Beata Gavurova
Viera Ivankova
Martin Rigelsky
Tawfik Mudarri2- 1Department of Addictology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czechia
- 2Institute of Earth Resources, Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Košice, Košice, Slovakia
- 3Department of Marketing and International Trade, Faculty of Management and Business, University of Prešov, Prešov, Slovakia
Internet addiction is a serious problem among young adults that requires increased attention, especially at a time of distance learning during the coronavirus disease 2019 (COVID-19) pandemic. The aim of the study was to assess the relationships between internet addiction and selected socio-demographic, study-related, and psychological characteristics of college students. Internet addiction was measured using the Internet Addiction Test both overall and in its individual subscales (Salience, Excessive Use, Neglect Work, Anticipation, Lack of Control, and Neglect Social Life). The selected characteristics represented (1) socio-demographic profile (gender, age, residence, family), (2) academic profile (housing during the semester, form of study), and (3) psychological profile (depressive symptoms—the Patient Health Questionnaire, stress—the Perceived Stress Scale, anxiety symptoms—the Generalized Anxiety Disorder). Data collection took place during the first wave of the COVID-19 pandemic in 2020 at Czech and Slovak colleges, with 1,422 students from the Czech Republic and 1,677 students from Slovakia participating in the research. The analytical processes were carried out through descriptive analysis, non-parametric difference analysis, and multiple negative binomial regression. Mild internet addiction was found in 387 (27.2%) Czech and 452 (27.0%) Slovak students. Moderate internet addiction was identified in 49 (3.4%) students from the Czech Republic and in 100 (6.0%) students from Slovakia. Two (0.1%) Czech and three (0.2%) Slovak students reported severe internet addiction. Increased likelihood of internet addiction overall, as well as in most individual subscales, was found particularly among male students and students who lived away from home during the semester. Depressive symptoms and stress could also be considered significant predictors in both countries. These results are important for the development of effective strategies and prevention programs, as Internet addiction may be a serious problem in the future, given the current times. When assessing internet addiction among college students, it would also be appropriate to evaluate the individual internet addiction subscales and their specifics.
Introduction
A strong attribute of today's modern times is the internet, which has become an everyday part not only of young people. The increasing accessibility of the Internet over the last two decades has significantly changed young people's lives in many dimensions. The internet is ubiquitous, whether it is for entertainment, relaxation, escaping worries, shopping, but also for seeking study information and connecting with friends and family (1, 2). However, the dark side is the fact that the internet can suck young individuals into an online world where excessive time spent online can contribute to compulsive unhealthy behavior (3). In this sense, it is possible to speak of a non-substance drug that brings with it many consequences in the social and academic spheres, but also in mental health (4, 5). In other words, the internet can help with many needs, but it can also be a harmful element if a reasonable level of use is exceeded.
Youth itself represents a complicated phase in an individual's life in which he or she may succumb to various pitfalls. Studying at college is a critical period during which young people face many challenges, which is why college students are often identified as a vulnerable group (6). Starting college is already a great change, in which the student encounters many new situations and problems. Relationships, acceptance and integration, academic performance, meeting expectations, but also future employability are important aspects at this time. The consequences of problematic internet use by college students can be poor academic performance, disinterest in engaging in other activities, excessive daytime sleepiness (7), poor sleep quality (8), reduced academic engagement (dedication and vigor) (9), risky health behaviors (10), but also health problems, such as being under- or over-weight (11, 12), migraine (12, 13), back pain (12), or increased resting heart rate (14). These are reasons for increased vigilance about young people's use of the internet. Caution is also stressed in view of the fact that college students are seen as an important future pillar of society and economic prosperity.
In addition to the facts mentioned in the previous paragraph, the coronavirus disease 2019 (COVID-19) pandemic represents another psychological burden that has also affected the lives of students (15). The measures related to COVID-19 disrupted students' education, social contact, and leisure time. This period can undoubtedly be considered psychologically challenging, as evidence shows that common mental disorders occurred frequently among students during the pandemic (16), while distance learning through digital technologies was a critical aspect for mental health (17). The regulations led to greater use of modern technology, including the internet (18, 19), which may have put young people's mental health at risk. For this reason, many studies have been conducted around the world to clarify the consequences of the COVID-19 pandemic. Compared to the pre-pandemic period, many studies showed higher rates of online activity (20–23), but also higher rates of internet addiction (24). However, it is also important to know what factors play a significant role in this problem.
Problematic internet use can be understood as using the internet in a way that has a negative influence on an individual's life and can lead to addiction (25). When a problem is identified, increased attention is focused not only on the time spent online, but especially on consequences such as loss of control, unsuccessful quit attempts, or disrupted relationships with family and friends (26). In addition, evidence clearly shows that problematic internet use is a significant predictor of depression, anxiety, and stress in young people (27, 28). On the other hand, there are also findings indicating that the presence of common mental disorders may explain problematic internet use (16). According to Masaeli and Farhadi (29), internet-based addictive behaviors during the COVID-19 pandemic were mostly due to financial hardships, isolation, problematic substance use, and psychological distress, including depression, anxiety, and stress. In this context, depression, anxiety, and stress are known to be associated with more severe problematic use and/or internet addiction in students (6, 18, 30–35). Based on these findings, it can be assumed that depressive symptoms, anxiety symptoms and perceived stress may also prove to be significant predictors in other countries, such as the Czech Republic and Slovakia. In addition, studies show that individual aspects such as gender, age, residence and academic characteristics also play an important role in problematic internet use (18, 32, 36–43).
In terms of internet addiction among college students, the pre-pandemic period is not sufficiently mapped in the Czech Republic and Slovakia, which is indicative of the fact that little attention has been paid to this issue in these two neighboring countries. For this reason, research-based knowledge and its translation into practice are lacking. In Slovakia, Sebena et al. (44) highlighted the seriousness of problematic internet use among college students and confirmed that depressive symptoms predict their problematic internet use. In the Czech Republic, the issue was addressed by Chraska (45). In both countries, however, the research focus was mainly on primary and secondary school students (46–48). Thus, to the best of the authors' knowledge, there is a lack of research on the issue among college students in both the Czech Republic and Slovakia, which can be identified as a relevant research gap. On this basis, this study examines the relationships between internet addiction and selected socio-demographic, academic, and psychological characteristics of Czech and Slovak college students during the COVID-19 pandemic. Knowledge of these relationships provides the opportunity to put in place effective protective and preventive measures to address the problem and avoid serious consequences in the future.
Materials and Methods
The aim of the study was to assess the relationships between internet addiction and selected socio-demographic, academic, and psychological characteristics of Czech and Slovak college students. The research examined internet addiction from an overall perspective but also in sub-scales, namely Salience, Excessive Use, Neglect Work, Anticipation, Lack of Control, and Neglect Social Life. As indicated, the selected characteristics were concentrated in three areas, namely (1) socio-demographic profile (gender, age, residence, and family), (2) academic profile (housing during the semester, form of study), and (3) psychological profile (depressive symptoms, stress, and anxiety symptoms). The research was conducted based on the following research questions (RQs).
RQ1: How are selected socio-demographic characteristics associated with internet addiction in Czech and Slovak students?
RQ2: How are selected academic characteristics associated with internet addiction in Czech and Slovak students?
RQ3: How are selected psychological characteristics associated with internet addiction in Czech and Slovak students?
Measures
In addition to the usual socio-demographic and academic identification data, the online questionnaire also collected data on students' level of psychological distress.
