska, I.; Kolwitz, M.; Karakiewicz, B. Problematic Internet Use among Polish Students: Prevalence, Relationship to Sociodemographic Data and Internet Usage Patterns. Int. J. Environ. Res. Public Health; 2023; 20, 2434. [DOI: https://dx.doi.org/10.3390/ijerph20032434]
13. Derbyshire, K.L.; Lust, K.A.; Schreiber, L.R.N.; Odlaug, B.L.; Christenson, G.A.; Golden, D.J.; Grant, J.E. Problematic Internet Use and Associated Risks in a College Sample. Compr. Psychiatry; 2013; 54, pp. 415-422. [DOI: https://dx.doi.org/10.1016/j.comppsych.2012.11.003]
14. Yıldız, Ü.; Kuruoğlu, E.; Günvar, T.; Çöme, O.; Mevsim, V. The Relationship Between Obesity and Internet Addiction in University Students: A Cross-Sectional Study. Am. J. Heal. Promot.; 2024; 38, pp. 513-521. [DOI: https://dx.doi.org/10.1177/08901171241227036]
15. Tian, Y.; Zhao, Y.; Lv, F.; Qin, N.; Chen, P. Associations Among the Big Five Personality Traits, Maladaptive Cognitions, and Internet Addiction Across Three Time Measurements in 3 Months During the COVID-19 Pandemic. Front. Psychol.; 2021; 12, 654825. [DOI: https://dx.doi.org/10.3389/fpsyg.2021.654825]
16. Macur, M.; Király, O.; Maraz, A.; Nagygyörgy, K.; Demetrovics, Z. Prevalence of Problematic Internet Use in Slovenia. Zdr. Varst.; 2016; 55, pp. 202-211. [DOI: https://dx.doi.org/10.1515/sjph-2016-0026]
17. Morioka, H.; Itani, O.; Osaki, Y.; Higuchi, S.; Jike, M.; Kaneita, Y.; Kanda, H.; Nakagome, S.; Ohida, T. The Association between Alcohol Use and Problematic Internet Use: A Large-Scale Nationwide Cross-Sectional Study of Adolescents in Japan. J. Epidemiol.; 2017; 27, pp. 107-111. [DOI: https://dx.doi.org/10.1016/j.je.2016.10.004]
18. Grant, J.; Potenza, M.; Weinstein, A.; Gorelick, D. Introduction to Behavioral Addictions. Am. J. Drug Alcohol Abus.; 2010; 36, pp. 233-241. [DOI: https://dx.doi.org/10.3109/00952990.2010.491884]
19. Lanthier-Labonté, S.; Dufour, M.; Milot, D.M.; Loslier, J. Is Problematic Internet Use Associated with Alcohol and Cannabis Use among Youth? A Systematic Review. Addict. Behav.; 2020; 106, 106331. [DOI: https://dx.doi.org/10.1016/j.addbeh.2020.106331]
20. Reed, P.; Davies, A.; Evans, K.; Osborne, L.A. Longitudinal Relationship between Problematic Internet Use with Loneliness during and after COVID-19 Social Restrictions: Short Title: Internet Use and Loneliness. Psychiatry Res.; 2023; 323, 115148. [DOI: https://dx.doi.org/10.1016/j.psychres.2023.115148]
21. Moretta, T.; Buodo, G. Problematic Internet Use and Loneliness: How Complex Is the Relationship? A Short Literature Review. Curr. Addict. Rep.; 2020; 7, pp. 125-136. [DOI: https://dx.doi.org/10.1007/s40429-020-00305-z]
22. Marinoni, C.; Rizzo, M.; Zanetti, M.A. Fake Profiles and Time Spent Online during the COVID 19 Pandemic: A Real Risk for Cyberbullying?. Curr. Psychol.; 2024; 43, pp. 26639-26647. [DOI: https://dx.doi.org/10.1007/s12144-024-05979-6]
23. Bipeta, R.; Yerramilli, S.S.; Karredla, A.R.; Gopinath, S. Diagnostic Stability of Internet Addiction in Obsessive-Compulsive Disorder: Data from a Naturalistic One-Year Treatment Study. Innov. Clin. Neurosci.; 2015; 12, pp. 14-23.
