Problematic Internet Use and Health Behaviors in Young Men:  Potential Links with Neural Reward Pathways

Problematic Internet Use and Health Behaviors in Young Men: Potential Links with Neural Reward Pathways | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Problematic Internet Use and Health Behaviors in Young Men: Potential Links with Neural Reward Pathways Marta Kożybska, Aleksandra Rył, Natalia Tomska, Grzegorz Raczyński, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7620010/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Problematic Internet Use (PIU) negatively impacts psychological, social, academic, and occupational functioning and is associated with unhealthy behaviors such as poor diet, inactivity, and sleep problems. The post-pandemic period has intensified Internet overuse, especially among young men, who are more prone to risky health behaviors. This study aimed to assess the relationship between health behaviors, PIU, and serotonin and dopamine levels in young men. Methods: A cross-sectional study was conducted among 325 men aged 18–30 from the West Pomeranian region of Poland. PIU severity was measured using the Internet Use Test (IUT), and health behaviors were assessed using the Health-Related Behavior Inventory (HBI). Blood samples were analyzed for serotonin and dopamine levels using ELISA. Results: 17.7% of participants exhibited high or very high PIU. One-third led unhealthy lifestyles. Higher PIU correlated with lower health behavior scores. The overall HBI score was negatively associated with non-work Internet use during weekdays and weekends. In the low PIU group, healthy eating habits positively correlated with dopamine levels. The linear correlation analysis, after adjusting for age and BMI, revealed a correlation between IUT scores and overall HBI scores, HBI healthy eating habits, and HBI positive mental attitudes. Conclusions: Increased Internet use was linked to poorer health behaviors. These findings underscore the need for interventions aimed at reducing Internet time and promoting healthy behaviors among young men with PIU. Further research should explore neurobiological mechanisms behind these associations. Health sciences/Diseases Health sciences/Health care Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors Problematic Internet Use health behaviors dopamine serotonin young men addiction lifestyle mental health 1. Introduction Problematic Internet Use (PIU) is defined as internet use that results in psychological, social, academic, and/or occupational difficulties in an individual's life 1 . It is frequently associated with prolonged online activity 2,3 , which can significantly influence lifestyle and lead to changes in health-related behaviors. Excessive screen time often occurs at the expense of physical activity, adequate sleep, or the preparation of healthy meals. The post-pandemic era has seen the intesified shift of daily activities such as work and education to online platforms. This transition is accompanied by a rising prevalence of Internet addiction 4,5 and a concurrent decline in health-promoting behaviors among young adults 6–8 . The issue is particularly pronounced among men, who, for years, have demonstrated poorer health behaviors compared to women, including shorter life expectancies 9 , higher rates of drug use, and greater engagement in risky sexual behaviors 10 . These disparities have deepened in recent years. During the COVID-19 pandemic, men reported greater declines in overall health 11 and health behaviors, such as physical activity 6 , relative to women. Although some studies report differing findings 12–14 , a substantial body of evidence indicates that men are at a higher risk for PIU compared to women 3,13,15–25 . This increased vulnerability may be partly explained by men’s higher engagement in online gaming 26 , a well-established predictor of Internet addiction 27 . The literature underscores the association between PIU and less healthy lifestyles. Research demonstrates that PIU correlates with both underweight and overweight conditions 28–30 , poor dietary habits, and addictive eating behaviors 31 . Individuals with PIU are more likely to consume fast food 32 . Kim et al. observed that high-risk Internet users often exhibit poor diet quality. These individuals frequently experience appetite suppression, skip meals, eat irregularly, and rely on snacking, resulting in imbalanced nutritional intake 33 . Previous studies have also identified an association between Internet addiction and low levels of physical activity 31,33–37 . Conversely, engaging in physical activity has been shown to prevent and even mitigate the severity of Internet addiction 38–40 . Unlike Internet use, physical activity offers opportunities to engage with the real world, promoting both physical fitness and psychological well-being 41 . Zhihao et al. proposed that physical activity may reduce the prevalence of Internet addiction by enhancing self-esteem 41 . Numerous studies underscore the association between PIU and adverse psychological factors, including loneliness 42 , symptoms of depression, family conflict 29 , social anxiety 43–46 , and positive screening results for post-traumatic stress disorder (PTSD) 28 . Additionally, Internet addiction has been linked to heightened stress levels and a decline in overall well-being 47 . Individuals exhibiting PIU frequently report insufficient sleep duration and a higher prevalence of sleep disorders 29,34,48–51 . Moreover, PIU correlates with other detrimental health behaviors, such as neglecting dental checkups, substance abuse (including illegal drugs, alcohol, and tobacco), and gambling 28,33,37 . Physical symptoms such as migraines and back pain are also more commonly reported among individuals with PIU compared to those without problematic Internet habits 29 . Adhering to fundamental principles of a healthy lifestyle – such as maintaining a balanced diet, engaging in regular physical activity, and ensuring adequate sleep – is crucial for maintaining balanced levels of neurotransmitters like dopamine and serotonin. Recent research has highlighted the significance of these neurotransmitters in the development of behavioral addictions. Dopamine, often referred to as the "reward neurotransmitter", is released during pleasurable activities, reinforcing behaviors that provide satisfaction. Internet use is one such activity that activates the brain’s reward system. However, excessive and prolonged stimulation of this system can result in reduced dopamine D2 receptor expression and decreased receptor sensitivity, necessitating increasingly intense stimuli to achieve similar levels of satisfaction 52,53 . Dopamine plays a pivotal role in addiction, including behavioral addictions. Studies have identified differences in dopamine levels between adolescents with Internet addiction and control groups 54 . Liu and Luo suggested that Internet addiction could result in neurobiological damage, with neuroimaging studies revealing dysfunctions in the brain’s dopaminergic systems 55 . Serotonin, another critical hormone regulating human behavior, is involved in processes related to motivation and desires 52 . Research indicates that individuals with Internet addiction often exhibit dysfunctions in the serotonergic system. A higher frequency of the homozygous short allelic variant of the serotonin transporter gene (SS-5HTTLPR) has been observed in individuals with excessive Internet use 56 . Dai et al. identified the T allele of rs6313 in the serotonin 2A receptor as a risk factor for Internet addiction disorder (IAD) 57 . Additionally, Annunzi et al. reported differential epigenetic regulation of the serotonin reuptake transporter gene among university students, depending on the severity of their Internet addiction 58 . Yaunin et al. further identified significant differences in peripheral blood levels of dopamine transporter (DAT) and serotonin transporter (SERT/5-HTT) between stressed students with Internet addiction and their stressed, non-addicted counterparts [58]. Current evidence indicates that young men tend to lead less healthy lifestyles compared to women and are at a higher risk of developing PIU in many populations. PIU is associated with various detrimental health behaviors, suggesting that excessive Internet use may exacerbate the negative health behaviors commonly observed in young men. This phenomenon could significantly contribute to poorer health outcomes for this demographic in the coming decades, posing a notable public health challenge. The aim of this study was to examine the relationship between health behaviors and PIU among young men, with a particular focus on serotonin and dopamine levels. Based on the theoretical framework presented, the following research hypotheses were formulated: The risk of PIU increases with the amount of time spent online. As time spent online increases, overall health behaviors, as well as specific aspects such as healthy eating habits, preventive behaviors, positive mental attitude, and health practices, deteriorate. Men with more severe PIU symptoms are more likely to exhibit unhealthy lifestyles, both overall and in specific areas, including healthy eating habits, preventive behaviors, positive mental attitude, and health practices. Dopamine and serotonin levels vary among young men depending on the severity of PIU symptoms and the intensity of their health behaviors. 2. Methods 2.1. Study procedure The cross-sectional study was conducted in the West Pomeranian region of Poland. In accordance with the study objectives, the inclusion criteria were male gender, age between 18 and 30 years, Internet usage, and a declared willingness to complete the questionnaire. The exclusion criterion was being under the care of a psychiatrist or psychologist. Participants were recruited via online forums, advertisements at healthcare centers, and social media. The questionnaires were completed using the paper-and-pencil method, and no remuneration was provided. Each participant was informed about the purpose of the study, the possibility of withdrawal at any stage, and provided written informed consent to participate. The study was approved by the Bioethics Committee of the Pomeranian Medical University in Szczecin (KB-0012/90/18). 2.2. Participants The study included 325 young men, with a mean age of 25.1 years (SD = 3.7). The participants were predominantly students, with more than half studying medical or health sciences. Most participants lived in urban areas, while 9% resided in rural areas. The average BMI of the participants was 25.5 (SD = 4.2). The differences in the overall IUT scores between the PIU risk groups were statistically significant (p = 0.006). Table 1. Characteristics of the study group, IUT and HBI results, dopamine and serotonin levels. Variables M SD Age 25.1 3.7 BMI 25.5 4.2 Dopamine level (ng/ml) 84.3 43.1 Serotonin level (ng/ml) 124.7 118.1 Variables n % Student status Student 227 69.6 Non-student 99 30.4 Residential area Rural Town 29 296 9.0 91.0 Self-assessment of financial situation very good sufficient poor 141 176 3 44.0 55.0 1.0 Average daily number of hours spent using the Internet on weekdays: for study or work purposes for purposes other than study or work 4 3 3 3 Average daily number of hours spent using the Internet on weekends: for study or work purposes for purposes other than study or work 2 4 2 3 IUT score 19.6 17.7 PIU category low PIU moderate PIU high and very high PIU 90 178 57 27.7 54.8 17.5 HBI score HBI overall HBI healthy eating habits HBI preventive behaviours HBI positive mental attitude HBI health practices 78.2 19.3 19.1 21.1 19.1 12.8 4.9 4.4 4.0 4.0 HBI category low moderate high 106 133 86 32.6 40.9 26.5 BMI, Body Mass Index; IUT, Internet Use Test; PIU, Problematic Internet Use; HBI, Health-Related Behaviour Inventory 2.3. Measures 2.3.1. Internet Use Test (IUT) To assess the severity of symptoms associated with problematic Internet use, the Internet Use Test (IUT), developed by Ryszard Poprawa from the Institute of Psychology at the University of Wrocław, Poland, was employed. The test includes questions addressing the psychological, social, and health consequences of Internet use. It consists of 23 items rated on a scale from 0 ("never") to 5 ("always"). The raw score ranges from 0 to 115, with higher scores indicating greater severity of problematic Internet use symptoms. Based on symptom severity, four groups can be distinguished: (1) Low Problematic Internet Use (0–5 points); (2) Moderate Problematic Internet Use (6–35 points); (3) High Problematic Internet Use (36–72 points); (4) Very High Problematic Internet Use (73–115 points) 59 . 