Psychological distress was measured using already existing scales from previous studies. Thus, depressive symptoms were measured using the Patient Health Questionnaire (PHQ-9) and anxiety symptoms using the Generalized Anxiety Disorder (GAD-7) screening scale from the study by Kroenke et al. (49). The PHQ-9 has been widely validated to screen for depressive symptoms (50) and is considered a suitable instrument also for a sample of college students (51, 52). Similarly, the GAD-7 is a commonly used instrument for screening anxiety symptoms among college students and show good psychometric properties (53–55). For these scales, respondents could choose one of the following answers for each questionnaire item: not at all-−0, several days-−1, more than half the days-−2, nearly every day-−3. The total scores were the sum of the coded responses. Based on this, depressive symptoms were identified as follows: no depressive symptoms (0–4), mild depressive symptoms (5–9), moderate depressive symptoms (10–14), moderately severe depressive symptoms (15–19), and severe depressive symptoms (20 or more). Anxiety symptoms were identified as follows: no anxiety symptoms (0–4), mild anxiety symptoms (5–9), moderate anxiety symptoms (10–14), and severe anxiety symptoms (15 or more).
Perceived stress was measured using the Perceived Stress Scale (PSS-10) scale from the study by Cohen et al. (56). This instrument, with its many benefits, is widely recommended for use across cultures and also in student populations (57–59). In this case, respondents could select one of the following answers for each of the 10 items: never-−0, almost never-−1, sometimes-−2, fairly often-−3, very often-−4. The ranges of the total PSS-10 score indicated: low stress (0–13), moderate stress (14–26), and high stress (27–40). Accordingly, the higher the total score, the more severe the psychological distress.
The research focused predominantly on internet addiction as measured by the Internet Addiction Test (IAT) developed by Young (60). The IAT represents an instrument with satisfactory psychometric properties that is commonly used in studies focusing on college students (61, 62). The scale consisted of 20 items with the following answers: not applicable-−0, rarely-−1, occasionally-−2, frequently-−3, often-−4, always-−5. Internet addiction was identified as follows: normal level of internet usage (0–30), mild addiction symptoms (31–49), moderate addiction symptoms (50–79), and severe addiction symptoms (80–100). The IAT scale was composed of six subscales for which precise interval thresholds are not defined. Therefore, higher score (closer to the theoretical maximum) represented greater addiction. Young (63) explained these subscales as follows:
• Salience. A high score on salience-related items points to the fact that the individual is likely to feel preoccupied with the internet, hides his/her behavior from others, and may show a loss of interest in other activities and/or relationships due to a preference for more solitary time on the internet. A high score also shows that the individual uses the internet as a form of mental escape from disturbing thoughts and may feel that life without the internet would be boring, empty, and/or joyless. Salience is represented by five items (ID: 10, 12, 13, 15, 19) with a theoretical score range of 0–25.
• Excessive Use. A high score on items related to excessive use reflects the fact that the individual engages in excessive online behavior and compulsive usage, and is intermittently unable to control his or her time spent on the internet, which he or she hides from others. The individual with a high score is very likely to fall into depression, panic, or anger if forced to stay off the internet for a prolonged period of time. Excessive use is represented by five items (ID: 1, 2, 14, 18, 20) with a theoretical score range of 0–25.
• Neglect Work. A high score on items related to neglect work points to the fact that the individual may consider the internet to be a necessary appliance, much like a television or telephone. Given the amount of time spent online, work or school performance and productivity are likely to deteriorate, and the individual may begin to defend or hide time spent online. Time spent on the internet conflicts with responsibilities (duties, school, and work). Neglect work is represented by three items (ID: 6, 8, 9) with a theoretical score range of 0–15.
• Anticipation. A high score on anticipation-related items shows that the individual is most likely to think about being online when not at a computer, and feel compelled to use the internet when offline. Thus, it is a compulsion to be on the internet. Anticipation is represented by two items (ID: 7, 11) with a theoretical score range of 0–10.
• Lack of Control. A high score on items related to lack of control items indicates that the individual has difficulty managing his or her time online. The individual often stays online longer than intended, and others may complain about the amount of time he or she spends online. Lack of control is represented by three items (ID: 5, 16, 17) with a theoretical score range of 0–15.
• Neglect Social Life. A high score on items related to neglecting social life points to the fact that the individual is likely to use online relationships to cope with situational problems and/or to reduce psychological strain and stress. The individual often establishes new relationships with other internet users and uses the internet to make social contacts that may be lacking in his/her life. Thus, it is about replacing offline social life with online. Neglect social life is represented by two items (ID: 3, 4) with a theoretical score range of 0–10.
Table 1 shows the IAT scale items, with the ID column representing the item number for easier assignment to individual subscales.
Table 1. IAT scale items.
Data Collection Process and Sample Selection
Respondents completed an online questionnaire distributed in the first half of 2020 (the first wave of the COVID-19 pandemic in the Czech Republic and Slovakia). Each respondent was given the same information and instructions and no one was offered a reward. Respondents completed the questionnaires in their national language, i.e., Czech or Slovak. The translation was done from English into Slovak and then from Slovak into Czech. The Slovak version was chosen for translation into Czech on the basis of the high similarity between the Slovak and Czech languages. The translated versions were verified by experts and tested on a group of students to confirm their understanding of the questionnaire items.
The data collection process was carried out in two steps. In the first step, representatives of colleges (deans, vice-deans, study officers) and student councils were asked by emails to share the online questionnaire among their students and student groups, and invited them to complete it. The questionnaire was also distributed to students through the social network Facebook. In the second step, addressed emails (from publicly available contact databases) were sent to college teachers of certain fields of study in some colleges requesting them to distribute the questionnaire to their students. The aim of the second step was to reach the missing segments of students in order to achieve the target sample structure. In order to have a representative sample in line with the study population, the sample was monitored based on the criterion of representation of colleges. In both countries, 80% of all colleges were covered. The interest was also to obtain a sample with a more consistent proportion of study fields and to have at least 30 observations for each field.
With regard to data processing, procedures were carried out to exclude irrelevant statistical units. Thus, 179 statistical units were excluded on the basis of a control item, 27 statistical units on the basis of the identification of a system error in the recording of responses, and 87 statistical units on the basis of the foreign nationality of the students (the survey focused on domestic students). In total, the research sample included 3,099 respondents (Czech Republic = 1,422; Slovakia = 1,677). For some identifying characteristics, respondents provided an apparently incorrect answer (e.g., 1,000 as the year of birth), these responses were deleted and appear as missing data in the analyzes. The selection process to obtain the final sample is provided in Figure 1.
Figure 1. Selection process to obtain the final sample.
Table 2 shows the basic characteristics of the research sample. Although the Czech Republic and Slovakia share a common history, the current specificities of these countries mean that each country should be evaluated separately. On this basis, it was possible to identify some inconsistencies. In terms of gender, higher proportions of female students were identified. This phenomenon is the result of a higher willingness of females to answer questionnaires, but also of the fact that more females (60%) than males (40%) attend college in the selected countries (64). The increased proportions in the younger age groups can be explained by the fact that students tend to be at a young age and older students (mostly part-time) represent a disproportionately smaller group in the higher education environment.
Table 2. Profile of the sample.
Statistical Analysis
The analysis focused on describing the data along with testing for differences, and on assessing possible relationships. Non-parametric tests of differences were used to assess differences, namely the Mann Whitney U-test for two categories and the Kruskal-Wallis H-test for three or more categories. Multiple negative binomial regression was used to assess the relationships, where the dependent variable was IAT and its subscales and the independent variables were characteristics such as gender, age, residence (urban/rural), family (incomplete/complete), housing during the semester (at home/away from home), form of study (part-time/full-time), PHQ-9, PSS-10, and GAD-7. The reference categories for nominal variables were selected with an emphasis on the frequency of observations in a given category and the logical interpretation of the results.
Statistical processing was performed using SPSS Statistic v. 26 (IBM, Inc., Armonk, NY, US) and programming language R v 4.1.2 (65).