24. Weinstein, A.; Dorani, D.; Elhadif, R.; Bukovza, Y.; Yarmulnik, A.; Dannon, P. Internet Addiction Is Associated with Social Anxiety in Young Adults. Ann. Clin. Psychiatry; 2015; 27, pp. 4-9.
25. Diotaiuti, P.; Mancone, S.; Corrado, S.; De Risio, A.; Cavicchiolo, E.; Girelli, L.; Chirico, A. Internet Addiction in Young Adults: The Role of Impulsivity and Codependency. Front. Psychiatry; 2022; 13, 893861. [DOI: https://dx.doi.org/10.3389/fpsyt.2022.893861]
26. Wang, Y.; Wu, W.; Cao, X.; Tang, X. Multi-Informant Profiles of Helicopter Parenting and Parent-Child Intimacy: Longitudinal Links with Adolescents’ Self-Discipline and Problematic Internet Use. J. Youth Adolesc.; 2025; [DOI: https://dx.doi.org/10.1007/s10964-025-02179-8]
27. Carone, N.; Benzi, I.M.A.; Muzi, L.; Parolin, L.A.L.; Fontana, A. Problematic Internet Use in Emerging Adulthood to Escape from Maternal Helicopter Parenting: Defensive Functioning as a Mediating Mechanism. Res. Psychother. Psychopathol. Process Outcome; 2023; 26, 693. [DOI: https://dx.doi.org/10.4081/ripppo.2023.693]
28. Werling, A.M.; Grünblatt, E. A Review of the Genetic Basis of Problematic Internet Use. Curr. Opin. Behav. Sci.; 2022; 46, 101149. [DOI: https://dx.doi.org/10.1016/j.cobeha.2022.101149]
29. Dong, Y.; Li, H. The Relationship between Social Support and Depressive Symptoms among the College Students of Liaoning, China: A Moderated Mediated Analysis. Psychol. Heal. Med.; 2020; 25, pp. 368-378. [DOI: https://dx.doi.org/10.1080/13548506.2019.1679844]
30. Hwang, T.J.; Rabheru, K.; Peisah, C.; Reichman, W.; Ikeda, M. Loneliness and Social Isolation during the COVID-19 Pandemic. Int. Psychogeriatr.; 2020; 32, pp. 1217-1220. [DOI: https://dx.doi.org/10.1017/S1041610220000988]
31. Sánchez-Fernández, M.; Borda-Mas, M.; Mora-Merchán, J. Problematic Internet Use by University Students and Associated Predictive Factors: A Systematic Review. Comput. Hum. Behav.; 2023; 139, 107532. [DOI: https://dx.doi.org/10.1016/j.chb.2022.107532]
32. Fernández-Villa, T.; Ojeda, J.A.; Gómez, A.A.; Carral, J.M.C.; Delgado-Rodríguez, M.; García-Martín, M.; Jiménez-Mejías, E.; Llorca, J.; Molina, A.J.; Moncada, R.O.
33. Gioia, F.; Rega, V.; Boursier, V. Problematic Internet Use and Emotional Dysregulation among Young People: A Literature Review. Clin. Neurpsychiatry; 2021; 18, pp. 41-54.