2.3.2. Health-Related Behavior Inventory (HBI) To uniformly and comprehensively measure health behaviors while considering the socioeconomic conditions of the studied population, the standardized Polish tool Health-Related Behavior Inventory (HBI) by Zygfryd Juczyński was selected 60 . The scale comprises 24 questions about health behaviors. Respondents rate their answers on a Likert scale ranging from 1 ("almost never") to 5 ("almost always"). Higher scores indicate a lifestyle more conducive to health. In addition to the nominal score, the tool allows categorization into three levels of health behaviors: (1) Low: 24–71 for men, 24–77 for women; (2) Moderate: 72–86 for men, 78–91 for women; (3) High: 87–120 for men, 92–120 for women. The tool also distinguishes four categories of health behaviors: 1. Healthy eating habits (e.g., questions about consuming fruits and vegetables, whole-grain bread, and limiting intake of animal fats, sugar, salt, and preservatives). 2. Preventive behaviors (e.g. includes avoiding illnesses, following medical advice, regular medical check-ups, and seeking health-related information). 3. Positive mental attitudes (e.g. includes avoiding depressive situations, stress, and tension, maintaining friendships, having a stable family life, avoiding negative emotions, and thinking positively). 4. Health practices (e.g. includes leading a healthy lifestyle with adequate rest, sleep, weight control, avoiding smoking, and refraining from excessive exercise). The Cronbach’s alpha for the entire scale is 0.85, and for the four subscales, it ranges from 0.60 to 0.65 60 . 2.3.3. Custom questionnaire on sociodemographic data and Internet usage characteristics The questionnaire included questions about age, body weight, height, student status, place of residence (city/village), self-assessment of financial situation (very good/sufficient/poor), and the average number of hours spent daily using the Internet: 1. From Monday to Friday: a. For study or work purposes. b. For purposes other than study or work. 2. On weekends: a. For study or work purposes. b. For purposes other than study or work. 2.3.4. Determination of dopamine and serotonin levels Blood samples were collected from the ulnar vein of fasting participants between 7:00 and 10:00 a.m. The blood was drawn into 9 mL coagulation tubes for the analysis of hormonal parameters. The samples were centrifuged, and the serum was stored at -20°C for further testing. Serum concentrations of 5-HT (serotonin) and DA (dopamine) were determined using the ELISA method 61 . The analyses were performed at the laboratory of the Pomeranian Medical University in Szczecin. The ELISA procedure involved the following steps. Empty wells: No samples or horseradish peroxidase (HRP) were added. Standard wells: 50 μL of standard solution was added to the designated wells. Specimen wells: 40 μL of diluent followed by 10 μL of the specimen was added to each well. HRP addition: A total of 50 μL of HRP was added to all wells except the blank well. The plates were gently vortexed and incubated at 37°C for 60 minutes. Excess fluid was discarded, and each well was rinsed with diluted cleaning fluid, mixed for 30 seconds, and the fluid was decanted. This washing process was repeated five times, and the wells were blotted dry. The reaction was stopped by adding 50 μL of stop solution to each well. Optical density (OD) measurements were taken at a wavelength of 450 nm. The blank well was set as the baseline, and the standard curve was generated using the OD values of the standards. Linear regression was used to calculate sample concentrations from their respective OD values. 2.4. Statistical analysis For statistical analysis, the groups of participants with High and Very High Problematic Internet Use were combined. The analysis was performed using Statistica 13.1 (StatSoft Poland, Krakow, Poland). As the distribution of continuous variables deviated from normality, the data are presented as medians and interquartile ranges. Statistical inference was conducted using the Mann–Whitney U test or the Kruskal–Wallis test. For logistic regression analysis, participants were grouped into two categories: men with low levels of Internet addiction and men with moderate to high levels of Internet addiction. The significance level was set at p < 0.05. 3. Results 3.1. Characteristics of Internet use in the study group As shown in Table 1, participants spent an average of 3 hours daily using the Internet for purposes other than work or study on weekdays and 4 hours daily on weekends. The mean IUT score was 19.6 points. Analysis of the categorical IUT results revealed that the majority of participants (54.8%) were classified as having moderate PIU. Comparable percentages of participants were found in the low PIU group (27.7%) and the high/very high PIU group (17.5%). Differences between groups were statistically significant (p = 0.006). Table 2 presents the average amount of time spent online by the study group, categorized by PIU risk levels. Post-hoc analysis revealed statistically significant differences between the groups in the time spent online for purposes other than study or work (both on weekdays and weekends), while no significant differences were found for time spent on study or work. Table 2. Number of hours spent online in PIU risk groups Variables Low PIU, n=90 Moderate PIU, n=178 High and very high PIU, n=57 p X Me Min Max SD X Me Min Max SD X Me Min Max SD Internet hours for study during the week 3.28 2.00 0.00 12.00 2.95 4.01 3.00 0.00 15.00 3.12 4.01 4.00 1.00 10.00 2.65 0.06 Internet hours for other activities during the week 8.24 2.00 0.00 101.00 22.99 4.23 2.00 0.00 101.00 10.09 5.70 4.00 0.50 101.00 13.28 0.0007 Internet hours for study on weekends 6.78 2.00 0.00 101.00 20.79 2.88 2.00 0.00 101.00 7.88 2.66 2.00 0.00 11.00 2.59 0.91 Internet hours for other activities on weekends 3.20 3.00 0.00 12.00 2.16 4.32 4.00 0.00 12.00 2.34 5.80 5.50 1.00 15.00 2.93 0.000 PIU, Problematic Internet Use 3.2. Health behaviors in the study group and Internet use Participants scored an average of 78.2 out of 120 possible points on the HBI (Table 1). One-third of participants, specifically 32.6% (106 individuals), demonstrated low levels of health behaviors (Table 1). Participants scored the highest in the positive mental attitudes category (mean 21.1) and the lowest in preventive behaviors and health practices categories (mean 19.1 for both) (Table 1). Table 3 shows the level of health behaviors across the PIU risk groups. Among individuals with low PIU (n=90), the highest proportion (11.1%, corresponding to 36 individuals) had a high level of health behaviors. Conversely, among those with high or very high PIU (n=57), the highest proportion (7.69%, corresponding to 25 individuals) had a low level of health behaviors. Table 3. HBI category in PIU risk groups HBI category Low PIU, n=90 Moderate PIU, n=178 High and very high PIU, n=57 low HBI, n=106 Number % of total 21 6% 60 18% 25 8% moderate HBI, n=133 Number % of total 33 10% 76 23% 24 7% high HBI, n=86 Number % of total 36 11% 42 13% 8 2% HBI, Health-Related Behaviour Inventory Analyzing correlations between health behaviors and the severity of problematic Internet use revealed that both the overall HBI score and all categories except preventive behaviors showed statistically significant negative correlations with IUT scores. However, these correlations were weak (Table 4). Table 4. Correlation analysis between the IUT score and the HBI score Health behaviors (HBI score) Problematic Internet Use (IUT score) R p HBI overall -0.217 0.000 HBI healthy eating habits -0.186 0.001 HBI preventive behaviours -0.074 0.187 HBI positive mental attitude -0.271 0.000 HBI health practices -0.112 0.045 IUT, Internet Use Test;HBI, Health-Related Behaviour Inventory Similar analyses conducted within each PIU risk group (low, moderate, high, and very high) showed that the negative correlation between positive mental attitudes and IUT scores in the moderate PIU group was at the threshold of statistical significance but did not meet the adopted criterion (p = 0.050) (Table 5). Table 5. Correlation analysis between the IUT score and health behaviors in PIU risk groups Pair of variables Low PIU, n=90 Moderate PIU, n=178 High and very high PIU, n=57 R p R p R p HBI overall IUT 3 score -0,084 0,434 -0,070 0,353 0,000 0,998 HBI healthy eating habits -0,132 0,219 -0,041 0,590 -0,037 0,786 HBI preventive behaviours 0,017 0,878 0,019 0,801 0,098 0,467 HBI positive mental attitude -0,090 0,405 -0,148 0,050 -0,052 0,701 HBI health practices -0,088 0,411 -0,008 0,914 0,032 0,814 PIU, Problematic Internet Use; HBI, Health-Related Behaviour Inventory;IUT, Internet Use Test Although the PIU risk groups did not consistently differ in all aspects of time spent online (Table 2), notable differences were observed in the correlations between online time and HBI scores across the groups (Table 6). Specifically, in the high/very high PIU group, the overall HBI score was negatively correlated with daily Internet use for purposes other than work or study, both on weekdays (R=−0.385,p=0.004) and weekends (R=−0.360,p=0.008). Additionally, negative correlations were observed between positive mental attitudes and time spent on the Internet for non-work/study purposes. These correlations were strongest in the high/very high PIU group, both on weekdays (R=−0.412,p=0.002) and weekends (R=−0.347,p=0.011), though still moderate. Negative correlations were also identified between daily Internet use for purposes other than work or study on weekdays and healthy eating habits (in the moderate PIU group: R=−0.188,p=0.014; in the high/very high PIU group: R=−0.380,p=0.004) and preventive behaviors (in the moderate PIU group: R=−0.189,p=0.014; in the high/very high PIU group: R=−0.309,p=0.021). All these significant correlations presented as moderate (Table 6). Table 6. Correlation analysis between time spent online and health behaviors according to PIU risk groups Pair of variables Low PIU, n=90 Moderate PIU, n=178 High and very high PIU, n=57 R p R p R p HBI overall daily internet time on weekdays for study/work -0.012 0.911 -0.077 0.322 -0.126 0.364 daily internet time on weekdays for other purposes -0.101 0.351 -0.168 0.027 -0.385 0.004 daily internet time on weekend for studying/working 0.085 0.432 0.062 0.425 0.051 0.717 daily internet time on weekend for other purposes -0.163 0.144 -0.156 0.043 -0.360 0.008 HBI healthy eating habits daily internet time on weekdays for study/work 0.058 0.601 0.010 0.900 -0.098 0.481 daily internet time on weekdays for other purposes -0.167 0.124 -0.188 0.014 -0.380 0.004 daily internet time on weekend for studying/working 0.068 0.533 0.086 0.272 0.193 0.162 daily internet time on weekend for other purposes -0.314 0.005 -0.203 0.009 -0.329 0.015 HBI preventive behaviours daily internet time on weekdays for study/work 0.026 0.817 -0.057 0.464 -0.073 0.599 daily internet time on weekdays for other purposes -0.100 0.364 -0.189 0.014 -0.309 0.021 daily internet time on weekend for studying/working 0.157 0.153 0.127 0.104 0.001 0.995 daily internet time on weekend for other purposes -0.133 0.242 -0.150 0.055 -0.268 0.051 HBI positive mental attitude daily internet time on weekdays for study/work -0.118 0.289 -0.030 0.697 -0.131 0.351 daily internet time on weekdays for other purposes -0.115 0.293 -0.063 0.413 -0.412 0.002 daily internet time on weekend for studying/working 0.089 0.415 0.078 0.316 -0.098 0.485 daily internet time on weekend for other purposes -0.116 0.308 -0.075 0.335 -0.347 0.011 HBI health practices daily internet time on weekdays for study/work -0.083 0.452 -0.117 0.134 -0.045 0.753 daily internet time on weekdays for other purposes 0.143 0.186 -0.016 0.835 0.097 0.491 daily internet time on weekend for studying/working -0.055 0.615 -0.082 0.293 0.086 0.544 daily internet time on weekend for other purposes 0.124 0.271 -0.018 0.815 0.073 0.609 PIU, Problematic Internet Use;HBI, Health-Related Behaviour Inventory 3.3. Dopamine and serotonin levels and health behaviors Table 7 presents correlations between HBI scores and dopamine and serotonin levels within PIU risk groups. In the low PIU group, a statistically significant, moderate positive correlation was observed between HBI healthy eating habits scores and dopamine levels. Other relationships were not statistically significant. Table 7. Correlation analysis between HBI score and dopamine and serotonin levels in PIU risk groups Pair of variables Low PIU, n=90 Moderate PIU, n=178 High and very high PIU, n=57 R p R p R p HBI overall serotonin level -0,070 0,584 0,063 0,484 0,088 0,568 HBI healthy eating habits -0,033 0,801 -0,054 0,556 -0,113 0,465 HBI preventive behaviours -0,231 0,078 0,141 0,119 0,033 0,832 HBI positive mental attitude 0,020 0,877 0,056 0,540 0,103 0,512 HBI health practices -0,071 0,584 0,053 0,557 0,210 0,183 HBI total score dopamine level 0,188 0,192 -0,027 0,780 0,207 0,219 HBI healthy eating habits 0,350 0,014 0,046 0,640 0,037 0,827 HBI preventive behaviours 0,215 0,142 -0,110 0,266 0,085 0,619 HBI positive mental attitude 0,146 0,321 -0,029 0,766 0,190 0,266 HBI health practices 0,029 0,842 -0,063 0,522 0,161 0,356 PIU, Problematic Internet Use;HBI, Health-Related Behaviour Inventory Table 8 shows the results of linear correlation analysis between health behaviors, serotonin and dopamine levels, and IUT scores after adjusting for age and BMI. Statistically significant but weak negative correlations were observed between IUT scores and HBI healthy eating habits , HBI positive mental attitudes , and overall HBI scores. While dopamine levels were not correlated with IUT scores (p=0.997), a statistically significant, albeit weak, correlation was observed between serotonin levels and IUT scores (p=0.020) (Table 8). Table 8. Linear correlation analysis between health behaviors, serotonin and dopamine levels, and IUT score (adjusted for age and BMI) IUT 1 score -95,00% 95,00% IUT score p Gr.ufn. Gr.ufn. Beta (ß) HBI 2 overall 0.000 -0.427 -0.119 -0.1955 HBI healthy eating habits 0.004 -6.087 -1.156 -0.1644 HBI preventive behaviours 0.215 -4.556 1.032 -0.0715 HBI positive mental attitude 0.000 -9.934 -4.120 -0.2628 HBI health practices 0.126 -5.398 0.672 -0.0878 serotonin level 0.020 0.0039 0.047 0.1560 dopamine level 0.997 -0.0611 0.061 0.0002 IUT, Internet Use Test; HBI, Health-Related Behaviour Inventory 4. Discussion 4.1. Prevalence of PIU and the relationship between time spent online and PIU risk (first hypothesis) The results indicate that 17.7% of participants had high or very high risk of developing problematic Internet use (PIU). By comparison, a systematic review conducted before the pandemic (2003–2018) 62 reported a prevalence of generalized Internet addiction (GIA) at 7.02% (95% CI: 6.09%–8.08%). Among countries with geographical proximity to Poland, prevalence rates varied: Germany, 0.5%–15.7% 63,64 ; Lithuania, 18.3% 65 ; Hungary, 1.6% 66 ; Slovenia, 3.1%–5.8% 66,67 ; and Romania, 4.6%–8.7% 66,68 . In Poland, studies conducted before the pandemic found a PIU prevalence of 10.2% 26 . During the COVID-19 pandemic, a meta-analysis revealed a pooled prevalence rate of 25% for PIU; however, applying a stricter threshold reduced the prevalence to 7.9% 69 . Even within geographically similar regions, prevalence rates varied widely. For instance, in Poland, 7.8% of individuals were identified as problematic Internet users 70 , compared to 21.3% in Switzerland 71 and only 3.9%–5.2% in Hungary 22,23 . This variability is a recurring phenomenon in PIU and Internet addiction research. It arises from differences in terminology (e.g., PIU, Internet addiction, excessive Internet use), the use of diverse measurement tools and criteria, cultural differences 69 , and variations in Internet access and usage patterns. Analyzing differences in PIU prevalence is crucial for a better understanding of the phenomenon. Several factors may explain why the prevalence rates in our study are higher than those observed in other populations. First, our study exclusively included men, who, according to numerous studies, are more susceptible to PIU and often score higher on PIU scales compared to women 3,13,15–25,72 . Second, the study was conducted during 2020–2021, a period characterized by a rapid shift of various activities from the real world to the online sphere due to the COVID-19 pandemic. Third, the participants were young adults and students – a demographic that, according to many sources, is particularly vulnerable to PIU 3,12,24,73,74 . This vulnerability may stem from both the necessity of using online tools for academic purposes and greater autonomy in managing their time. Interestingly, in our study, the average time spent online for study or work purposes was not significantly associated with membership in any of the PIU risk groups. However, the time spent online for other, non-study/work purposes was significantly different across PIU risk groups. Therefore, the first hypothesis – that the risk of PIU increases with the amount of time spent online – must be partially rejected, as the type of online activity appears to be a more critical factor than overall duration. This finding aligns with previous research suggesting that the development of addictive Internet use is determined not by the duration of use but by the nature of online activities. Fernández-Villa et al. found that men spend less time online per week compared to women but engage more in activities deemed highly addictive, such as recreational activities (e.g., gaming and shopping), rather than social purposes 29 . This suggests that the type of activity is key in the context of PIU development. A 2023 systematic review highlighted that online gaming, in particular, is a strong predictor of Internet addiction 27 . 4.2. Health behaviors and their relationship with PIU (second hypothesis) and time spent online (third hypothesis) Our study revealed that one-third of participants exhibited a low level of health-promoting behaviors as measured by the HBI questionnaire. This high percentage of young individuals leading unhealthy lifestyles is concerning, as it increases their future risk of developing diseases associated with poor habits. Participants scored the highest in the positive mental attitudes category and the lowest in preventive behaviors and health practices categories. In the low PIU group, the largest proportion of participants had a high level of health behaviors. Conversely, among those with high or very high PIU, the majority had a low level of health behaviors. Both the overall HBI score and all its categories (except preventive behaviors ) were negatively and significantly correlated with IUT scores, although the correlations were weak. Thus, the hypothesis that men with more severe PIU symptoms are more likely to lead unhealthy lifestyles overall – and in areas such as healthy eating habits , positive mental attitudes , and health practices – is supported. In the group of users with high or very high PIU, the amount of time spent online was significantly associated with health behaviors. Users who spent less time on recreational Internet use (activities unrelated to work or study) during weekdays exhibited greater levels of health-promoting behaviors overall, as well as in the categories of positive mental attitudes , healthy eating habits , and preventive behaviors . Regarding Internet use on weekends, spending more time online for non-work/study purposes was associated with lower levels of overall health behaviors and positive mental attitudes . However, the time spent online for work or study purposes, whether on weekdays or weekends, was not associated with health behaviors. Thus, the second hypothesis – that increasing time spent online leads to a decrease in health behaviors overall and in the areas of healthy eating habits , preventive behaviors , positive mental attitudes , and health practices – was partially confirmed. This suggests that while the amount of time spent online may not directly cause addictive Internet use, it plays a crucial role in shaping one’s lifestyle. The relationship between PIU and unhealthy lifestyles has also been confirmed in other studies. Bener and colleagues analyzed data from 3,000 students aged 12–25, 72% of whom were boys and men. They found that compared to girls and women, boys and men showed stronger PIU symptoms and depression and were more likely to consume fast food. Those without PIU were more likely to engage in mild or moderate physical activity than those with PIU and had more hours of sleep 75 . Similarly, Atilla et al. studied a comparable age group (15–24 years) with an overrepresentation of women (84%). Their findings also demonstrated a link between unhealthy diets and low levels of physical activity with Internet addiction. This study is particularly notable as, like the present research, health behaviors were measured using a standardized tool (Nutrition Exercise Behavior Scale) 31 . The Family Nutrition and Physical Activity Tool has been used in the context of PIU in younger age groups (7–10 years). It demonstrated associations between Internet addiction and behaviors such as eating while using the Internet, excessive body weight, the overall score on the Family Nutrition and Physical Activity Tool (albeit with a very weak correlation), and sleep disturbances. These findings suggest that PIU among children is linked to family environments, practices, and behaviors that may contribute to the development of obesity in the future 76 . In their research, Tóth et al. examined lifestyle elements including adherence to WHO physical activity guidelines, sleep duration, self-preparation of meals, skipping breakfast, eating snacks or beverages while engaging in online learning, and smoking. They found that among individuals spending more hours on online learning, a smaller percentage skipped breakfast. However, longer online learning hours were associated with shorter and poorer-quality sleep, as well as lower quality of life in terms of mental health. These results differ from those of the present study, where time spent online for work or study purposes was not linked to health behaviors. It is worth noting that in Tóth et al.’s research, women constituted 70% of the sample, which may explain the observed differences 77 . Fernández-Villa et al., in a Spanish study of 2,780 university students, found a relationship between PIU and insufficient rest. However, they did not find any associations between PIU and the use of alcohol, tobacco, or cannabis 29 . Similarly, Peltzer et al. linked heavy Internet use to behaviors such as skipping dental checkups, sedentary lifestyles, drug use, and gambling. They also identified health issues such as being underweight, overweight, or obese, and positive screenings for PTSD 28 . In both studies, women constituted the majority of participants. In the current study, the strongest correlation between PIU scores and individual HBI categories was observed with positive mental attitudes . This aligns with numerous studies highlighting the relationship between PIU and loneliness 42 , symptoms of depression, family problems 29 , social anxiety 43–46 , and sleep disturbances 29,34,48–51 . Depression, negative affect, and life stress have been identified as the strongest psychological risk factors for PIU 27 . For individuals experiencing negative emotional states, the Internet can serve as a maladaptive coping strategy 27,78,79 . Brand et al. emphasize that depression and loneliness are both risk factors for and consequences of Internet addiction 80,81 . On one hand, individuals experiencing loneliness or depressive symptoms may seek solace in the virtual world. On the other hand, excessive Internet use can exacerbate loneliness and increase stress levels 82,83 . The literature underscores the importance of promoting psychological well-being, teaching stress management skills, and treating existing mental health disorders as part of PIU prevention strategies 27 . 4.3. Dopamine and serotonin levels versus PIU and health behaviors (fourth hypothesis) In our study, while direct significant differences in mean dopamine or serotonin levels across PIU risk groups or overall health behavior levels were not observed, a notable exception was found. In the low PIU group, a moderate, statistically significant positive correlation was observed between HBI healthy eating habits scores and dopamine levels (R=0.350,p=0.014) (Table 7). This indicates that among individuals with low Internet engagement, healthier eating habits were associated with higher dopamine levels. Thus, the hypothesis that dopamine and serotonin levels differ in young men depending on the severity of PIU symptoms and the intensity of health behaviors was only partially confirmed, primarily through this specific correlation. Among individuals with low Internet engagement, healthier eating habits were associated with higher dopamine levels. It should be noted, however, that the lack of differences may stem from measuring serum concentrations rather than cerebrospinal fluid levels. The observed relationship between healthy eating habits and dopamine levels in individuals with low PIU aligns with the findings of Korehpaz-Mashhadi et al. 84 , who demonstrated that aerobic exercise can improve the dopaminergic system disrupted by Internet addiction. This suggests that both diet and physical activity can influence dopamine levels and potentially alleviate addiction symptoms. Although our study did not include direct genetic analysis, it aligns with broader research on the role of genetics in addiction. Lee et al. 56 identified a link between short allelic variants of the serotonin transporter gene ( 5-HTTLPR ) and Internet addiction, particularly in men. While our study primarily focused on dopamine and serotonin, other neurotransmitters, such as neurotensin, may play a critical role in the pathogenesis of Internet addiction. Ferraro et al. 85 emphasized the importance of neurotensin in modulating dopamine signaling and its potential role in addiction treatment. Neuroimaging studies, such as those by Yuan et al. 86 and Hong et al. 87 ,, have revealed structural and functional brain changes in individuals addicted to the Internet. Our findings on dopamine may complement these observations, highlighting potential neurobiological mechanisms underlying these changes. The study by Yaunin et al. 88 also identified differences in serotonin transporter ( 5-HTTLPR ) levels in peripheral blood between stressed students addicted to the Internet and those who were stressed but not addicted. They found a link between stress, dopamine transporters, serotonin transporters, and Internet addiction. Their logistic regression analysis showed that a high level of dopamine transporters strongly influenced the likelihood of a stressed student becoming addicted to the Internet. In conclusion, our study demonstrated that excessive Internet use is associated with numerous negative consequences for health and lifestyle. Individuals spending significant time online were more likely to neglect their health, with less attention to diet, physical activity, and mental well-being. Furthermore, time spent in the virtual world often correlated with worsening emotional well-being. Importantly, not only the amount of time spent online but also the type of activity mattered. Those spending extensive time on recreational activities were at higher health risks compared to individuals using the Internet for educational or professional purposes. The findings suggest a strong link between problematic Internet use and threats to both mental and physical health. They underscore the urgent need for effective preventive and therapeutic programs to mitigate the negative impacts of PIU. Developing targeted interventions could significantly help individuals struggling with this issue. 