Over the past decades, internet and smartphones have grown increasingly relied on throughout the world, and became an important part of modern life [1]. According to the statistics, in 2021, almost 4.6 billion people accessed internet [2].

Used properly, internet gives quick and easy access to information, entertainment and social contacts, and simplifies communication. However, this environment is becoming not only a space of opportunities, but also that of risks, including the risks of destructive and autodestructive behavior. Excessive and uncontrolled use of internet is associated with development of internet addiction and mental health problems [3]. Internet addiction is a behavioral problem that has gained greater scientific recognition over the past decade; some researchers call it "the 21st century epidemic" [4]. According to scientific data, Internet addiction is a non-chemical behavioral addiction stemming from human-machine (computer-internet) interaction with the following psychopathological symptoms: excessive use of internet associated with lack of control over screen time; neglect of work/study that degrades academic performance and productivity; irresistible obsessive desire to use internet; neglect of social life and search for social connections lacking in real life online [5–10].

Higher education students are particularly prone to developing behavior-modifying internet addiction. In medicine and healthcare, internet helps to practice evidence-based medicine, conduct research and training, access medical and online databases, treat patients in remote areas. The web is also used for academic and entertainment purposes. At the same time, limited or non-existent parental control, lifestyle of higher education students, use of internet in the context of studies (from preparations of projects to communication with peers and professors), its exam anxiety and stress relieve potential, as well as a primitive understanding of leisure time and lack w`of opportunities to realize intellectual and creative potential create risks of development of internet addiction.

This study aimed to profile psychophysiological characteristics of students at different levels of risk of developing behavior-modifying internet addiction.

METHODS

This was a cross-sectional study that involved 261 medical students of 5th and 6th years, 196 female and 65 male, conducted during the classroom studies period. To be included, participants had to sign the informed consent to examination. Chronic diseases and mental disorders were the exclusion criteria. The required sample size was not calculated in advance.

We used the Chen Internet Addiction Scale (CIAS) modified by K.A. Feklisov and V.L. Malygin [11] to assess the attitude of participants towards internet. The scale consists of 6 subscales that score compulsive symptoms (Com; obsessive desire to go online); withdrawal symptoms (Wit; peculiar to cessation of use of internet, associated with discomfort); tolerance symptoms (Tol; gauged by time online needed to achieve satisfaction); intrapersonal issues and health problems (IH); quality of time management (TM). Summed up, Com + Wit + Tol scores allow calculation of the integral (key) symptoms of internet addiction (IA-Sym), and IH + TM scores yield the internet addiction-associated problem indicator value (IA-Rp). The sum total of points scored on all CIAS subscales reflects the examinee's current status, which can be one of the following: 27 to 42 points — no internet addiction; 43 to 64 points — propensity to internet addiction/preaddictive stage; 65 points and above — diagnosed internet addiction.

Seeking to determine the specifics of the risk of internet addiction in group 1 (no internet addiction) and group 2 (prone to internet addiction), we analyzed the participants' mental and social health, and quality of life. The study did not include a control group of students suffering from internet addiction because they were too few.

To assess the participating students' mental health, we used the Buss-Durkee Hostility Inventory (1957) as standardized by A.A. Hwan, Yu.A. Zaitsev, Yu.A. Kuznetsova (2005).

To measure their anxiety, negative emotional experiences and cognitive activity in everyday and academic lives, we used the Spielberger State-Trait Anxiety Inventory as modified by A.D. Andreeva (1988). Their stress was gauged with the help of РSМ-25 (Psychological Stress Measure scale). The participants' social health was explored with the help of E.V. Tsikalyuk questionnaire [12], which includes 25 questions in five blocks: block A — social adaptation, block B — relationships with others, block C — social activity, block D — attitudes to social norms, block E — value orientations; the scores are used to calculate the social health coefficient (Csh) by the following formula:

C_sh = (2 • A + B – D – 2 • E)/25.

Furthermore, a score from 1.5 to 2 points means a high level of social health and a prosocial type of functioning; from 0.5 to 1.4 points corresponds to an average level of social health and a conformal type of functioning; from –0.4 to 0.4 points translates into a low level of social health and inert social functioning; from –1.4 to –0.5 points signals of poor social health, asociality; and score from –2 to –1.5 points alarms of social illness, antisociality. As for the quality of life, we evaluated it using the MOS-SF-36 questionnaire by J.E. Ware (1992), as modified by V.R. Kuchma, E.I. Shubochkina, E.G. Blinova et al. (2016). The resulting points could fall into one of three tiers: 100 to 70 points meant that the participant found the quality of life good, 70 to 50 — satisfactory, below 50 points — unsatisfactory.

For statistical data analysis employing parametric methods of medical statistics, we used StatTech v. 3.1.8 (StatTech; Russia). Kolmogorov-Smirnov test enabled verification of normalcy of distribution; the resulting data distributed normally, and were presented as arithmetic means (M) and arithmetic mean errors (m). Calculating the Student's t-test for independent samples, we compared selected means, and subsequently established the level of statistical significance (p). The differences were considered significant at p ≤ 0.05. To uncover the relationship between the studied psychophysiological indicators and internet addiction criteria, we applied the Pearson's chi-squared test (p) and established the determination coefficient (R).