34. Christakis, D.A.; Moreno, M.M.; Jelenchick, L.; Myaing, M.T.; Zhou, C. Problematic Internet Usage in US College Students: A Pilot Study. BMC Med.; 2011; 9, 77. [DOI: https://dx.doi.org/10.1186/1741-7015-9-77] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21696582]
35. Moreno, M.; Riddle, K.; Jenkins, M.C.; Singh, A.P.; Zhao, Q.; Eickhoff, J. Measuring Problematic Internet Use, Internet Gaming Disorder, and Social Media Addiction in Young Adults: Cross-Sectional Survey Study. JMIR Public Health Surveill.; 2022; 8, e27719. [DOI: https://dx.doi.org/10.2196/27719] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34081596]
36. Dhamnetiya, D.; Singh, S.; Jha, R.P. Correlates of Problematic Internet Use among Undergraduate Medical Students of Delhi. BMC Psychiatry; 2021; 21, 511. [DOI: https://dx.doi.org/10.1186/s12888-021-03529-z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34654407]
37. Shadzi, M.; Salehi, A.; Vardanjani, H. Problematic Internet Use, Mental Health, and Sleep Quality among Medical Students: A Path-Analytic Model. Indian J. Psychol. Med.; 2020; 42, pp. 128-135. [DOI: https://dx.doi.org/10.4103/IJPSYM.IJPSYM_238_19]
38. Sánchez-Fernández, M.; Borda-Mas, M. Problematic Smartphone Use and Specific Problematic Internet Uses among University Students and Associated Predictive Factors: A Systematic Review. Educ. Inf. Technol.; 2023; 28, pp. 7111-7204. [DOI: https://dx.doi.org/10.1007/s10639-022-11437-2]
39. Laconi, S.; Rodgers, R.F.; Chabrol, H. The Measurement of Internet Addiction: A Critical Review of Existing Scales and Their Psychometric Properties. Comput. Hum. Behav.; 2014; 41, pp. 190-202. [DOI: https://dx.doi.org/10.1016/j.chb.2014.09.026]
40. Young, K.S. Internet Addiction: The Emergence of a New Clinical Disorder. Cyberpsychol. Behav.; 1998; 1, pp. 237-244. [DOI: https://dx.doi.org/10.1089/cpb.1998.1.237]
41. Meerkerk, G.-J.; Van Den Eijnden, R.J.J.M.; Vermulst, A.A.; Garretsen, H.F.L. The Compulsive Internet Use Scale (CIUS): Some Psychometric Properties. Cyberpsychol. Behav.; 2008; 12, pp. 1-6. [DOI: https://dx.doi.org/10.1089/cpb.2008.0181]
42. Pettorruso, M.; Valle, S.; Cavic, E.; Martinotti, G.; di Giannantonio, M.; Grant, J.E. Problematic Internet Use (PIU), Personality Profiles and Emotion Dysregulation in a Cohort of Young Adults: Trajectories from Risky Behaviors to Addiction. Psychiatry Res.; 2020; 289, 113036. [DOI: https://dx.doi.org/10.1016/j.psychres.2020.113036] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32450451]
43. Terzic-Supic, Z.; Jelic, M.; Santric-Milicevic, M.; Kilibarda, B.; Mirkovic, M.; Bankovic-Lazarevic, D.; Todorovic, J. National Survey on Lifestyles and Gambling in Serbia: Gambling Participation and Problem Gambling in Adult Population. Int. Gambl. Stud.; 2019; 19, pp. 265-281. [DOI: https://dx.doi.org/10.1080/14459795.2018.1552713]
44. Results of the National Health Survey of the Republic of Serbia 2013; 2014; ISBN 9788673580623. Available online: https://www.batut.org.rs/download/publikacije/2013SerbiaHealthSurvey.pdf (accessed on 15 April 2025).
45. Salmela-Aro, K.; Read, S. Study Engagement and Burnout Profiles among Finnish Higher Education Students. Burn. Res.; 2017; 7, pp. 21-28. [DOI: https://dx.doi.org/10.1016/j.burn.2017.11.001]
46. Meh, K.; Jurak, G.; Sorić, M.; Rocha, P.; Sember, V. Validity and Reliability of IPAQ-SF and GPAQ for Assessing Sedentary Behaviour in Adults in the European Union: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health; 2021; 18, 4602. [DOI: https://dx.doi.org/10.3390/ijerph18094602]
47. Hagströmer, M.; Oja, P.; Sjöström, M. The International Physical Activity Questionnaire (IPAQ): A Study of Concurrent and Construct Validity. Public Health Nutr.; 2006; 9, pp. 755-762. [DOI: https://dx.doi.org/10.1079/PHN2005898]
48. Zimet, G.; Powell, S.; Farley, G. Multidimensional Scale of Perceived Social Support (MSPSS); Johns Hopkins University Press: Baltimore, MD, USA, 2012; 52, pp. 10-12.
49. Zimet, G.D.; Dahlem, N.W.; Zimet, S.G.; Farley, G.K. The Multidimensional Scale of Perceived Social Support. J. Pers. Assess.; 1988; 52, pp. 30-41. [DOI: https://dx.doi.org/10.1207/s15327752jpa5201_2]
50. Zung, W.W. A Rating Instrument for Anxiety Disorders. Psychosom. J. Consult. Liaison Psychiatry; 1971; 12, pp. 371-379.