5. Limitations This study has several limitations. First, the lack of standardized questionnaires and cutoff points for problematic Internet use complicates comparisons between studies. Sec-ond, self-report measures, such as questionnaires, are prone to errors influenced by factors like the respondent’s mood and social desirability. Third, the inconsistent terminology used to describe problematic Internet use complicates research and comparisons across studies. Fourth, the study was conducted in a stationary setting during the COVID-19 pandemic, which likely influenced respondent recruitment. Despite these limitations, the findings contribute to the growing body of research on problematic Internet use and Internet addiction, particularly within the Polish population. The results provide valuable data for future cross-cultural comparisons and inform the development of standardized assessment tools and intervention strategies. 6. Conclusions The findings of this study demonstrated that problematic Internet use, particularly characterized by extensive time spent online for non-study/work purposes, was associated with poorer health behavior scores. This suggests that excessive Internet use, especially for recreational activities, may negatively impact mental and physical health. Individuals with higher levels of PIU exhibited poorer positive mental attitudes scores, which may, in turn, contribute to the occurrence of depressive and anxiety symptoms. The results highlight the need to develop interventions aimed at reducing time spent online and improving health behaviors in individuals with problematic Internet use. Pre-ventive actions targeting youth and adults are necessary to promote healthy Internet usage habits. Further research is essential to better understand the mechanisms linking Internet use and health and to develop effective treatment methods for individuals struggling with problematic Internet use. Declarations Acknowledgements We would like to thank the study participants for their time. Author contributions Conceptualization, M.K., A.R. and I.R.; methodology, M.K., A.R., N.T., G.R., P.R., I.R.; formal analysis, A.R.; data curation, A.R., N.T.; writing—original draft preparation, M.K., A.R., G.R., P.R., I.R.; writing—review and editing, M.K., A.R., N.T., I.R.; supervision, I.R.; funding acquisition, A.R., I.R. All authors have read and agreed to the published version of the manuscript. Data availability statement The data presented in this study is available on request from the corresponding author. Additional Information 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. Funding: This research was funded by National Bureau for Drug Prevention. Number 17/HTK/2021 (4 January 2021). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7620010","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":557507685,"identity":"daae3afa-d07d-4603-8a95-5d00bde9cc5c","order_by":0,"name":"Marta Kożybska","email":"","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Kożybska","suffix":""},{"id":557507688,"identity":"dc66559a-8773-48dc-8420-cbef54d1045f","order_by":1,"name":"Aleksandra Rył","email":"","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Aleksandra","middleName":"","lastName":"Rył","suffix":""},{"id":557507691,"identity":"7adaf885-332c-4818-815d-2a292f50750c","order_by":2,"name":"Natalia Tomska","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYNCCAgko4wCDHITBBsTsuJQzA7GBBEQRUIsxQgszXi0McC2JDYS0mLOfP/iBwcBCnkG++ejGH2fs0rdLZCcwfCg7zGCOQ4tlTzKzBNBhhg1sbGm3eW4k5+6ckbuBcca5wwyWzdi1GBxIZpD+YyDB2MDGY3ab4QNz7oYbuRuYedsOMxgcxqHl/GPmH0Bb7EFabv74UJ9uANLyF5+WG8lsIIclgrTc4LlxOAGshRGvlsdmFkAtyW1saUC/nDluuLPn7YaDPefSeXD65Xzi4xsMFXW2/cyHj938caxa3pw9d+ODH2XWcubsDdj1wAAb3BAGUPQwMPAY4NeAbC8GYxSMglEwCkY6AADlaVoxpvt+sQAAAABJRU5ErkJggg==","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Tomska","suffix":""},{"id":557507698,"identity":"7c0fd167-5726-4fc4-b906-89867e80c0a1","order_by":3,"name":"Grzegorz Raczyński","email":"","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Grzegorz","middleName":"","lastName":"Raczyński","suffix":""},{"id":557507701,"identity":"f55e753e-ddfe-4261-9a74-395a55bac3df","order_by":4,"name":"Paulina Rotter","email":"","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Paulina","middleName":"","lastName":"Rotter","suffix":""},{"id":557507707,"identity":"c6c32638-63a2-46a1-a43e-a13427f5c496","order_by":5,"name":"Iwona Rotter","email":"","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Iwona","middleName":"","lastName":"Rotter","suffix":""}],"badges":[],"createdAt":"2025-09-15 11:23:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7620010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7620010/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97980337,"identity":"f1ef1afe-69fa-4c48-bd71-7f5e5e5dc396","added_by":"auto","created_at":"2025-12-11 12:39:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6792444,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportsKozybska.docx","url":"https://assets-eu.researchsquare.com/files/rs-7620010/v1/27a33e58baf88fa2f480caff.docx"},{"id":97980334,"identity":"dfa079fc-26e6-44eb-a2bf-8b399a72d373","added_by":"auto","created_at":"2025-12-11 12:39:43","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7628,"visible":true,"origin":"","legend":"","description":"","filename":"2e1754fe7bf94c3fb948280c7dfbe576.json","url":"https://assets-eu.researchsquare.com/files/rs-7620010/v1/41530abe22a9337543b34e74.json"},{"id":98622611,"identity":"72d5b3b6-e251-4d06-a4b8-d24d9b87813a","added_by":"auto","created_at":"2025-12-19 16:59:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1206472,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7620010/v1/efa837cf-c55d-4e56-9dfc-337928732ac4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Problematic Internet Use and Health Behaviors in Young Men: Potential Links with Neural Reward Pathways","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eProblematic Internet Use (PIU) is defined as internet use that results in psychological, social, academic, and/or occupational difficulties in an individual's life \u003csup\u003e1\u003c/sup\u003e. It is frequently associated with prolonged online activity \u003csup\u003e2,3\u003c/sup\u003e, which can significantly influence lifestyle and lead to changes in health-related behaviors. Excessive screen time often occurs at the expense of physical activity, adequate sleep, or the preparation of healthy meals.\u003c/p\u003e\n\u003cp\u003eThe post-pandemic era has seen the intesified shift of daily activities such as work and education to online platforms. This transition is accompanied by a rising prevalence of Internet addiction \u003csup\u003e4,5\u003c/sup\u003e and a concurrent decline in health-promoting behaviors among young adults \u003csup\u003e6–8\u003c/sup\u003e. The issue is particularly pronounced among men, who, for years, have demonstrated poorer health behaviors compared to women, including shorter life expectancies \u003csup\u003e9\u003c/sup\u003e, higher rates of drug use, and greater engagement in risky sexual behaviors \u003csup\u003e10\u003c/sup\u003e. These disparities have deepened in recent years. During the COVID-19 pandemic, men reported greater declines in overall health \u003csup\u003e11\u003c/sup\u003e and health behaviors, such as physical activity \u003csup\u003e6\u003c/sup\u003e, relative to women. Although some studies report differing findings \u003csup\u003e12–14\u003c/sup\u003e, a substantial body of evidence indicates that men are at a higher risk for PIU compared to women \u003csup\u003e3,13,15–25\u003c/sup\u003e. This increased vulnerability may be partly explained by men’s higher engagement in online gaming \u003csup\u003e26\u003c/sup\u003e, a well-established predictor of Internet addiction \u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe literature underscores the association between PIU and less healthy lifestyles. Research demonstrates that PIU correlates with both underweight and overweight conditions \u003csup\u003e28–30\u003c/sup\u003e, poor dietary habits, and addictive eating behaviors \u003csup\u003e31\u003c/sup\u003e. Individuals with PIU are more likely to consume fast food \u003csup\u003e32\u003c/sup\u003e. Kim et al. observed that high-risk Internet users often exhibit poor diet quality. These individuals frequently experience appetite suppression, skip meals, eat irregularly, and rely on snacking, resulting in imbalanced nutritional intake \u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePrevious studies have also identified an association between Internet addiction and low levels of physical activity \u003csup\u003e31,33–37\u003c/sup\u003e. Conversely, engaging in physical activity has been shown to prevent and even mitigate the severity of Internet addiction \u003csup\u003e38–40\u003c/sup\u003e. Unlike Internet use, physical activity offers opportunities to engage with the real world, promoting both physical fitness and psychological well-being \u003csup\u003e41\u003c/sup\u003e. Zhihao et al. proposed that physical activity may reduce the prevalence of Internet addiction by enhancing self-esteem \u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eNumerous studies underscore the association between PIU and adverse psychological factors, including loneliness \u003csup\u003e42\u003c/sup\u003e, symptoms of depression, family conflict \u003csup\u003e29\u003c/sup\u003e, social anxiety \u003csup\u003e43–46\u003c/sup\u003e, and positive screening results for post-traumatic stress disorder (PTSD) \u003csup\u003e28\u003c/sup\u003e. Additionally, Internet addiction has been linked to heightened stress levels and a decline in overall well-being \u003csup\u003e47\u003c/sup\u003e. Individuals exhibiting PIU frequently report insufficient sleep duration and a higher prevalence of sleep disorders \u003csup\u003e29,34,48–51\u003c/sup\u003e. Moreover, PIU correlates with other detrimental health behaviors, such as neglecting dental checkups, substance abuse (including illegal drugs, alcohol, and tobacco), and gambling \u003csup\u003e28,33,37\u003c/sup\u003e. Physical symptoms such as migraines and back pain are also more commonly reported among individuals with PIU compared to those without problematic Internet habits \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAdhering to fundamental principles of a healthy lifestyle – such as maintaining a balanced diet, engaging in regular physical activity, and ensuring adequate sleep – is crucial for maintaining balanced levels of neurotransmitters like dopamine and serotonin. Recent research has highlighted the significance of these neurotransmitters in the development of behavioral addictions. Dopamine, often referred to as the \"reward neurotransmitter\", is released during pleasurable activities, reinforcing behaviors that provide satisfaction. Internet use is one such activity that activates the brain’s reward system. However, excessive and prolonged stimulation of this system can result in reduced dopamine D2 receptor expression and decreased receptor sensitivity, necessitating increasingly intense stimuli to achieve similar levels of satisfaction \u003csup\u003e52,53\u003c/sup\u003e. Dopamine plays a pivotal role in addiction, including behavioral addictions. Studies have identified differences in dopamine levels between adolescents with Internet addiction and control groups \u003csup\u003e54\u003c/sup\u003e. Liu and Luo suggested that Internet addiction could result in neurobiological damage, with neuroimaging studies revealing dysfunctions in the brain’s dopaminergic systems \u003csup\u003e55\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSerotonin, another critical hormone regulating human behavior, is involved in processes related to motivation and desires \u003csup\u003e52\u003c/sup\u003e. Research indicates that individuals with Internet addiction often exhibit dysfunctions in the serotonergic system. A higher frequency of the homozygous short allelic variant of the serotonin transporter gene (SS-5HTTLPR) has been observed in individuals with excessive Internet use \u003csup\u003e56\u003c/sup\u003e. Dai et al. identified the T allele of rs6313 in the serotonin 2A receptor as a risk factor for Internet addiction disorder (IAD) \u003csup\u003e57\u003c/sup\u003e. Additionally, Annunzi et al. reported differential epigenetic regulation of the serotonin reuptake transporter gene among university students, depending on the severity of their Internet addiction \u003csup\u003e58\u003c/sup\u003e. Yaunin et al. further identified significant differences in peripheral blood levels of dopamine transporter (DAT) and serotonin transporter (SERT/5-HTT) between stressed students with Internet addiction and their stressed, non-addicted counterparts [58].