Results
This section presents the results of the analytical procedures. First, the mean values of the total IAT scores and the IAT subscale scores in the sorting categories of the selected characteristics are presented. The presentation of results is provided in order of gender, age, residence, family, housing during the semester, form of study, depressive symptoms (PHQ-9), stress (PSS-10), and anxiety symptoms (GAD-7). Subsequently, the results of a regression model focusing on the relationships between Internet addiction and these selected characteristics are presented.
Results of Frequency Analysis
In terms of overall IAT scores, 984 (69.2%) Czech students and 1,122 (66.9%) Slovak students showed no symptoms of internet addiction, i.e., normal internet usage. Mild internet addiction was found in 387 (27.2%) Czech and 452 (27.0%) Slovak students. Moderate internet addiction was identified in 49 (3.4%) students from the Czech Republic and in 100 (6.0%) students from Slovakia. Two (0.1%) Czech and three (0.2%) Slovak students reported severe internet addiction.
Results of Descriptive and Difference Analyzes
In Tables 3–11, the IAT subscales were compared between groups of students with selected sociodemographic, academic, and psychological characteristics. Socio-demographic and academic characteristics make it possible to identify groups of students who should be given increased attention. Psychological characteristics make it possible to capture the intensity of mental health problems that may co-occur with increased internet addiction.
Table 3. Mean values of the IAT and its subscales in the classification by gender.
Based on the results from Table 3, it was possible to confirm gender differences in Salience, Anticipation and Neglect Social Life in the Czech Republic. In the case of Salience and Neglect Social Life, higher mean values were measured for males, while in the case of Anticipation, a higher value was found for females. There was no difference in the total IAT scores between Czech male and female students. In contrast, significant gender differences between Slovak students were evident in all cases except Anticipation. In these significant cases, higher mean values were measured for males.
Table 4 shows the results of testing the differences in the IAT and its subscales between students in the age classification. Significant differences were evident among both Czech and Slovak students. The only case in which no significant difference was found was Neglect Social Life in the Slovak sample. With a focus on mean values, the most pronounced difference was observed in the oldest age group (31+), where the lowest values were found.
Table 4. Mean values of the IAT and its subscales in the classification by age.
The results from Table 5 indicated that whether students lived in rural or urban areas was unlikely to have a decisive effect in the development of Internet addiction. This was apparent from the fact that the only significant difference between urban and rural students was found in Lack of Control. In this analyzed case, a higher mean value was measured for Czech rural students.
Table 5. Mean values of the IAT and its subscales in the classification by residence.
Similar to residence, the family in which college students grew up did not appear to be significant in the development of internet addiction. This was supported by the results in Table 6, where no significant difference was found.
Table 6. Mean values of the IAT and its subscales in the classification by family.
In addition to the mean values, Table 7 shows the results of testing the differences in the IAT and its subscales between students who lived at home during the semester and students who lived away from home during the semester (e.g., dormitory, private accommodation, etc.). In the Czech sample, significant differences were identified in all cases except Anticipation. In the Slovak sample, significant differences were not found in two cases, namely Anticipation and Neglect Social Life. In all significant cases, higher mean values of internet addiction and its subscales were measured for students who lived away from home during the semester.
Table 7. Mean values of the IAT and its subscales in the classification by housing during the semester.
Significant differences were also revealed in the study form, which are presented along with the mean values in Table 8. For both Czech and Slovak students, significant differences were consistently observed in the total IAT score as well as in all its subscales except Anticipation and Neglect Social Life. Accordingly, Czech and Slovak full-time students reported higher mean values than part-time students.
Table 8. Mean values of the IAT and its subscales in the classification by form of study.
Table 9 presents the mean values and the results of testing for differences between students with different severity of depressive symptoms. In all cases, significant differences were confirmed, with a higher severity of depression being associated with a higher level of internet addiction. This was indicated by the mean values, which increased with higher severity of depressive symptoms in most cases. In other words, Czech and Slovak students with more severe depressive symptoms also showed higher scores for internet addiction and its subscales.
Table 9. Mean values of the IAT and its subscales in the classification by severity of depressive symptoms.
From Table 10, it was possible to conclude that the higher mean values of the total IAT scores, as well as the scores in the vast majority of the IAT subscales, were concentrated in the higher levels of stress. Thus, Czech and Slovak students with higher stress also reported higher internet addiction. Significant differences were found in most of the analyzed cases, with the exception of only one case, namely Neglect Social Life in the Czech sample.
Table 10. Mean values of the IAT and its subscales in the classification by stress level.
The results in Table 11 point to the fact that higher mean scores for internet addiction were observed in Czech and Slovak students with more severe anxiety symptoms. In all cases, significant differences in internet addiction were found between groups of students with different severity of anxiety symptoms.
Table 11. Mean values of the IAT and its subscales in the classification by severity of anxiety symptoms.
Results of Multiple Negative Binomial Regression Analysis
The multiple negative binomial regression was used to assess the relationships between selected socio-demographic, academic, and psychological characteristics of Czech and Slovak college students and internet addiction. In this analysis, several variables (compared to previous outputs) appeared as cardinal variables. These variables were age (CZ = mean: 24.8 ± 6.2, min: 19, max: 52; SK = mean: 23.5 ± 4.5, min: 18, max: 54), PHQ-9 (CZ = mean: 6.3 ± 5.5, min: 0, max: 27; SK = mean: 5.8 ± 5.3, min: 0 max: 27), PSS-10 (CZ = mean: 19.8 ± 5.6, min: 4, max: 38; SK = mean: 19.3 ± 5.3, min: 2, max: 39), and GAD-7 (CZ = mean: 4.7 ± 4.6, min: 0, max: 21; SK = mean: 4.2 ± 4.3, min: 0, max: 21).
Table 12 presents the results of the analysis focusing on the possible relationships between the analyzed variables, and this table is divided into two parts by country—Slovakia (SK) and the Czech Republic (CZ). The table provides information on the β coefficient (positive coefficients present positive relationships and vice versa), p-value (relationships significant at α < 0.05 are highlighted), standard error, and odds ratio (values >1 present positive relationships). As can be seen, a significant number of the analyzed relationships proved to be significant.
Table 12. Relationships of the IAT and its subscales with selected socio-demographic, academic, and psychological characteristics.
Focusing on Slovakia, significant relationships were most frequently found for gender, housing during the semester, PHQ-9 and PSS-10. In the Czech Republic, the results were similar to those in Slovakia, but more significant relationships were found also for age. In both countries, the results showed that male students were more likely to be seriously addicted to the internet compared to female students. Age was particularly significant in the Czech Republic, and the results revealed that younger students were more prone to internet addiction than older ones. Whether a student lived in an urban or rural area was relevant to internet addiction in only one case in both countries. In Slovakia, a slightly higher likelihood of Excessive Use [odds ratio (OR): 1.05] was identified for students living in urban areas. In the Czech Republic, there was a slightly higher likelihood of Lack of Control (OR: 0.93) for students living in rural areas. The family structure (complete/incomplete) in which the students grew up was not so important for internet addiction, as the only significant relationship was found in the Czech Republic. In this case, a slightly higher likelihood of Neglect Work (OR: 0.94) was identified for students from complete families. In terms of housing during the semester, the results indicated that Czech and Slovak students who lived away from home (dormitory, private accommodation, etc.) were more likely to suffer from internet addiction, including several conditions represented by the IAT subscales. The form of study proved to be significant in only one case in Slovakia. More specifically, Slovak full-time students were more likely to report Lack of Control (OR: 0.86) than part-time students. In both countries, depressive symptoms and stress could be considered significant predictors of internet addiction overall, but also in most subscales. Anxiety symptoms were significant in two cases in the Slovak sample. There was a slightly higher likelihood of lower anxiety symptoms and a higher rate of Neglect Work (OR: 0.93) and a slightly higher likelihood of lower anxiety symptoms and a higher rate of Lack of Control (OR: 0.95). One significant relationship was found in the Czech sample. It was possible to observe a slightly higher likelihood of lower anxiety symptoms and a higher rate of Lack of Control (OR: 0.95).