51. Kroenke, K.; Spitzer, R.L.; Williams, J.B. The PHQ-9: Validity of a Brief Depression Severity Measure. J. Gen. Intern. Med.; 2001; 16, pp. 606-613. [DOI: https://dx.doi.org/10.1046/j.1525-1497.2001.016009606.x]
52. Iribarren, M.M. Validation and Psychometric Properties of the State Impulsivity Scale (SIS). Actas Esp. Psiquiatr.; 2011; 39, pp. 49-60.
53. Kelley, K.J.; Gruber, E.M. Computers in Human Behavior Psychometric Properties of the Problematic Internet Use Questionnaire Q. Comput. Hum. Behav.; 2010; 26, pp. 1838-1845. [DOI: https://dx.doi.org/10.1016/j.chb.2010.07.018]
54. Demetrovics, Z.; Király, O.; Koronczai, B.; Griffiths, M.D.; Nagygyörgy, K.; Elekes, Z.; Tamás, D.; Kun, B.; Kökönyei, G.; Urbán, R. Psychometric Properties of the Problematic Internet Use Questionnaire Short-Form (PIUQ-SF-6) in a Nationally Representative Sample of Adolescents. PLoS ONE; 2016; 11, e0159409. [DOI: https://dx.doi.org/10.1371/journal.pone.0159409] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27504915]
55. Sivo, S.A.; Xitao, F.A.N.; Witta, E.L.; Willse, J.T. The Search for “Optimal” Cutoff Properties: Fit Index Criteria in Structural Equation Modeling. J. Exp. Educ.; 2006; 74, pp. 267-288. [DOI: https://dx.doi.org/10.3200/JEXE.74.3.267-288]
56. Shi, D.; Lee, T.; Maydeu-Olivares, A. Understanding the Model Size Effect on SEM Fit Indices. Educ. Psychol. Meas.; 2019; 79, pp. 310-334. [DOI: https://dx.doi.org/10.1177/0013164418783530] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30911195]
57. Grunst, M.L.; Grunst, A.S.; Pinxten, R.; Eens, G.; Eens, M. An Experimental Approach to Investigating Effects of Artificial Light at Night on Free-Ranging Animals: Implementation, Results, and Directions for Future Research. J. Vis. Exp.; 2022; 2022, pp. 53-60. [DOI: https://dx.doi.org/10.3791/63381]
58. Aivali, P.; Efthymiou, V.; Tsitsika, A.K.; Vlachakis, D.; Chrousos, G.P.; Kanaka-Gantenbein, C.; Bacopoulou, F. Validation of the Greek Version of the Problematic Internet Use Questionnaire—Short Form (PIUQ-SF-6). EMBnet. J.; 2021; 26, e978. [DOI: https://dx.doi.org/10.14806/ej.26.1.978] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34621630]
59. Lin, M.; Kim, Y. The Reliability and Validity of the 18-Item Long Form and Two Short Forms of the Problematic Internet Use Questionnaire in Three Japanese Samples. Addict. Behav.; 2020; 101, 105961. [DOI: https://dx.doi.org/10.1016/j.addbeh.2019.04.019]
60. Kojima, R.; Sato, M.; Akiyama, Y.; Shinohara, R.; Mizorogi, S.; Suzuki, K.; Yokomichi, H.; Yamagata, Z. Problematic Internet Use and Its Associations with Health-Related Symptoms and Lifestyle Habits among Rural Japanese Adolescents. Psychiatry Clin. Neurosci.; 2019; 73, pp. 20-26. [DOI: https://dx.doi.org/10.1111/pcn.12791]
61. Opakunle, T.; Aloba, O.; Opakunle, O.; Eegunranti, B. Problematic Internet Use Questionnaire-Short Form-6 (PIUQ-SF-6): Dimensionality, Validity, Reliability, Measurement Invariance and Mean Differences across Genders and Age Categories among Nigerian Adolescents. Int. J. Ment. Health; 2020; 49, pp. 229-246. [DOI: https://dx.doi.org/10.1080/00207411.2020.1776457]
62. Mengistu, N.; Tarekegn, D.; Bayisa, Y.; Yimer, S.; Madoro, D.; Assefa, D.G.; Zeleke, E.D.; Molla, W.; Wudneh, A.; Shumye, S.