\u003c/p\u003e\n\u003cp\u003eCurrent evidence indicates that young men tend to lead less healthy lifestyles compared to women and are at a higher risk of developing PIU in many populations. PIU is associated with various detrimental health behaviors, suggesting that excessive Internet use may exacerbate the negative health behaviors commonly observed in young men. This phenomenon could significantly contribute to poorer health outcomes for this demographic in the coming decades, posing a notable public health challenge.\u003c/p\u003e\n\u003cp\u003eThe aim of this study was to examine the relationship between health behaviors and PIU among young men, with a particular focus on serotonin and dopamine levels. Based on the theoretical framework presented, the following research hypotheses were formulated:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe risk of PIU increases with the amount of time spent online. \u003c/li\u003e\n\u003cli\u003eAs time spent online increases, overall health behaviors, as well as specific aspects such as healthy eating habits, preventive behaviors, positive mental attitude, and health practices, deteriorate. \u003c/li\u003e\n\u003cli\u003eMen with more severe PIU symptoms are more likely to exhibit unhealthy lifestyles, both overall and in specific areas, including healthy eating habits, preventive behaviors, positive mental attitude, and health practices. \u003c/li\u003e\n\u003cli\u003eDopamine and serotonin levels vary among young men depending on the severity of PIU symptoms and the intensity of their health behaviors. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"2.\tMethods","content":"\u003cp\u003e2.1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Study procedure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cross-sectional study was conducted in the West Pomeranian region of Poland. In accordance with the study objectives, the inclusion criteria were male gender, age between 18 and 30 years, Internet usage, and a declared willingness to complete the questionnaire. The exclusion criterion was being under the care of a psychiatrist or psychologist. Participants were recruited via online forums, advertisements at healthcare centers, and social media. The questionnaires were completed using the paper-and-pencil method, and no remuneration was provided. Each participant was informed about the purpose of the study, the possibility of withdrawal at any stage, and provided written informed consent to participate. The study was approved by the Bioethics Committee of the Pomeranian Medical University in Szczecin (KB-0012/90/18).\u003c/p\u003e\n\u003cp\u003e2.2. Participants\u003c/p\u003e\n\u003cp\u003eThe study included 325 young men, with a mean age of 25.1 years (SD = 3.7). The participants were predominantly students, with more than half studying medical or health sciences. Most participants lived in urban areas, while 9% resided in rural areas. The average BMI of the participants was 25.5 (SD = 4.2). The differences in the overall IUT scores between the PIU risk groups were statistically significant (p = 0.006).\u003c/p\u003e\n\u003cp\u003eTable 1. Characteristics of the study group, IUT and HBI results, dopamine and serotonin levels.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDopamine level (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSerotonin level (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e124.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e118.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudent status\u003c/p\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;69.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResidential area\u003c/p\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003cp\u003eTown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e29\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003cp\u003e91.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelf-assessment of financial situation\u003c/p\u003e\n \u003cp\u003every good\u003c/p\u003e\n \u003cp\u003esufficient\u003c/p\u003e\n \u003cp\u003epoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e141\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e176\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage daily number of hours spent using the Internet on weekdays:\u003c/p\u003e\n \u003cp\u003efor study or work purposes\u003c/p\u003e\n \u003cp\u003efor purposes other than study or work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage daily number of hours spent using the Internet on weekends:\u003c/p\u003e\n \u003cp\u003efor study or work purposes\u003c/p\u003e\n \u003cp\u003efor purposes other than study or work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIUT score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePIU category\u003c/p\u003e\n \u003cp\u003elow PIU\u003c/p\u003e\n \u003cp\u003emoderate PIU\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehigh and very high PIU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003cp\u003e54.8\u003c/p\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI score\u003c/p\u003e\n \u003cp\u003eHBI overall\u003c/p\u003e\n \u003cp\u003eHBI healthy eating habits\u003c/p\u003e\n \u003cp\u003eHBI preventive behaviours \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHBI positive mental attitude\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHBI health practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e78.2\u003c/p\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003cp\u003e19.1 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI category\u003c/p\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003cp\u003ehigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e32.6\u003c/p\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI, Body Mass Index; IUT, Internet Use Test; PIU, Problematic Internet Use; HBI, Health-Related Behaviour Inventory\u003c/p\u003e\n\u003cp\u003e2.3.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Measures\u003c/p\u003e\n\u003cp\u003e2.3.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Internet Use Test (IUT)\u003c/p\u003e\n\u003cp\u003eTo assess the severity of symptoms associated with problematic Internet use, the Internet Use Test (IUT), developed by Ryszard Poprawa from the Institute of Psychology at the University of Wrocław, Poland, was employed. The test includes questions addressing the psychological, social, and health consequences of Internet use. It consists of 23 items rated on a scale from 0 (\u0026quot;never\u0026quot;) to 5 (\u0026quot;always\u0026quot;). The raw score ranges from 0 to 115, with higher scores indicating greater severity of problematic Internet use symptoms. Based on symptom severity, four groups can be distinguished: (1) Low Problematic Internet Use (0\u0026ndash;5 points); (2) Moderate Problematic Internet Use (6\u0026ndash;35 points); (3) High Problematic Internet Use (36\u0026ndash;72 points); (4) Very High Problematic Internet Use (73\u0026ndash;115 points)\u0026nbsp;\u003csup\u003e59\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Health-Related Behavior Inventory (HBI)\u003c/p\u003e\n\u003cp\u003eTo uniformly and comprehensively measure health behaviors while considering the socioeconomic conditions of the studied population, the standardized Polish tool Health-Related Behavior Inventory (HBI) by Zygfryd Juczyński was selected\u0026nbsp;\u003csup\u003e60\u003c/sup\u003e. The scale comprises 24 questions about health behaviors. Respondents rate their answers on a Likert scale ranging from 1 (\u0026quot;almost never\u0026quot;) to 5 (\u0026quot;almost always\u0026quot;). Higher scores indicate a lifestyle more conducive to health. In addition to the nominal score, the tool allows categorization into three levels of health behaviors: (1) Low: 24\u0026ndash;71 for men, 24\u0026ndash;77 for women; (2) Moderate: 72\u0026ndash;86 for men, 78\u0026ndash;91 for women; (3) High: 87\u0026ndash;120 for men, 92\u0026ndash;120 for women.\u003c/p\u003e\n\u003cp\u003eThe tool also distinguishes four categories of health behaviors:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Healthy eating habits (e.g., questions about consuming fruits and vegetables, whole-grain bread, and limiting intake of animal fats, sugar, salt, and preservatives).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Preventive behaviors (e.g. includes avoiding illnesses, following medical advice, regular medical check-ups, and seeking health-related information).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Positive mental attitudes (e.g. includes avoiding depressive situations, stress, and tension, maintaining friendships, having a stable family life, avoiding negative emotions, and thinking positively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Health practices (e.g. includes leading a healthy lifestyle with adequate rest, sleep, weight control, avoiding smoking, and refraining from excessive exercise).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Cronbach\u0026rsquo;s alpha for the entire scale is 0.85, and for the four subscales, it ranges from 0.60 to 0.65\u0026nbsp;\u003csup\u003e60\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.3.3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Custom questionnaire on sociodemographic data and Internet usage characteristics\u003c/p\u003e\n\u003cp\u003eThe questionnaire included questions about age, body weight, height, student status, place of residence (city/village), self-assessment of financial situation (very good/sufficient/poor), and the average number of hours spent daily using the Internet:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;From Monday to Friday:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;\u0026nbsp;For study or work purposes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;For purposes other than study or work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;On weekends:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;\u0026nbsp;For study or work purposes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;For purposes other than study or work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3.4.\u0026nbsp;Determination of dopamine and serotonin levels\u003c/p\u003e\n\u003cp\u003eBlood samples were collected from the ulnar vein of fasting participants between 7:00 and 10:00 a.m. The blood was drawn into 9 mL coagulation tubes for the analysis of hormonal parameters. The samples were centrifuged, and the serum was stored at -20\u0026deg;C for further testing.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Serum concentrations of 5-HT (serotonin) and DA (dopamine) were determined using the ELISA method\u0026nbsp;\u003csup\u003e61\u003c/sup\u003e. The analyses were performed at the laboratory of the Pomeranian Medical University in Szczecin.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The ELISA procedure involved the following steps. Empty wells: No samples or horseradish peroxidase (HRP) were added. Standard wells: 50 \u0026mu;L of standard solution was added to the designated wells. Specimen wells: 40 \u0026mu;L of diluent followed by 10 \u0026mu;L of the specimen was added to each well. HRP addition: A total of 50 \u0026mu;L of HRP was added to all wells except the blank well.