Discussion
College students are definitely a vulnerable group in terms of psychological distress. They face many challenges during their studies, and therefore the college environment should be adapted to maintain and improve their mental health. Moreover, today's students face another threatening factor, i.e., the COVID-19 pandemic, which has brought with it many concerns and changes in the educational process (66, 67). Evidence shows that COVID-19-related fear (to die, to get sick) was relatively more common among young people (68). They were also worried the influence of COVID-19 on daily life, academic delays due to COVID-19, decreased social support during COVID-19 (69, 70). Thus, given that the pandemic poses a risk to the mental health of college students, it should be given adequate attention in research and practice.
Prevalence of Internet Addiction
From an overall perspective, during the first wave of the COVID-19 pandemic in the Czech Republic and Slovakia, mild and more severe symptoms of Internet addiction were identified in one third of college students. Moderate and severe symptoms (the cut-off point for IAT was ≥ 50) occurred in 6.02% of Slovak students and 3.5% of Czech students. In an international comparison, Thai students reported a similar prevalence, i.e., 5.8% (71), while substantially higher prevalence rates were observed in other studies. More specifically, 12.4% of internet-addicted students were found in Spain (72), 16.8% in the United States (73), 20% in Brazil (13), 28.4% in China (74), and 32.6% in Bangladesh (75). On this basis, it can be concluded that Czech and Slovak college students did not report such a high prevalence as in other countries, despite the COVID-19 pandemic. A possible explanation is the cultural differences emphasized in the study conducted by Lozano-Blasco et al. (76). However, the problem is mainly the fact that internet addiction is currently not given sufficient attention in the Czech Republic and Slovakia, which may result in serious consequences in the near future.
Differences and Predictors of Internet Addiction
When assessing internet addiction overall as well as in its subscales, there were differences between students in almost all cases in Slovakia, while in the Czech Republic the occurrence of differences was not as frequent. In more detail, there were several significant differences between males and females. In the case of age, older students reported lower rates of internet addiction. At the same time, students who lived away from home during the semester were found to have significantly higher rates of internet addiction compared to students who lived at home. In terms of the form of study, it can be noted that higher scores for internet addiction and most of its subscales were observed in full-time students. The results for place of residence (urban and rural) and family structure did not show significant differences. The results showed that there were significant differences in internet addiction between the categories of depressive symptoms, anxiety symptoms, and stress symptoms. This finding can be interpreted in such a way that higher mean IAT scores occurred in students with higher levels of psychological distress. The results of the multiple negative binomial regression analysis showed that the main predictors of internet addiction in Czech and Slovak students include gender, age, housing during the semester, depressive symptoms, and stress. The main findings are discussed below.
Gender
In both countries, male students were more likely to suffer from internet addiction than female students. Other studies also revealed a higher risk of problematic internet use or internet addiction in male students compared to female students (18, 38, 40–43). The predominance of males in this type of problematic behavior is evident (77). Online behavior is considered more “normal” for males than females. In this context, social norms, socio-cultural attitudes, but also a lower digital participation of females may play an important role in terms of protective factors (78). However, there is also evidence of female susceptibility to internet addiction (79). A possible explanation for this is the pandemic itself, which may have erased male dominance in terms of internet addiction (6). Nevertheless, such a result occurs less frequently in studies. According to Lin et al. (36), the main online activity among college students with moderate and severe internet addiction was online gaming for males and online streaming for females. Having roommates who engage in similar internet entertainment was a risk factor for internet addiction only for males, while the absence of a romantic relationship was a risk factor for internet addiction only for females. Internet infatuation before college and difficulty adjusting to college life were common risk factors for both genders in the groups of students with mild and moderate internet addiction. In any case, gender is a significant factor in the issue of internet addiction (32).
Age
In terms of other demographic characteristics, Khazaie et al. (37) revealed that age is also one of the significant predictors of internet addiction. In this study, younger age appeared to be a risk factor especially in the Czech Republic. Similar findings were revealed by Fernández-Villa et al. (12). This is consistent with the findings of Arzani-Birgani et al. (38), who concluded that age is inversely related to internet addiction. Regarding the new younger generations, their greater susceptibility can be explained by an increase of individualism, lower sociability and enculturation (76).
Residence
With a focus on place of residence, Slovak urban students showed a slightly higher likelihood of Excessive Use, while Czech rural students showed a slightly higher likelihood of Lack of Control. The identified discrepancy between the analyzed countries requires further investigation. The results can be compared to those of Zewde et al. (40) who found in their study that students from urban areas are more likely to be addicted to the internet. It is generally known that urban areas are more internet accessible and urban individuals make greater use of the internet, which may explain the higher rates of addictive behavior in urban areas (80–82).
Housing During the Semester
This study also showed consistent results for housing during the semester. More specifically, Czech and Slovak students who lived away from home (dormitory, private accommodation, etc.) were more likely to be addicted to the internet compared to students who lived at home. The results of Ramón-Arbués et al. (39) also showed that the likelihood of problematic internet use was significantly related to the habitation status of college students. However, the results of this study are not consistent with those of Kivrak and Kivrak (83), who found that dormitory students suffer from internet addiction less than those staying at home. Thus, dormitory housing did not prove to be a protective factor in this study, although dormitory students seem to have more difficult access to the internet.
Family
In this study, family structure (complete/incomplete) was not found to be significant in the majority of cases; only one significant relationship with Neglect Work was found in the Czech Republic. This is inconsistent with other studies that emphasize the role of the family environment in the issue of Internet addiction (84, 85). Regarding the form of study, Slovak full-time students were more likely to report Lack of Control compared to part-time students. A similar result was found in a neighboring country, Poland. Thus, increased problematic internet use was observed in full-time students (43). However, this study did not show any other significant relationship between the form of study and internet addiction or its subscale. Thus, family and study form did not play as important a role as in other studies. Possible explanations are cultural differences and different study conditions across countries.
Depressive Symptoms, Anxiety Symptoms, Stress
In addition to the socio-demographic profile, significant relationships were also clearly evident for the psychological profile of the students. In this context, Czech and Slovak students with depressive symptoms were more prone to internet addiction. In comparison with national studies, it was also revealed in Slovakia by Sebena et al. (44) that depressive symptoms predict problematic internet use. In international comparison, similar results were confirmed by Yoo et al. (86) and Chi et al. (84). Truzoli et al. (18) also revealed similar results and confirmed that students with severe depression are at higher risk of problematic internet use during the COVID-19 pandemic, while social support, self-efficacy, and self-esteem appeared to be protective factors in their study. It was possible to agree with Werner et al. (6), who found that depressive symptoms were associated with internet addiction in college students, and this phenomenon was observed in both the pre-pandemic and pandemic periods. However, a bidirectional association can also be considered. The fact is that baseline depression has a significant net-predictive effect on follow-up internet addiction, and baseline internet addiction has a significant net-predictive effect on follow-up depression (87). The issue is thus more complex than it seems.
In terms of stress, the results of this study are consistent with those of Yang et al. (31), who found that stress is associated with internet addiction in college medical students. Gong et al. (33) also confirmed a significant predictive effect of perceived stress on internet addiction among college students. Thus, it can be concluded that perceived stress increases the risk of internet addiction among young people (86).
Overall, the findings of this study are consistent with those of Mota et al. (16), who stated that common mental disorders are the variables that best explain problematic internet use, and emphasized that high levels of digital socialization should be considered in mental health care interventions targeting college students in the context of COVID-19. In other words, mental distress is a significant risk factor for internet addiction (88), and the presented study contributes to this knowledge. For students with depression and stress, internet use is believed to be a coping strategy for negative emotions (89–91) or stressful situations (89, 92).