We found only 1.5% of the participating students to have internet addiction, with 44.5% of the sample prone thereto (group 2) and 54.0% showing no signs thereof (group 1). Group 2 had 1.5-fold greater CIAS scores than group 1 (51.4 ± 1.16 vs. 34.7 ± 0.83 points, p ≤ 0.05). As for the internet addiction symptoms, the scores in group 2 were 1.6 times higher than in group 1 (30.7 ± 0.69 vs. 19.5 ± 0.61 points, p ≤ 0.05), and the former also scored 1.4 more points for problems related to internet addiction than the latter (20.7 ± 0.70 vs. 15.2 ± 0.43 points, p ≤ 0.05). Considering other indicators, in comparison to group 1, group 2 had the compulsive symptoms score 1.7 times higher, that describing withdrawal symptoms and tolerance — 1.5 times higher, and gained 1.3 times and 1.4 times more points for intrapersonal/health-related issues and time management problems, respectively (fig. 1).

Likely, maladaptive use of internet triggered aggressive behavior in group 1 (78.7% of participants), while students from group 1, on the in contrary, suppressed aggressive and hostile reactions (41.9%) (fig. 2).

Therefore, compared to group 1, group 2 exhibited 1.5-fold higher level of irritability, 1.4-fold higher level of resentment, 1.3-fold higher level of guilt and hostility, 1.2-fold higher level of verbal aggression (tab. 1).

Compared to group 1, group 2 had more students exhibiting high level of physical aggression (2.7 times more), high level of irritability (2.0 times more), high level of resentment (1.7 more), high level of guilt and indirect aggression (1.4 times more), and high level of verbal aggression (1.3 times more).

Moreover, compared to group 1, in group 2, we registered 15.1% higher level of anxiety in everyday life, 13.0% higher level of studies-related anxiety, 13.7% higher level of negative emotional experiences in everyday life, 13.3% higher level of studies-related negative emotional experiences (tab. 2).

In group 2, 22.6% of students had a high level of everyday life anxiety and 48.1% — studies-related anxiety. In group 1, these figures were 6.7% (high level of everyday life anxiety) and 26.7% (high level of studies-related anxiety), respectively.

As for everyday life negative emotional experiences, the shares of those that exhibited high level thereof were 37.0% in group 2 and 20.0% in group 1; studies-related negative experiences were reported to be high by 18.5% of group 2 participants and no one in group 1. These figures may explain why, compared to group 1, group 2 had 1.8 times less students exhibiting everyday life high cognitive activity, and 1.2 times less students highly active in their studies.

Considering aggression, anxiety and negative emotional experiences, group 2 scored 1.3 times more stress points than group 1 (79.7 ± 6.32 vs. 62.5 ± 4.84 points, p ≤ 0.05). From the viewpoint of the level of stress, 60.4% of group 2 students and 79.3% of group 1 students had low level thereof, 22.2% of group 2 students and 20.7% of group 1 students — moderate level, and as for high level of stress, it was established in 17.4% of group 2 students and no one in group 1.

One of the typical negative consequences of internet addiction is social isolation, deteriorating social functioning [4–7]. The scores reflecting social health did not differ significantly between the two groups: 0.8 ± 0.09 in group 2 and 1.0 ± 0.05 in group 1 (p ≥ 0.05). This makes the overall social health of the sample average, with social functioning conformal, characterized by latent rejection of the social environment; it is also likely that the students' behavior tends to change under pressure exerted by their social group. However, despite most participants being average in terms of social health (77.8% of group 2 and 89.5% of group 1), only 5.6% and 10.5% of students of groups 2 and 1, respectively, had social functioning at a high level, i.e., capable of adapting easily in 0social environments, while 16.7% of group 2 students registered poor social health, indicating the risk of social maladjustment and social passivity of students.

The resulting objective data that describe the state of mental and social health of students are reflected in their subjective assessments of own health and quality of life in general. It was found that the quality of life indicators recorded in group 2 were significantly inferior to those registered in group 1: on the pain intensity scale — by 13.0%, vitality scale — by 16.2%, social functioning scale — by 16.3%, role-based emotional functioning scale — by 33.0%, mental health scale — by 15.0%, integral psychological health scale — by 19.7% (tab. 3).

A noteworthy fact is the two-fold difference in the number of students in groups 1 and 2 that considered their psychological health as unsatisfactory: 37.5% and 78.6%, respectively.