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The plates were gently vortexed and incubated at 37\u0026deg;C for 60 minutes. Excess fluid was discarded, and each well was rinsed with diluted cleaning fluid, mixed for 30 seconds, and the fluid was decanted. This washing process was repeated five times, and the wells were blotted dry. The reaction was stopped by adding 50 \u0026mu;L of stop solution to each well. Optical density (OD) measurements were taken at a wavelength of 450 nm. The blank well was set as the baseline, and the standard curve was generated using the OD values of the standards. Linear regression was used to calculate sample concentrations from their respective OD values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.4. Statistical analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor statistical analysis, the groups of participants with High and Very High Problematic Internet Use were combined. The analysis was performed using Statistica 13.1 (StatSoft Poland, Krakow, Poland). As the distribution of continuous variables deviated from normality, the data are presented as medians and interquartile ranges. Statistical inference was conducted using the Mann\u0026ndash;Whitney U test or the Kruskal\u0026ndash;Wallis test. For logistic regression analysis, participants were grouped into two categories: men with low levels of Internet addiction and men with moderate to high levels of Internet addiction. The significance level was set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Characteristics of Internet use in the study group\u003c/p\u003e\n\u003cp\u003eAs shown in Table 1, participants spent an average of 3 hours daily using the Internet for purposes other than work or study on weekdays and 4 hours daily on weekends. The mean IUT score was 19.6 points. Analysis of the categorical IUT results revealed that the majority of participants (54.8%) were classified as having moderate PIU. Comparable percentages of participants were found in the low PIU group (27.7%) and the high/very high PIU group (17.5%). Differences between groups were statistically significant (p = 0.006). Table 2 presents the average amount of time spent online by the study group, categorized by PIU risk levels. Post-hoc analysis revealed statistically significant differences between the groups in the time spent online for purposes other than study or work (both on weekdays and weekends), while no significant differences were found for time spent on study or work.\u003c/p\u003e\n\u003cp\u003eTable 2. Number of hours spent online in PIU risk groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow PIU,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate PIU, n=178\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh and very high PIU, n=57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInternet hours for study during the week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInternet hours for other activities during the week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInternet hours for study on weekends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInternet hours for other activities on weekends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePIU, Problematic Internet Use\u003c/p\u003e\n\u003cp\u003e3.2. Health behaviors in the study group and Internet use\u003c/p\u003e\n\u003cp\u003eParticipants scored an average of 78.2 out of 120 possible points on the HBI (Table 1). One-third of participants, specifically 32.6% (106 individuals), demonstrated low levels of health behaviors (Table 1). Participants scored the highest in the \u003cem\u003epositive mental attitudes\u003c/em\u003e category (mean 21.1) and the lowest in \u003cem\u003epreventive behaviors\u003c/em\u003e and \u003cem\u003ehealth practices\u003c/em\u003e categories (mean 19.1 for both) (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 3 shows the level of health behaviors across the PIU risk groups. Among individuals with low PIU (n=90), the highest proportion (11.1%, corresponding to 36 individuals) had a high level of health behaviors. Conversely, among those with high or very high PIU (n=57), the highest proportion (7.69%, corresponding to 25 individuals) had a low level of health behaviors.\u003c/p\u003e\n\u003cp\u003eTable 3. HBI category in PIU risk groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBI category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow PIU,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate PIU,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=178\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh and very high PIU, n=57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elow HBI, n=106\u003c/p\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003cp\u003e% of total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emoderate HBI, n=133\u003c/p\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003cp\u003e% of total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003cp\u003e23%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehigh HBI, n=86\u003c/p\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003cp\u003e% of total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHBI, Health-Related Behaviour Inventory\u003c/p\u003e\n\u003cp\u003eAnalyzing correlations between health behaviors and the severity of problematic Internet use revealed that both the overall HBI score and all categories except \u003cem\u003epreventive behaviors\u003c/em\u003e showed statistically significant negative correlations with IUT scores. However, these correlations were weak (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Correlation analysis between the IUT score and the HBI score\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth behaviors (HBI score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eProblematic Internet Use (IUT score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI overall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI healthy eating habits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI preventive behaviours \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI positive mental attitude\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI health practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIUT, \u0026nbsp;Internet Use Test;HBI, Health-Related Behaviour Inventory\u003c/p\u003e\n\u003cp\u003eSimilar analyses conducted within each PIU risk group (low, moderate, high, and very high) showed that the negative correlation between \u003cem\u003epositive mental attitudes\u003c/em\u003e and IUT scores in the moderate PIU group was at the threshold of statistical significance but did not meet the adopted criterion (p = 0.050) (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5. Correlation analysis between the IUT score and health behaviors in PIU risk groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Pair of variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow PIU,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate PIU,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=178\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh and very high PIU, n=57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI overall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eIUT\u003csup\u003e3\u003c/sup\u003e score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI healthy eating habits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI preventive behaviours \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI positive mental attitude\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBI health practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePIU, Problematic Internet Use; HBI, Health-Related Behaviour Inventory;IUT, Internet Use Test\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough the PIU risk groups did not consistently differ in all aspects of time spent online (Table 2), notable differences were observed in the correlations between online time and HBI scores across the groups (Table 6). Specifically, in the high/very high PIU group, the overall HBI score was negatively correlated with daily Internet use for purposes other than work or study, both on weekdays (R=\u0026minus;0.385,p=0.004) and weekends (R=\u0026minus;0.360,p=0.008). Additionally, negative correlations were observed between positive mental attitudes and time spent on the Internet for non-work/study purposes. These correlations were strongest in the high/very high PIU group, both on weekdays (R=\u0026minus;0.412,p=0.002) and weekends (R=\u0026minus;0.347,p=0.011), though still moderate. Negative correlations were also identified between daily Internet use for purposes other than work or study on weekdays and healthy eating habits (in the moderate PIU group: R=\u0026minus;0.188,p=0.014; in the high/very high PIU group: R=\u0026minus;0.380,p=0.004) and preventive behaviors (in the moderate PIU group: R=\u0026minus;0.189,p=0.014; in the high/very high PIU group: R=\u0026minus;0.309,p=0.021). All these significant correlations presented as moderate (Table 6).\u003c/p\u003e\n\u003cp\u003eTable 6. Correlation analysis between time spent online and health behaviors according to PIU risk groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Pair of variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow PIU,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate PIU,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=178\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh and very high PIU, n=57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHBI overall\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekdays for study/work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekdays for other purposes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekend for studying/working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekend for other purposes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHBI healthy eating habits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekdays for study/work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekdays for other purposes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekend for studying/working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekend for other purposes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHBI preventive behaviours \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekdays for study/work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekdays for other purposes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekend for studying/working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekend for other purposes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHBI positive mental attitude\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekdays for study/work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekdays for other purposes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekend for studying/working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekend for other purposes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHBI health practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekdays for study/work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekdays for other purposes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekend for studying/working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edaily internet time on weekend for other purposes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePIU, Problematic Internet Use;HBI, Health-Related Behaviour Inventory\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.3.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Dopamine and serotonin levels and health behaviors\u003c/p\u003e\n\u003cp\u003eTable 7 presents correlations between HBI scores and dopamine and serotonin levels within PIU risk groups. In the low PIU group, a statistically significant, moderate positive correlation was observed between \u003cem\u003eHBI healthy eating habits\u003c/em\u003e scores and dopamine levels. Other relationships were not statistically significant.\u003c/p\u003e\n\u003cp\u003eTable 7. Correlation analysis between HBI score and dopamine and serotonin levels in PIU risk groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Pair of variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow PIU,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate PIU,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=178\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh and very high PIU, n=57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI overall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eserotonin level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI healthy eating\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehabits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI preventive behaviours \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI positive mental attitude\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI health practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003edopamine level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI healthy eating\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehabits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI preventive behaviours \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI positive mental attitude\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI health practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0,063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePIU, Problematic Internet Use;HBI, Health-Related Behaviour Inventory\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 8 shows the results of linear correlation analysis between health behaviors, serotonin and dopamine levels, and IUT scores after adjusting for age and BMI. Statistically significant but weak negative correlations were observed between IUT scores and \u003cem\u003eHBI healthy eating habits\u003c/em\u003e, \u003cem\u003eHBI positive mental attitudes\u003c/em\u003e, and overall HBI scores. While dopamine levels were not correlated with IUT scores (p=0.997), a statistically significant, albeit weak, correlation was observed between serotonin levels and IUT scores (p=0.020) (Table 8).