Finally, this study revealed that lower anxiety occurred in students with a higher rate of internet addiction in subscales such as Neglect Work and Lack of Control. These findings can be explained by the fact that higher score on internet addiction may be a protective factor against anxiety in students, which was revealed in a study by Ismail et al. (93). On the other hand, there are inconsistent findings (94, 95). Cai et al. (30) found that students with more severe anxiety symptoms were independently and significantly associated with more severe problematic internet use. In other words, students with a higher level of anxiety tend to have a greater likelihood of internet addiction (32). Thus, it was possible to observe inconsistencies in the results with other studies (6), suggesting that the relationship between anxiety and internet use is a complex issue, as in the case of depression.
Based on all the above-mentioned findings, it was possible to agree that the co-existence of internet addiction and depressive symptoms, anxiety symptoms, and stress in college students indicates that internet addiction is associated with other psychological problems (38). From the opposite point of view, students who were psychologically well were less likely to be addicted (35, 42).
Implications for Mental Health Policies
Internet addiction and psychological distress in college students are two problems that require increased attention at all times, especially in a difficult period such as a pandemic. The findings of this study may emphasize the importance of the issue and encourage the development of early preventive and protective interventions against problematic internet use to promote the health of college students in the Czech Republic and Slovakia as two countries that are largely neglected in terms of proactive steps. In addition to promoting healthy use of the internet as such, policy makers should take actions to alleviate psychological distress among college students, which could be beneficial in reducing internet addiction. It is recommended to provide college students with the necessary psychological care, especially for symptoms of depression and stress. In these cases, excessive use of the internet is not recommended to improve mood, as it can contribute to deteriorating mental health (17). On the contrary, it is highly recommended to promote the benefits of physical activity (32, 96). This study also suggests that strategies and programs should particularly target male students and students who lived away from home during the semester. All relevant actors, including college teachers, experts, parents and policy makers, should be involved in addressing this problem, which requires a comprehensive approach. This is emphasized particularly in countries such as the Czech Republic and Slovakia, where it is evident that insufficient attention is being paid to this problem, which may have more serious consequences in the future. In these countries, access to help for students should be improved, both in the college environment and in the specialized counseling centers. In this context, it is recommended to identify in time the students who need this help, as well as to provide relevant assistance to students seeking help. However, all these steps inevitably require social, legislative and professional support, which is currently underestimated and insufficient in both countries (97). The authors call for greater research interest in this issue and for the implementation of evidence-based measures into practice in the Czech and Slovak conditions.
Strengths and Limitations
This study has many strengths, which include in particular the sample size, but also the uniqueness of the findings for mental health policies in the Czech Republic and Slovakia, where the issue is under-researched and poorly evidence-based. The findings of this study fill a substantial research gap in this region and can therefore be considered novel. The study offers a detailed statistical insight into the issue and provides a valuable basis for further research, as well as effective mechanisms and successful strategies and programs.
The study did not avoid certain limitations. In this respect, the situation in the Czech Republic and Slovakia during the pandemic did not allow for a fully balanced sample. For this reason, the ratio of male to female students was slightly skewed in favor of female students (especially in the Czech Republic). As males are generally more addicted to the internet compared to females, there may have been some bias. Differences in the duration and mode of online (distance) learning in the Czech Republic and Slovakia may have had some, but minimal, effects on the sample. During the pandemic, distance learning was implemented to a greater extent in Slovakia than in the Czech Republic. In terms of country development, it can be assumed that the initial implementation of distance learning in Slovakia was of lower quality than in the Czech Republic. However, the authors do not expect this bias to be so significant as to substantially affect the results.
The research was conducted during the pandemic characterized by many specifics (online learning, isolation, social distance, etc.); therefore, future research should focus on the post-pandemic state.
Conclusion
Internet addiction among young adults is a phenomenon of recent years that has been more highlighted by the COVID-19 pandemic. Physical distance has required college students to use the internet for virtually all daily activities during the pandemic. For this reason, it is important to identify and monitor vulnerable groups in order to prevent the situation from worsening in the future. However, this cannot be done without knowledge of the risk factors that commonly characterize students and which should be the focus of proactive actions. All of this was the motivation for conducting the presented study, which aimed to assess the relationships between internet addiction and selected socio-demographic, study-related, and psychological characteristics of college students in the Czech Republic and Slovakia. In addition, the rationale for the research was based on the previous lack of attention in these two countries. This study mapped the situation and pointed to a problem that could have serious consequences in the future. The main findings showed that male students and students who lived away from home during the semester were more likely to be addicted to the internet both overall and in terms of most of the individual subscales of internet addiction. Depressive symptoms and stress could also be considered significant predictors in both countries. Thus, these groups of students can be identified as vulnerable and should be targeted by protective and preventive actions. The existing relationships between internet addiction and positive screening for psychological distress and other demographic and academic characteristics of students are an alert for professionals to address and further investigate this serious issue. Knowledge of these relationships provides an opportunity to implement effective protective and preventive measures, such as psychoeducation, and to offer appropriate treatment.
Data Availability Statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics Statement
The research was approved by the Ethics Committee of the General University Hospital in Prague as individual research (Ref. 915/20 S–IV). The study was conducted according to the guidelines of the Declaration of Helsinki. All respondents were over 18 years of age and provided their informed consent to participate in this research.
Author Contributions
BG: conceptualization, investigation, writing—original draft preparation, writing—review and editing, visualization, supervision, project administration, and funding acquisition. VI: conceptualization, methodology, investigation, resources, writing—original draft preparation, writing—review and editing, visualization, supervision, and funding acquisition. MR: conceptualization, methodology, software, data curation, formal analysis, investigation, writing—original draft preparation, writing—review and editing, and visualization. TM: conceptualization, investigation, resources, writing—original draft preparation, writing—review and editing, visualization, supervision, and project administration. All authors contributed to manuscript revision, read, and approved the submitted version.
Funding
This research was funded by the Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic and the Slovak Academy Sciences as part of the research project VEGA 1/0797/20: Quantification of Environmental Burden Impacts of the Slovak Regions on Health, Social and Economic System of the Slovak Republic. This research was supported by the Slovak Research and Development Agency under the contract No. APVV-17-0360: Multidimensional analysis of significant determinants of public procurement efficiency with emphasis on the application of Health Technology Assessment in the procurement preparation phase. This study was supported by Institutional Research Program PROGRES No. Q06/LF1.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Acknowledgments
The authors thank the journal editor and reviewers for their guidance and constructive suggestions.