Analysis of the data given in tab. 4 reveals a significant moderate correlation between compulsivity and irritability score (r = 0.68 ± 0.097) and scores reflecting resentment (r = 0.67 ± 0.097), guilt (r = 0.63 ± 0.102), verbal aggression (r = 0.67 ± 0.098). Withdrawal symptoms scores correlated with those describing stress (r = 0.52 ± 0.112), vitality (r = –0.61 ± 0.104), role-playing emotional functioning (r = –0.61 ± 0.104), mental health (r = –0.60 ± 0.105), psychological component of health (r= –0.66 ± 0.098). We identified a moderate correlation between the tolerance irritability scores (r = 0.66 ± 0.099), guilt (r = 0.60 ± 0.105), resentment (r = 0.70 ± 0.094), hostility (r = 0.68 ± 0.096), anxiety (r = 0.57 ± 0.116) and negative emotional experiences (r = 0.62 ± 0.103), stress (r = 0.62 ± 0.103), social health (r = –0.61 ± 0.104), vitality (r = –0.64 ± 0.101) and psychological component of health (r = –0.62 ± 0.103). The scores reflecting intrapersonal problems correlated with everyday life (r = 0.62 ± 0.103) and studies-related anxiety (r = 0.67 ± 0.103), stress (r = 0.63 ± 0.102), social health (r = –0.65 ± 0.101), social functioning (r = –0.67 ± 0.098), role-based emotional functioning (r = –0.65 ± 0.100), mental health (r = –0.66 ± 0.099) and psychological component of health (r = –0.60 ± 0.105). Time management values correlated with social health (r = –0.65 ± 0.101), vitality (r = –0.68 ± 0.096), social functioning (r = –0.67 ± 0.098), role-based emotional functioning (r = –0.67 ± 0.097), mental health (r = –0.67 ± 0.097) and psychological component of health (r = -0.73 ± 0.090).

DISCUSSION

Multiple studies indicate that internet addiction has many negative consequences [6– 10, 13–24]. The key health risks associated with maladaptive use of internet are eye strain (computer visual syndrome) and stress of the musculoskeletal system (pain in the neck, back, hands), involuntary rejection of the most important healthy lifestyle components (proper diet, physical activity, outdoor walks, sleep, leisure activities) [6–10, 13–21]. In addition, escape from the real life complicates interpersonal relationships, entails loss of friends, problems in family functions, and leads to social maladaptation of students [6–10]. The desire to spend more and more time online, neglecting educational activities, and an obsessive wish to use internet become the main reasons behind loss of interest in everyday life and studies, as well as poor academic performance [6–10]. Ultimately, excessive use of internet translates into mental health problems such as stress, anxiety, depression, and social dysfunction [22–24].

This study found that aptitude to internet addiction in group 2 was characterized by the development of compulsive symptoms, which were 1.7 times more intense than in group 1; withdrawal symptoms and tolerance, which were 1.5 times stronger; intrapersonal issues and health-related problems, which were by 1.3 more severe; and time management problems, which were 1.4 times more complicated. Against the background of development of symptoms of internet addiction, more than half of the students of group 2 registered a high level of irritation (89.3%), verbal aggression (60.7%), feeling of guilt (75.0%); 22.0 to 48.1% of the participants had a high level of anxiety, 18.6% to 37.0% — experienced negative emotional experiences. High level of stress was registered in 17.4% of the students, 16.7% of them suffered social health deterioration, and for 78.6%, psychological component of the quality of life was declining.

Currently, there are only rudimentary internet addiction prevention efforts realized in Russia, especially concerning hygienic training and education. Young people are not aware of the basics of internet addiction prevention, and parents and teachers do not focus on the development of safe internet use skills in young people. In addition, the data from research activities and statistics have not been aggregated, i.e., it is impossible to assess the extent of internet addiction and the level of its severity among young people, which would allow identification of prevention priorities. The primary factor complicating the assessment of prevalence of internet addiction is lack of a unified classification of its types and degrees.

As a result, the data collected in studies are very contradictory. For example, study [25] concludes that 2.3% of medical students show signs of internet addiction, and 13.9% of the sample had more serious respective problems. Another study [26] stated that 8.2% of the participating medical students had a pronounced and stableform of internet addiction.

A study that involved medical students from Minsk has shown 62.5% of them to have internet addiction of low degree, 30.4% of average degree, and 4.4% of high degree [27]. In Moscow, internet addiction was detected in 9.2% of the participating medical students, and 28.65% used internet excessively. In the Udmurt Republic, internet addiction was diagnosed in 1.7% of the invited higher eduction students, signs thereof (average level internet addiction) in 25.7% of students, and 73.7% of those that took part in this study were announced to have no addiction [27]. Different internet addiction prevalence figures indicate that registration and evaluation of this condition, and the respective criteria, are still a problem, which points to the need to systematize the said criteria and use a unified scientifically based methodology when diagnosing internet addiction.

CONCLUSIONS

Our results indicate that aptitude for internet addiction is associated with negative changes in mental and social health and quality of life of students. They suggest the need for internet addiction screenings among higher education students and vocational school students, such screenings allowing to identify both at-risk students and internet-addicted students, and ensure timely preventive measures aimed at correcting psychological and social factors influencing development of addictive behaviors.