63. Ramón-Arbués, E.; Granada-López, J.M.; Martínez-Abadía, B.; Echániz-Serrano, E.; Antón-Solanas, I.; Nash, M. Prevalence and Factors Associated with Problematic Internet Use in a Population of Spanish University Students. Int. J. Environ. Res. Public Health; 2021; 18, 7620. [DOI: https://dx.doi.org/10.3390/ijerph18147620]
64. Zhang, M.W.B.; Lim, R.B.C.; Lee, C.; Ho, R.C.M. Prevalence of Internet Addiction in Medical Students: A Meta-Analysis. Acad. Psychiatry; 2018; 42, pp. 88-93. [DOI: https://dx.doi.org/10.1007/s40596-017-0794-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28849574]
65. Hammond, C.J.; Mayes, L.C.; Potenza, M.N. Neurobiology of Adolescent Substance Use and Addictive Behaviors: Treatment Implications. Adolesc. Med. State Art Rev.; 2014; 25, pp. 15-32.
66. Cai, Z.; Mao, P.; Wang, Z.; Wang, D.; He, J.; Fan, X. Associations Between Problematic Internet Use and Mental Health Outcomes of Students: A Meta-Analytic Review. Adolesc. Res. Rev.; 2023; 8, pp. 45-62. [DOI: https://dx.doi.org/10.1007/s40894-022-00201-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36744121]
67. Lavoie, C.; Dufour, M.; Berbiche, D.; Therriault, D.; Lane, J. The Relationship between Problematic Internet Use and Anxiety Disorder Symptoms in Youth: Specificity of the Type of Application and Gender. Comput. Hum. Behav.; 2023; 140, 107604. [DOI: https://dx.doi.org/10.1016/j.chb.2022.107604]
68. Johnson, O.A.; Olaniyi, S.F.; John, S.; Sheila, O.; Daniel, O.; Imam, A.; Olaiya, A.P. Baseline and Postintervention Assessment of Sexual Violence and Condom Use among Female Sex Workers in a Semiurban African Community. Soc. Heal. Behav.; 2020; 3, pp. 137-141. [DOI: https://dx.doi.org/10.4103/SHB.SHB_29_20]
69. Gecaite-Stonciene, J.; Saudargiene, A.; Pranckeviciene, A.; Liaugaudaite, V.; Griskova-Bulanova, I.; Simkute, D.; Naginiene, R.; Dainauskas, L.L.; Ceidaite, G.; Burkauskas, J. Impulsivity Mediates Associations Between Problematic Internet Use, Anxiety, and Depressive Symptoms in Students: A Cross-Sectional COVID-19 Study. Front. Psychiatry; 2021; 12, 634464. [DOI: https://dx.doi.org/10.3389/fpsyt.2021.634464]
© 2025 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Mirkovic Momcilo 2 ; Stamenkovic Zeljka 1 ; Stevanovic Aleksandar 1
; Sotirovic Ivana 1
; Zdujic Marija 1 ; Radic Ivana 3 ; Milijasevic Dragana 3 ; Tamas Tatjana 4
; Kocic Sanja 5 ; Arnaut Aleksandra 6
; Markovic, Roberta 7 ; Grbic Sonja 8 ; Nesic Dejan 9
; Terzic-Supic Zorica 1
1 Faculty of Medicine, Institute of Social Medicine, University of Belgrade, 11000 Belgrade, Serbia; [email protected] (Z.S.); [email protected] (A.S.); [email protected] (I.S.); [email protected] (M.Z.); [email protected] (Z.T.-S.)
2 Department for Preventive Medicine, Faculty of Medicine, University of Pristina- Kosovska Mitrovica, 40000 Kosovska Mitrovica, Serbia; [email protected]
3 Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia; [email protected] (I.R.); [email protected] (D.M.); [email protected] (T.T.), Institute of Public Health of Vojvodina, 21000 Novi Sad, Serbia
4 Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia; [email protected] (I.R.); [email protected] (D.M.); [email protected] (T.T.), Oncology Institute of Vojvodina, 21208 Sremska Kamenica, Serbia
5 Department of Social Medicine, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia; [email protected]
6 Department of Dentistry, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia; [email protected]
7 Department of Social Medicine and Hygiene with Medical Ecology, Faculty of Medicine, University of Nis, 18000 Nis, Serbia; [email protected]
8 Clinical-Hospital centre of Kosovska Mitrovica, Psychiatry Hospital, 40000 Kosovska Mitrovica, Serbia; [email protected]
9 Institute of Medical Physiology, Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia; [email protected]