References
1. Hashemi Y, Zarani F, Heidari M, Borhani K. Purposes of internet use among Iranian university students: exploring its relationship with social networking site (SNS) addiction. BMC Psychol. (2022) 10:80. doi: 10.1186/s40359-022-00745-4
2. Ehsan A, Iqbal F, Rao MA. Frequency and risk factors of internet addiction in medical students: a cross-sectional study. J Pak Med Assoc. (2021) 8:2111. doi: 10.47391/JPMA.451
3. Balasubramanian N, Parayitam S. Antecedents and consequences of internet addiction among school and college students: evidence from India. Glob Knowl Mem Commun. (2022) 2021:211. doi: 10.1108/GKMC-12-2021-0211
4. Beard K, Wolf E. Modification in the proposed diagnostic criteria for internet addiction. CyberPsychol Behav. (2001) 4:377–83. doi: 10.1089/109493101300210286
5. Moreno MA, Jelenchick LA, Christakis DA. Problematic internet use among older adolescents: a conceptual framework. Comput Human Behav. (2013) 29:1879–87. doi: 10.1016/j.chb.2013.01.053
6. Werner AM, Petersen J, Muller KW, Tibubos AN, Schafer M, Mulder LM, et al. Prevalence of internet addiction before and during the COVID-19 pandemic among students at Johannes Gutenberg University Mainz, Germany. Suchttherapie. (2021) 22:183–93. doi: 10.1055/a-1653-8186
7. Elbilgahy AA, Sweelam RK, Eltaib FA, Bayomy HE, Elwasefy SA. Effects of electronic devices and internet addiction on sleep and academic performance among female Egyptian and Saudi nursing students: a comparative study. SAGE Open Nurs. (2021) 7:23779608211055614. doi: 10.1177/23779608211055614
8. Tahir MJ, Malik NI, Ullah I, Khan HR, Perveen S, Ramalho R, et al. Internet addiction and sleep quality among medical students during the COVID-19 pandemic: a multinational cross-sectional survey. PLoS ONE. (2021) 16:e0259594. doi: 10.1371/journal.pone.0259594
9. Singh A, Srivastava DK. Understanding the effect of internet addiction on student academic engagement. Int J Inf Commun Technol Educ. (2021) 17:1–12. doi: 10.4018/IJICTE.20211001.oa11
10. Özarici E, Cangöl Sögüt S. The relationship between internet addiction and risky health behaviors in university students: a cross-sectional study in Turkey. Perspect Psychiatr Care. (2022) 58:214–20. doi: 10.1111/ppc.12872
11. Peltzer K, Pengpid S, Apidechkul T. Heavy internet use and its associations with health risk and health-promoting behaviours among Thai university students. Int J Adolesc Med Health. (2014) 26:187–94. doi: 10.1515/ijamh-2013-0508
12. Fernández-Villa T, Ojeda JA, Gómez AA, Carral JMC, Delgado-Rodríguez M, García-Martín M, et al. Problematic Internet Use in university students: associated factors and differences of gender. Adicciones. (2015) 27:265–75. doi: 10.20882/adicciones.751
13. Corrêa Rangel T, Falcão Raposo MC, Sampaio Rocha-Filho PA. Internet addiction, headache, and insomnia in university students: a cross-sectional study. Neurol Sci. (2022) 43:1035–41. doi: 10.1007/s10072-021-05377-x
14. Nayak A, Saranya K, Fredrick J, Madumathy R, Subramanian SK. Assessment of burden of internet addiction and its association with quality of sleep and cardiovascular autonomic function in undergraduate medical students. Clin Epidemiol Glob Health. (2021) 11:100773. doi: 10.1016/j.cegh.2021.100773
15. Hosen I, Al Mamun F, Mamun MA. The role of sociodemographics, behavioral factors, and internet use behaviors in students' psychological health amid COVID-19 pandemic in Bangladesh. Health Sci Rep. (2021) 4:e398. doi: 10.1002/hsr2.398
16. Mota DCB, Silva YVD, Costa TAF, Aguiar MHDC, Marques MEM, Monaquezi RM. Mental health and internet use by university students: coping strategies in the context of COVID-19. Cien Saude Colet. (2021) 26:2159–70. doi: 10.1590/1413-81232021266.44142020
17. Zalewska A, Gałczyk M, Sobolewski M, Białokoz-Kalinowska I. Depression as compared to level of physical activity and internet addiction among polish physiotherapy students during the COVID-19 pandemic. Int J Environ Res Public Health. (2021) 18:10072. doi: 10.3390/ijerph181910072
18. Truzoli R, Pirola V, Celebre L, Piccoli E, Vanzetto S, Conti D, et al. Intrapersonal and social factors for problematic internet use among students during the COVID-19 pandemic. Psychiatr Danub. (2021) 33:144–54.
19. Gecaite-Stonciene J, Saudargiene A, Pranckeviciene A, Liaugaudaite V, Griskova-Bulanova I, Simkute D, et al. 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: 10.3389/fpsyt.2021.634464
20. Dong H, Yang F, Lu X, Hao W. Internet addiction and related psychological factors among children and adolescents in China during the coronavirus disease 2019 (COVID-19) epidemic. Front Psychiatry. (2020) 11:00751. doi: 10.3389/fpsyt.2020.00751
21. Rogier G, Zobel SB, Velotti P. COVID-19, loneliness and technological addiction: longitudinal data. J Gambl Issues. (2021) 47:108–20. doi: 10.4309/jgi.2021.47.4
22. Håkansson A. Changes in gambling behavior during the COVID-19 pandemic—a web survey study in Sweden. Int J Environ Res Public Health. (2020) 17:4013. doi: 10.3390/ijerph17114013
23. Lemenager T, Neissner M, Koopmann A, Reinhard I, Georgiadou E, Müller A, et al. COVID-19 lockdown restrictions and online media consumption in Germany. Int J Environ Res Public Health. (2021) 18:14. doi: 10.3390/ijerph18010014
24. Li YY, Sun Y, Meng SQ, Bao YP, Cheng JL, Chang XW, et al. Internet addiction increases in the general population during COVID-19: evidence from China. Am J Addict. (2021) 30:389–97. doi: 10.1111/ajad.13156
25. Kuss DJ, Griffiths MD, Binder JF. Internet addiction in students: prevalence and risk factors. Comput Hum Behav. (2013) 29:959–66. doi: 10.1016/j.chb.2012.12.024
26. Griffiths M. Does internet and computer “addiction” exist? Some case study evidence. CyberPsychol Behav. (2000) 3:211–8. doi: 10.1089/109493100316067
27. Odaci H, Çikrici Ö. An exploration of the associations among internet use, depression, anxiety and stress among youths. Mediterr J Clin Psychol. (2017) 5:1–16. doi: 10.6092/2282-1619/2017.5.1635
28. Xie X, Zhu K, Xue Q, Zhou Y, Liu Q, Wu H, et al. Problematic Internet use was associated with psychological problems among university students during COVID-19 outbreak in China. Front Public Health. (2021) 9:675380. doi: 10.3389/fpubh.2021.675380
29. Masaeli N, Farhadi H. Prevalence of internet-based addictive behaviors during COVID-19 pandemic: a systematic review. J Addict Dis. (2021) 39:468–88. doi: 10.1080/10550887.2021.1895962
30. Cai H, Xi HT, Zhu Q, Wang Z, Han L, Liu S, et al. Prevalence of problematic Internet use and its association with quality of life among undergraduate nursing students in the later stage of COVID-19 pandemic era in China. Am J Addict. (2021) 30:585–92. doi: 10.1111/ajad.13216
31. Yang Q, Wu Z, Yang X, Jiang S, Wu D, Oliffe JL. Associations between uncertainty stress, life stress and internet addiction among medical students. Front Public Health. (2022) 9:809484. doi: 10.3389/fpubh.2021.809484
32. Ye S, Cheng H, Zhai Z, Liu H. Relationship between social anxiety and internet addiction in Chinese college students controlling for the effects of physical exercise, demographic, and academic variables. Front Psychol. (2021) 12:698748. doi: 10.3389/fpsyg.2021.698748
33. Gong Z, Wang L, Wang H. Perceived stress and internet addiction among Chinese college students: mediating effect of procrastination and moderating effect of flow. Front Psychol. (2021) 12:632461. doi: 10.3389/fpsyg.2021.632461
34. Seo EH, Kim SG, Lee SK, Park SC, Yoon HJ. Internet addiction and its associations with clinical and psychosocial factors in medical students. Psychiatry Investig. (2021) 18:408–16. doi: 10.30773/pi.2020.0405
35. de Ávila GB, Dos Santos ÉN, Jansen K, Barros FC. Internet addiction in students from an educational institution in Southern Brazil: prevalence and associated factors. Trends Psychiatry Psychother. (2020) 42:302–10. doi: 10.1590/2237-6089-2019-0098
36. Lin X, Gu JY, Guo WJ, Meng YJ, Wang HY, Li XJ, et al. The gender-sensitive social risk factors for internet addiction in college undergraduate students. Psychiatry Investig. (2021) 18:636–44. doi: 10.30773/pi.2020.0277
37. Khazaie H, Lebni JY, Abbas J, Mahaki B, Chaboksavar F, Kianipour N, et al. Internet addiction status and related factors among medical students: a cross-sectional study in Western Iran. Int Q Community Health Educ. (2021). doi: 10.1177/0272684X211025438
38. Arzani-Birgani A, Zarei J, Favaregh L, Ghanaatiyan E. Internet addiction, mental health, and sleep quality in students of medical sciences, Iran: a cross-sectional study. J Educ Health Promot. (2021) 10:409. doi: 10.4103/jehp.jehp_1506_20
39. Ramón-Arbués E, Granada-López JM, 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: 10.3390/ijerph18147620
40. Zewde EA, Tolossa T, Tiruneh SA, Azanaw MM, Yitbarek GY, Admasu FT, et al. Internet addiction and its associated factors among African high school and university students: systematic review and meta-analysis. Front Psychol. (2022) 13:847274. doi: 10.3389/fpsyg.2022.847274
41. Esen PY, Kutlu R, Cihan FG. Internet addiction, substance use and alexithymic dimensions in two different faculties' students. Cent Eur J Public Health. (2021) 29:209–16. doi: 10.21101/cejph.a6786
42. Koo K, Nyunt G, Wang B. Who spends too much time online? Associated factors of internet addiction among international college students in the United States. J Int Students. (2021) 11:122–43. doi: 10.32674/jis.v11i1.2063
43. Kozybska M, Kurpisz J, Radlińska I, Skwirczyńska E, Serwin N, Zabielska P, et al. Problematic internet use, health behaviors, depression and eating disorders: a cross-sectional study among Polish medical school students. Ann Gen Psychiatry. (2022) 21:5. doi: 10.1186/s12991-022-00384-4
44. Sebena R, Orosova O, Benka J. Are self-regulation and depressive symptoms predictors of problematic Internet use among first year university students? PsychNology J. (2013) 11:235–49.