\u003c/p\u003e\n\u003cp\u003eTable 8. Linear correlation analysis between health behaviors, serotonin and dopamine levels, and IUT score (adjusted for age and BMI)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"675\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIUT\u003csup\u003e1\u003c/sup\u003e score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e-95,00%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95,00%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIUT score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGr.ufn.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGr.ufn.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBeta (\u0026szlig;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI\u003csup\u003e2\u003c/sup\u003e overall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.1955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI healthy eating habits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.1644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI preventive behaviours \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.0715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI positive mental attitude\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-9.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.2628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBI health practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.0878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eserotonin level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edopamine level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.0611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIUT, Internet Use Test; HBI, Health-Related Behaviour Inventory\u003c/p\u003e"},{"header":"4.\tDiscussion","content":"\u003cp\u003e4.1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Prevalence of PIU and the relationship between time spent online and PIU risk (first hypothesis)\u003c/p\u003e\n\u003cp\u003eThe results indicate that 17.7% of participants had high or very high risk of developing problematic Internet use (PIU). By comparison, a systematic review conducted before the pandemic (2003\u0026ndash;2018)\u0026nbsp;\u003csup\u003e62\u003c/sup\u003e reported a prevalence of generalized Internet addiction (GIA) at 7.02% (95% CI: 6.09%\u0026ndash;8.08%). Among countries with geographical proximity to Poland, prevalence rates varied: Germany, 0.5%\u0026ndash;15.7%\u0026nbsp;\u003csup\u003e63,64\u003c/sup\u003e; Lithuania, 18.3%\u0026nbsp;\u003csup\u003e65\u003c/sup\u003e; Hungary, 1.6%\u0026nbsp;\u003csup\u003e66\u003c/sup\u003e; Slovenia, 3.1%\u0026ndash;5.8%\u0026nbsp;\u003csup\u003e66,67\u003c/sup\u003e; and Romania, 4.6%\u0026ndash;8.7%\u0026nbsp;\u003csup\u003e66,68\u003c/sup\u003e. In Poland, studies conducted before the pandemic found a PIU prevalence of 10.2%\u0026nbsp;\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;During the COVID-19 pandemic, a meta-analysis revealed a pooled prevalence rate of 25% for PIU; however, applying a stricter threshold reduced the prevalence to 7.9%\u0026nbsp;\u003csup\u003e69\u003c/sup\u003e. Even within geographically similar regions, prevalence rates varied widely. For instance, in Poland, 7.8% of individuals were identified as problematic Internet users\u0026nbsp;\u003csup\u003e70\u003c/sup\u003e, compared to 21.3% in Switzerland\u0026nbsp;\u003csup\u003e71\u003c/sup\u003e and only 3.9%\u0026ndash;5.2% in Hungary\u0026nbsp;\u003csup\u003e22,23\u003c/sup\u003e. This variability is a recurring phenomenon in PIU and Internet addiction research. It arises from differences in terminology (e.g., PIU, Internet addiction, excessive Internet use), the use of diverse measurement tools and criteria, cultural differences\u0026nbsp;\u003csup\u003e69\u003c/sup\u003e, and variations in Internet access and usage patterns. Analyzing differences in PIU prevalence is crucial for a better understanding of the phenomenon.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Several factors may explain why the prevalence rates in our study are higher than those observed in other populations. First, our study exclusively included men, who, according to numerous studies, are more susceptible to PIU and often score higher on PIU scales compared to women\u0026nbsp;\u003csup\u003e3,13,15\u0026ndash;25,72\u003c/sup\u003e. Second, the study was conducted during 2020\u0026ndash;2021, a period characterized by a rapid shift of various activities from the real world to the online sphere due to the COVID-19 pandemic. Third, the participants were young adults and students \u0026ndash; a demographic that, according to many sources, is particularly vulnerable to PIU\u0026nbsp;\u003csup\u003e3,12,24,73,74\u003c/sup\u003e. This vulnerability may stem from both the necessity of using online tools for academic purposes and greater autonomy in managing their time.\u003c/p\u003e\n\u003cp\u003eInterestingly, in our study, the average time spent online for study or work purposes was not significantly associated with membership in any of the PIU risk groups. However, the time spent online for other, non-study/work purposes was significantly different across PIU risk groups. Therefore, the first hypothesis \u0026ndash; that the risk of PIU increases with the amount of time spent online \u0026ndash; must be partially rejected, as the type of online activity appears to be a more critical factor than overall duration.\u003c/p\u003e\n\u003cp\u003eThis finding aligns with previous research suggesting that the development of addictive Internet use is determined not by the duration of use but by the nature of online activities. Fern\u0026aacute;ndez-Villa et al. found that men spend less time online per week compared to women but engage more in activities deemed highly addictive, such as recreational activities (e.g., gaming and shopping), rather than social purposes\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e.\u0026nbsp;This suggests that the type of activity is key in the context of PIU development. A 2023 systematic review highlighted that online gaming, in particular, is a strong predictor of Internet addiction\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Health behaviors and their relationship with PIU (second hypothesis) and time spent online (third hypothesis)\u003c/p\u003e\n\u003cp\u003eOur study revealed that one-third of participants exhibited a low level of health-promoting behaviors as measured by the HBI questionnaire. This high percentage of young individuals leading unhealthy lifestyles is concerning, as it increases their future risk of developing diseases associated with poor habits. Participants scored the highest in the \u003cem\u003epositive mental attitudes\u003c/em\u003e category and the lowest in \u003cem\u003epreventive behaviors\u003c/em\u003e and \u003cem\u003ehealth practices\u003c/em\u003e categories.\u003c/p\u003e\n\u003cp\u003eIn the \u003cem\u003elow PIU\u003c/em\u003e group, the largest proportion of participants had a high level of health behaviors. Conversely, among those with high or very high PIU, the majority had a low level of health behaviors. Both the overall HBI score and all its categories (except \u003cem\u003epreventive behaviors\u003c/em\u003e) were negatively and significantly correlated with IUT scores, although the correlations were weak. Thus, the hypothesis that men with more severe PIU symptoms are more likely to lead unhealthy lifestyles overall \u0026ndash; and in areas such as \u003cem\u003ehealthy eating habits\u003c/em\u003e, \u003cem\u003epositive mental attitudes\u003c/em\u003e, and \u003cem\u003ehealth practices\u003c/em\u003e \u0026ndash; is supported.\u003c/p\u003e\n\u003cp\u003eIn the group of users with high or very high PIU, the amount of time spent online was significantly associated with health behaviors. Users who spent less time on recreational Internet use (activities unrelated to work or study) during weekdays exhibited greater levels of health-promoting behaviors overall, as well as in the categories of \u003cem\u003epositive mental attitudes\u003c/em\u003e, \u003cem\u003ehealthy eating habits\u003c/em\u003e, and \u003cem\u003epreventive behaviors\u003c/em\u003e. Regarding Internet use on weekends, spending more time online for non-work/study purposes was associated with lower levels of overall health behaviors and \u003cem\u003epositive mental attitudes\u003c/em\u003e. However, the time spent online for work or study purposes, whether on weekdays or weekends, was not associated with health behaviors. Thus, the second hypothesis \u0026ndash; that increasing time spent online leads to a decrease in health behaviors overall and in the areas of \u003cem\u003ehealthy eating habits\u003c/em\u003e, \u003cem\u003epreventive behaviors\u003c/em\u003e, \u003cem\u003epositive mental attitudes\u003c/em\u003e, and \u003cem\u003ehealth practices\u003c/em\u003e \u0026ndash; was partially confirmed. This suggests that while the amount of time spent online may not directly cause addictive Internet use, it plays a crucial role in shaping one\u0026rsquo;s lifestyle.\u003c/p\u003e\n\u003cp\u003eThe relationship between PIU and unhealthy lifestyles has also been confirmed in other studies. Bener and colleagues analyzed data from 3,000 students aged 12\u0026ndash;25, 72% of whom were boys and men. They found that compared to girls and women, boys and men showed stronger PIU symptoms and depression and were more likely to consume fast food. Those without PIU were more likely to engage in mild or moderate physical activity than those with PIU and had more hours of sleep\u0026nbsp;\u003csup\u003e75\u003c/sup\u003e. Similarly, Atilla et al. studied a comparable age group (15\u0026ndash;24 years) with an overrepresentation of women (84%). Their findings also demonstrated a link between unhealthy diets and low levels of physical activity with Internet addiction. This study is particularly notable as, like the present research, health behaviors were measured using a standardized tool (Nutrition Exercise Behavior Scale)\u0026nbsp;\u003csup\u003e31\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Family Nutrition and Physical Activity Tool has been used in the context of PIU in younger age groups (7\u0026ndash;10 years). It demonstrated associations between Internet addiction and behaviors such as eating while using the Internet, excessive body weight, the overall score on the Family Nutrition and Physical Activity Tool (albeit with a very weak correlation), and sleep disturbances. These findings suggest that PIU among children is linked to family environments, practices, and behaviors that may contribute to the development of obesity in the future\u0026nbsp;\u003csup\u003e76\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn their research, T\u0026oacute;th et al. examined lifestyle elements including adherence to WHO physical activity guidelines, sleep duration, self-preparation of meals, skipping breakfast, eating snacks or beverages while engaging in online learning, and smoking. They found that among individuals spending more hours on online learning, a smaller percentage skipped breakfast. However, longer online learning hours were associated with shorter and poorer-quality sleep, as well as lower quality of life in terms of mental health. These results differ from those of the present study, where time spent online for work or study purposes was not linked to health behaviors. It is worth noting that in T\u0026oacute;th et al.\u0026rsquo;s research, women constituted 70% of the sample, which may explain the observed differences\u0026nbsp;\u003csup\u003e77\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFern\u0026aacute;ndez-Villa et al., in a Spanish study of 2,780 university students, found a relationship between PIU and insufficient rest. However, they did not find any associations between PIU and the use of alcohol, tobacco, or cannabis\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e. Similarly, Peltzer et al. linked heavy Internet use to behaviors such as skipping dental checkups, sedentary lifestyles, drug use, and gambling. They also identified health issues such as being underweight, overweight, or obese, and positive screenings for PTSD\u0026nbsp;\u003csup\u003e28\u003c/sup\u003e. In both studies, women constituted the majority of participants.\u003c/p\u003e\n\u003cp\u003eIn the current study, the strongest correlation between PIU scores and individual HBI categories was observed with \u003cem\u003epositive mental attitudes\u003c/em\u003e. This aligns with numerous studies highlighting the relationship between PIU and loneliness\u0026nbsp;\u003csup\u003e42\u003c/sup\u003e, symptoms of depression, family problems\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e, social anxiety\u0026nbsp;\u003csup\u003e43\u0026ndash;46\u003c/sup\u003e, and sleep disturbances\u0026nbsp;\u003csup\u003e29,34,48\u0026ndash;51\u003c/sup\u003e. Depression, negative affect, and life stress have been identified as the strongest psychological risk factors for PIU\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e. For individuals experiencing negative emotional states, the Internet can serve as a maladaptive coping strategy\u0026nbsp;\u003csup\u003e27,78,79\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBrand et al. emphasize that depression and loneliness are both risk factors for and consequences of Internet addiction\u0026nbsp;\u003csup\u003e80,81\u003c/sup\u003e. On one hand, individuals experiencing loneliness or depressive symptoms may seek solace in the virtual world. On the other hand, excessive Internet use can exacerbate loneliness and increase stress levels\u0026nbsp;\u003csup\u003e82,83\u003c/sup\u003e.