45. Chraska M. Typology of Czech university students by their internet addiction. In: Besedova P, Heinrichova N, Ondrakova J, editors, European Proceedings of Social and Behavioural Sciences. 10th International Conference on Education and Educational Psychology (ICEEPSY); 2019 Oct 9–12. Barcelona. Nicosia: Future Academy (2019). p. 558–67.
46. Kopecký K, Hinojo-Lucena FJ, Szotkowski R, Gomez-Garcia G. Behaviour of young Czechs on the digital network with a special focus on YouTube An analytical study. Child Youth Serv Rev. (2020) 116:105191. doi: 10.1016/j.childyouth.2020.105191
47. Petruzelka B, Vacek J, Gavurova B, Kubak M, Gabrhelik R, Rogalewicz V, et al. Interaction of socioeconomic status with risky internet use, gambling and substance use in adolescents from a structurally disadvantaged region in Central Europe. Int J Environ Res Public Health. (2020) 17:4803. doi: 10.3390/ijerph17134803
48. Hostovecký M, Prokop P. The relationship between internet addiction and personality traits in Slovak secondary schools students. J Appl Math Stat Inform. (2018) 14:83–101. doi: 10.2478/jamsi-2018-0006
49. Kroenke K, Spitzer RL, Williams JB, Löwe B. The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. Gen Hosp Psychiatry. (2010) 32:345–59. doi: 10.1016/j.genhosppsych.2010.03.006
50. Costantini L, Pasquarella C, Odone A, Colucci ME, Costanza A, Serafini G, et al. Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): a systematic review. J Affect Disord. (2021) 279:473–83. doi: 10.1016/j.jad.2020.09.131
51. Zajenkowska A, Jasielska D, Melonowska J. Stress and sensitivity to frustration predicting depression among young adults in Poland and Korea - psychological and philosophical explanations. Curr Psychol. (2019) 38:769–74. doi: 10.1007/s12144-017-9654-0
52. Miranda CAC, Scoppetta O. Factorial structure of the Patient Health Questionnaire-9 as a depression screening instrument for university students in Cartagena, Colombia. Psychiatry Res. (2018) 269:425–9. doi: 10.1016/j.psychres.2018.08.071
53. Lee B, Kim YE. The psychometric properties of the Generalized Anxiety Disorder scale (GAD-7) among Korean university students. Psychiatr Clin Psychopharmacol. (2019) 29:864–71. doi: 10.1080/24750573.2019.1691320
54. Sriken J, Johnsen ST, Smith H, Sherman MF, Erford BT. Testing the factorial validity and measurement invariance of college student scores on the Generalized Anxiety Disorder (GAD-7) scale across gender and race. Meas Eval Couns Dev. (2022) 55:1–16. doi: 10.1080/07481756.2021.1902239
55. Byrd-Bredbenner C, Eck K, Quick V. Psychometric properties of the generalized anxiety disorder-7 and generalized anxiety disorder-mini in United States university students. Front Psychol. (2020) 11:550533. doi: 10.3389/fpsyg.2020.550533
56. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. (1983) 24:385–96. doi: 10.2307/2136404
57. Örücü MÇ, Demir A. Psychometric evaluation of perceived stress scale for Turkish university students. Stress Health. (2009) 25:103–9. doi: 10.1002/smi.1218
58. Makhubela M. Assessing psychological stress in South African university students: measurement validity of the perceived stress scale (PSS-10) in diverse populations. Curr Psychol. (2022) 41:2802–9. doi: 10.1007/s12144-020-00784-3
59. Roberti JW, Harrington LN, Storch EA. Further psychometric support for the 10-item version of the perceived stress scale. J Coll Couns. (2006) 9:135–47. doi: 10.1002/j.2161-1882.2006.tb00100.x
60. Young KS. Internet addiction: the emergence of a new clinical disorder. Cyber Psychol Behav. (1998) 1:237–44. doi: 10.1089/cpb.1998.1.237
61. Fernández-Villa T, Molina AJ, García-Martín M, Llorca J, Delgado-Rodríguez M, Martín V. Validation and psychometric analysis of the Internet Addiction Test in Spanish among college students. BMC Public Health. (2015) 15:953. doi: 10.1186/s12889-015-2281-5
62. Fioravanti G, Casale S. Evaluation of the psychometric properties of the Italian Internet Addiction Test. Cyberpsychol Behav Soc Netw. (2015) 18:120–8. doi: 10.1089/cyber.2014.0493
64. Centrum vedecko-technických informácií Slovenskej republiky (CVTI SR). Štatistická ročenka – vysoké školy. (2021) Available online at: https://www.cvtisr.sk/cvti-sr-vedecka-kniznica/informacie-o-skolstve/statistiky/statisticka-rocenka-publikacia/statisticka-rocenka-vysoke-skoly.html?page_id=9596 (accessed May 25, 2022).
65. R Core Team. R: A Language Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. (2021). Available online at: https://www.R-project.org/ (accessed May 14, 2022).
66. Atlam E, Ewis A, Abd El-Raouf MM, Ghoneim O, Gad I. A new approach in identifying the psychological impact of COVID-19 on university student's academic performance. Alex Eng J. (2021) 61:5223–33. doi: 10.1016/j.aej.2021.10.046
67. Branchu C, Flaureau E. “I'm not listening to my teacher, I'm listening to my computer:” online learning, disengagement, and the impact of COVID-19 on French university students. High Educ. (2022) 854:4. doi: 10.1007/s10734-022-00854-4
68. Costanza A, Macheret L, Folliet A, Amerio A, Aguglia A, Serafini G, et al. COVID-19 related fears of patients admitted to a psychiatric emergency department during and post-lockdown in Switzerland: preliminary findings to look ahead for tailored preventive mental health strategies. Medicina. (2021) 57:1360. doi: 10.3390/medicina57121360
69. Dhar BK, Ayittey FK, Sarkar SM. Impact of COVID-19 on psychology among the university students. Glob Chall. (2020) 4:2000038. doi: 10.1002/gch2.202000038
70. Son C, Hegde S, Smith A, Wang X, Sasangohar F. Effects of COVID-19 on college students' mental health in the United States: interview survey study. J Med Internet Res. (2020) 22:e21279. doi: 10.2196/21279
71. Wongpakaran N, Wongpakaran T, Pinyopornpanish M, Simcharoen S, Kuntawong P. Loneliness and problematic internet use: testing the role of interpersonal problems and motivation for internet use. BMC Psychiatry. (2021) 21:447. doi: 10.1186/s12888-021-03457-y
72. Romero-Rodríguez J-M, Marín-Marín J-A, Hinojo-Lucena F-J, Gómez-García G. An explanatory model of problematic Internet use of Southern Spanish university students. Soc Sci Comput Rev. (2021) 31:100–10. doi: 10.1177/0894439321998650
73. Younes F, Halawi G, Jabbour H, El Osta N, Karam L, Hajj A, et al. Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in university students: a cross-sectional designed study. PLoS ONE. (2016) 11:e0161126. doi: 10.1371/journal.pone.0161126
74. Zhao Y, Jiang Z, Guo S, Wu P, Lu Q, Xu Y, et al. Association of symptoms of attention deficit and hyperactivity with problematic internet use among university students in Wuhan, China during the COVID-19 Pandemic. J Affect Disord. (2021) 286:220–7. doi: 10.1016/j.jad.2021.02.078
75. Mamun MA, Hossain MS, Siddique AB, Sikder MT, Kuss DJ, Griffiths MD. Problematic internet use in Bangladeshi students: the role of socio-demographic factors, depression, anxiety, and stress. Asian J Psychiatr. (2019) 44:48–54. doi: 10.1016/j.ajp.2019.07.005
76. Lozano-Blasco R, Robres AQ, Sanchez AS. Internet addiction in young adults: a meta-analysis and systematic review. Comput Hum Behav. (2022) 130:107201. doi: 10.1016/j.chb.2022.107201
77. Aigelová E, Charvát M, Miovsky M. Personality disorders: the treatment of gambling disorders in male patients from the perspective of Cloninger's theory. Adiktologie. (2020) 20:7–18. doi: 10.35198/01-2020-001-0002
78. Su W, Han X, Jin C, Yan Y, Potenza MN. Are males more likely to be addicted to the internet than females? A meta-analysis involving 34 global jurisdictions. Comput Hum Behav. (2019) 99:86–100. doi: 10.1016/j.chb.2019.04.021
79. Shehata WM, Abdeldaim DE. Internet addiction among medical and non-medical students during COVID-19 pandemic, Tanta University, Egypt. Environ Sci Pollut Res Int. (2021) 28:59945–52. doi: 10.1007/s11356-021-14961-9
80. Karmakar T. A comparative study on internet use by rural and urban college students. Int J Adv Res. (2017) 5:1125–32. doi: 10.21474/IJAR01/3615
81. Pawłowska B, Zygo M, Potembska E, Kapka-Skrzypczak L, Dreher P, Kedzierski Z. Prevalence of Internet addiction and risk of developing addiction as exemplified by a group of Polish adolescents from urban and rural areas. Ann Agric Environ Med. (2015) 22:129–36. doi: 10.5604/12321966.1141382
82. Stavropoulos V, Alexandraki K, Motti-Stefanidi F. Recognizing internet addiction: prevalence and relationship to academic achievement in adolescents enrolled in urban and rural Greek high schools. J Adolesc. (2013) 36:565–76. doi: 10.1016/j.adolescence.2013.03.008
83. Kivrak Y, Kivrak I. Internet addiction in adolescents and staying at a dormitory: a controlled study. Eur Psychiatry. (2017) 41:S690. doi: 10.1016/j.eurpsy.2017.01.1208
84. Chi X, Lin L, Zhang P. Internet addiction among college students in China: prevalence and psychosocial correlates. Cyberpsychol Behav Soc Netw. (2016) 19:567–73. doi: 10.1089/cyber.2016.0234
85. Lin YH, Gau SS. Association between morningness-eveningness and the severity of compulsive Internet use: the moderating role of gender and parenting style. Sleep Med. (2013) 14:1398–404. doi: 10.1016/j.sleep.2013.06.015
86. Yoo YS, Cho OH, Cha KS. Associations between overuse of the internet and mental health in adolescents. Nurs Health Sci. (2014) 16:193–200. doi: 10.1111/nhs.12086
87. Yang X, Guo WJ, Tao YJ, Meng YJ, Wang HY, Li XJ, et al. A bidirectional association between internet addiction and depression: a large-sample longitudinal study among Chinese university students. J Affect Disord. (2022) 299:416–24. doi: 10.1016/j.jad.2021.12.013
88. Zenebe Y, Kunno K, Mekonnen M, Bewuket A, Birkie M, Necho M, et al. Prevalence and associated factors of internet addiction among undergraduate university students in Ethiopia: a community university-based cross-sectional study. BMC Psychol. (2021) 9:4. doi: 10.1186/s40359-020-00508-z
89. Brand M, Young KS, Laier C, Wölfling K, Potenza MN. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: an interaction of person-affect-cognition-execution (I-PACE) model. Neurosci Biobehav Rev. (2016) 71:252–66. doi: 10.1016/j.neubiorev.2016.08.033
90. Davis RA. Cognitive-behavioral model of pathological internet use. Comput Human Behav. (2001) 17:187–95. doi: 10.1016/S0747-5632(00)00041-8
91. Sariyska R, Reuter M, Lachmann B, Montag C. Attention deficit/hyperactivity disorder is a better predictor for problematic internet use than depression: evidence from Germany. J Addict Res Ther. (2015) 6:209. doi: 10.4172/2155-6105.1000209
92. Stracke K, Brand M. Effects of stress on decisions under uncertainty: a meta-analysis. Psychol Bull. (2016) 142:909–33. doi: 10.1037/bul0000060
93. Ismail N, Tajjudin AI, Jaafar H, Nik Jaafar NR, Baharudin A, Ibrahim N. The relationship between internet addiction, internet gaming and anxiety among medical students in a Malaysian Public University during COVID-19 Pandemic. Int J Environ Res Public Health. (2021) 18:11870. doi: 10.3390/ijerph182211870
94. Lee BW, Leeson PRC. Online gaming in the context of social anxiety. Psychol Addict Behav. (2015) 29:473–82. doi: 10.1037/adb0000070
95. Lee-Won RJ, Herzog L, Park SG. Hooked on Facebook: the role of social anxiety and need for social assurance in problematic use of Facebook. Cyberpsychol Behav Soc Netw. (2015) 18:567–74. doi: 10.1089/cyber.2015.0002
96. Khan MA, Shabbir F, Rajput TA. Effect of gender and physical activity on internet addiction in medical students. Pak J Med Sci. (2017) 33:191–4. doi: 10.12669/pjms.331.11222
Keywords: mental health, problematic internet use, depressive symptoms, anxiety symptoms, stress, study-related characteristics, socio-demographic characteristics, relationships
Citation: Gavurova B, Ivankova V, Rigelsky M and Mudarri T (2022) Internet Addiction in Socio-Demographic, Academic, and Psychological Profile of College Students During the COVID-19 Pandemic in the Czech Republic and Slovakia. Front. Public Health 10:944085. doi: 10.3389/fpubh.2022.944085
Received: 14 May 2022; Accepted: 31 May 2022;
Published: 23 June 2022.
Edited by:
Li Wang, Institute of Psychology (CAS), ChinaReviewed by:
Zoltan Rozsa, Alexander Dubcek University in Trencin, SlovakiaJozef Zahumensky, Comenius University, Slovakia
Copyright © 2022 Gavurova, Ivankova, Rigelsky and Mudarri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Beata Gavurova, YmVhdGEuZ2F2dXJvdmEmI3gwMDA0MDtsZjEuY3VuaS5jeg==