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The literature underscores the importance of promoting psychological well-being, teaching stress management skills, and treating existing mental health disorders as part of PIU prevention strategies\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.3. Dopamine and serotonin levels versus PIU and health behaviors (fourth hypothesis)\u003c/p\u003e\n\u003cp\u003eIn our study, while direct significant differences in mean dopamine or serotonin levels across PIU risk groups or overall health behavior levels were not observed, a notable exception was found. In the low PIU group, a moderate, statistically significant positive correlation was observed between HBI healthy eating habits scores and dopamine levels (R=0.350,p=0.014) (Table 7). This indicates that among individuals with low Internet engagement, healthier eating habits were associated with higher dopamine levels. Thus, the hypothesis that dopamine and serotonin levels differ in young men depending on the severity of PIU symptoms and the intensity of health behaviors was only partially confirmed, primarily through this specific correlation. Among individuals with low Internet engagement, healthier eating habits were associated with higher dopamine levels. It should be noted, however, that the lack of differences may stem from measuring serum concentrations rather than cerebrospinal fluid levels.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The observed relationship between healthy eating habits and dopamine levels in individuals with low PIU aligns with the findings of Korehpaz-Mashhadi et al.\u0026nbsp;\u003csup\u003e84\u003c/sup\u003e, who demonstrated that aerobic exercise can improve the dopaminergic system disrupted by Internet addiction. This suggests that both diet and physical activity can influence dopamine levels and potentially alleviate addiction symptoms. Although our study did not include direct genetic analysis, it aligns with broader research on the role of genetics in addiction. Lee et al.\u0026nbsp;\u003csup\u003e56\u003c/sup\u003e identified a link between short allelic variants of the serotonin transporter gene (\u003cem\u003e5-HTTLPR\u003c/em\u003e) and Internet addiction, particularly in men.\u003c/p\u003e\n\u003cp\u003eWhile our study primarily focused on dopamine and serotonin, other neurotransmitters, such as neurotensin, may play a critical role in the pathogenesis of Internet addiction. Ferraro et al.\u0026nbsp;\u003csup\u003e85\u003c/sup\u003e emphasized the importance of neurotensin in modulating dopamine signaling and its potential role in addiction treatment. Neuroimaging studies, such as those by Yuan et al.\u0026nbsp;\u003csup\u003e86\u003c/sup\u003e and Hong et al.\u0026nbsp;\u003csup\u003e87\u003c/sup\u003e,, have revealed structural and functional brain changes in individuals addicted to the Internet. Our findings on dopamine may complement these observations, highlighting potential neurobiological mechanisms underlying these changes.\u003c/p\u003e\n\u003cp\u003eThe study by Yaunin et al.\u0026nbsp;\u003csup\u003e88\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ealso identified differences in serotonin transporter (\u003cem\u003e5-HTTLPR\u003c/em\u003e) levels in peripheral blood between stressed students addicted to the Internet and those who were stressed but not addicted. They found a link between stress, dopamine transporters, serotonin transporters, and Internet addiction. Their logistic regression analysis showed that a high level of dopamine transporters strongly influenced the likelihood of a stressed student becoming addicted to the Internet.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study demonstrated that excessive Internet use is associated with numerous negative consequences for health and lifestyle. Individuals spending significant time online were more likely to neglect their health, with less attention to diet, physical activity, and mental well-being. Furthermore, time spent in the virtual world often correlated with worsening emotional well-being. Importantly, not only the amount of time spent online but also the type of activity mattered. Those spending extensive time on recreational activities were at higher health risks compared to individuals using the Internet for educational or professional purposes.\u003c/p\u003e\n\u003cp\u003eThe findings suggest a strong link between problematic Internet use and threats to both mental and physical health. They underscore the urgent need for effective preventive and therapeutic programs to mitigate the negative impacts of PIU. Developing targeted interventions could significantly help individuals struggling with this issue.\u003c/p\u003e"},{"header":"5.\tLimitations","content":"\u003cp\u003eThis study has several limitations. First, the lack of standardized questionnaires and cutoff points for problematic Internet use complicates comparisons between studies. Sec-ond, self-report measures, such as questionnaires, are prone to errors influenced by factors like the respondent\u0026rsquo;s mood and social desirability. Third, the inconsistent terminology used to describe problematic Internet use complicates research and comparisons across studies. Fourth, the study was conducted in a stationary setting during the COVID-19 pandemic, which likely influenced respondent recruitment.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, the findings contribute to the growing body of research on problematic Internet use and Internet addiction, particularly within the Polish population. The results provide valuable data for future cross-cultural comparisons and inform the development of standardized assessment tools and intervention strategies.\u003c/p\u003e"},{"header":"6.\tConclusions","content":"\u003cp\u003eThe findings of this study demonstrated that problematic Internet use, particularly characterized by extensive time spent online for non-study/work purposes, was associated with poorer health behavior scores. This suggests that excessive Internet use, especially for recreational activities, may negatively impact mental and physical health. Individuals with higher levels of PIU exhibited poorer positive mental attitudes scores, which may, in turn, contribute to the occurrence of depressive and anxiety symptoms.\u003c/p\u003e\n\u003cp\u003eThe results highlight the need to develop interventions aimed at reducing time spent online and improving health behaviors in individuals with problematic Internet use. Pre-ventive actions targeting youth and adults are necessary to promote healthy Internet usage habits. Further research is essential to better understand the mechanisms linking Internet use and health and to develop effective treatment methods for individuals struggling with problematic Internet use.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the study participants for their time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, M.K., A.R. and I.R.; methodology, M.K., A.R., N.T., G.R., P.R., I.R.; formal analysis, A.R.; data curation, A.R., N.T.; writing\u0026mdash;original draft preparation, M.K., A.R., G.R., P.R., I.R.; writing\u0026mdash;review and editing, M.K., A.R., N.T., I.R.; supervision, I.R.; funding acquisition, A.R., I.R. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study is available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eFunding: This research was funded by National Bureau for Drug Prevention. Number 17/HTK/2021 (4 January 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Bioethics Committee of the Pomera-nian Medical University in Szczecin (protocol code KB-0012/90/18, with a date of approval of 18 June 2018). Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBeard, K. W. \u0026amp; Wolf, E. M. Modification in the proposed diagnostic criteria for Internet addiction. \u003cem\u003eCyberpsychol. Behav.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 377\u0026ndash;383 (2001).\u003c/li\u003e\n\u003cli\u003eTong, W. T., Islam, M., Low, W. Y., Choo, W.-Y. \u0026amp; Abdullah, A. 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Engl.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 112\u0026ndash;127 (2016).\u003c/li\u003e\n\u003cli\u003eYuan, K. \u003cem\u003eet al.\u003c/em\u003e Cortical Thickness Abnormalities in Late Adolescence with Online Gaming Addiction. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e53055 (2013).\u003c/li\u003e\n\u003cli\u003eHong, S.-B. \u003cem\u003eet al.\u003c/em\u003e Decreased Functional Brain Connectivity in Adolescents with Internet Addiction. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e57831 (2013).\u003c/li\u003e\n\u003cli\u003eYaunin, Y. \u003cem\u003eet al.\u003c/em\u003e The Difference of Dopamine Transporter and Serotonin Transporter Level Between Addicted and Non-Addicted Internet User Experiencing Stress in Senior High School Students in Padang, Indonesia. in (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Problematic Internet Use, health behaviors, dopamine, serotonin, young men, addiction, lifestyle, mental health","lastPublishedDoi":"10.21203/rs.3.rs-7620010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7620010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Problematic Internet Use (PIU) negatively impacts psychological, social, academic, and occupational functioning and is associated with unhealthy behaviors such as poor diet, inactivity, and sleep problems. The post-pandemic period has intensified Internet overuse, especially among young men, who are more prone to risky health behaviors. This study aimed to assess the relationship between health behaviors, PIU, and serotonin and dopamine levels in young men. Methods: A cross-sectional study was conducted among 325 men aged 18–30 from the West Pomeranian region of Poland. PIU severity was measured using the Internet Use Test (IUT), and health behaviors were assessed using the Health-Related Behavior Inventory (HBI). Blood samples were analyzed for serotonin and dopamine levels using ELISA. Results: 17.7% of participants exhibited high or very high PIU. One-third led unhealthy lifestyles. Higher PIU correlated with lower health behavior scores. The overall HBI score was negatively associated with non-work Internet use during weekdays and weekends. In the low PIU group, healthy eating habits positively correlated with dopamine levels. The linear correlation analysis, after adjusting for age and BMI, revealed a correlation between IUT scores and overall HBI scores, HBI healthy eating habits, and HBI positive mental attitudes. Conclusions: Increased Internet use was linked to poorer health behaviors. These findings underscore the need for interventions aimed at reducing Internet time and promoting healthy behaviors among young men with PIU. Further research should explore neurobiological mechanisms behind these associations.","manuscriptTitle":"Problematic Internet Use and Health Behaviors in Young Men: Potential Links with Neural Reward Pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 12:39:37","doi":"10.21203/rs.3.rs-7620010/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-12T09:37:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-11T11:13:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-27T12:45:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246026706241468178567331730018007541486","date":"2025-12-19T09:16:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142988083174205509119108767628356533230","date":"2025-12-08T20:20:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-08T17:10:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T13:58:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T03:02:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-15T11:19:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe6a2583-1788-4c93-89e0-4b38c112b026","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59335476,"name":"Health sciences/Diseases"},{"id":59335477,"name":"Health sciences/Health care"},{"id":59335478,"name":"Biological sciences/Neuroscience"},{"id":59335479,"name":"Biological sciences/Psychology"},{"id":59335480,"name":"Social science/Psychology"},{"id":59335481,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-15T01:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 12:39:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7620010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7620010","identity":"rs-7620010","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}