The mediating role of personality traits and internet addiction among medical students: a mediation analysis of demographic and behavioral factors

Internet addiction (IA) has emerged as a significant public health concern, particularly among adolescents and university students. This study investigates the complex relationships between personality traits, demographic/behavioral factors, and IA severity among 1,182 medical students. Utilizing cross-sectional study, we assessed IA prevalence, administered validated scales (Chinese Internet Addiction Scale and Big Five Personality Inventory), and conducted mediation analyses to identify indirect pathways. Results revealed an 18.8% IA prevalence, with neuroticism, extraversion, and agreeableness exhibiting the strongest associations with IA severity. Mediation analyses highlighted extraversion and agreeableness as significant mediators between behavioral factors (e.g., proactive family support-seeking, social activity patterns) and IA, with agreeableness showing heightened susceptibility to demographic moderation. Sophomore students demonstrated peak vulnerability (26.8% IA rate), while team activity participation reduced IA risk (57.4% vs. 45.9%). The findings underscore the bidirectional influence of personality-environment interactions, emphasizing the need for tailored interventions that integrate intrinsic traits and contextual moderators. This study advances understanding of IA etiology by elucidating the mediating mechanisms linking personality architecture to behavioral outcomes, offering actionable insights for mitigating IA in academic populations.

1 Introduction

Internet Addiction (IA) has evolved into a pressing global public health challenge, with adolescents and student populations being disproportionately affected (Ariyadasa et al., 2022; Mboya et al., 2020). Epidemiological studies document considerable variability in IA prevalence among college students worldwide, spanning from 0.9 to 33% (Ibrahim et al., 2022; Duc et al., 2024), a disparity potentially attributable to methodological variations in assessment instruments, cultural frameworks, and differential internet accessibility across regions (Duc et al., 2024). Illustrating this geographical heterogeneity, Ethiopian undergraduates demonstrate a 31% IA prevalence rate (Mboya et al., 2020), contrasting with 7.7% clinically confirmed IA cases among Hunan Province students in China, where above 50% additionally exhibit subclinical risk profiles (Taha et al., 2019; Shen et al., 2020). Extreme manifestations include Saudi Arabian medical students showing 51.7% severe addiction rates (Shehata and Abdeldaim, 2021) and Bhutan reporting comparable prevalence trends (Tenzin et al., 2018). The COVID-19 pandemic has intensified this crisis through mandatory online education platforms (Zhang et al., 2022). These findings necessitating urgent implementation of systematic screening and tailored intervention protocols for academic communities (Khosravi et al., 2022; Örnek and Gündogmus, 2022).

The etiology of IA manifests through multifactorial determinants intersecting demographic, behavioral, and socioeconomic dimensions. Sex-specific patterns emerge distinctly, with Saudi female medical students demonstrating elevated severe IA rates compared to male counterparts(Ibrahim et al., 2022; Duc et al., 2024; Shehata and Abdeldaim, 2021), while Chinese male students exhibit heightened vulnerability mediated through anxiety disorders and self-injurious behaviors(Shen et al., 2021; Guo et al., 2024). Paradoxically, despite greater academic dependence on digital resources, medical students show reduced severe IA incidence relative to non-medical peers (Shen et al., 2020; Shehata and Abdeldaim, 2021; Khazaie et al., 2023). Socioeconomic gradients reveal protective effects from limited internet access in economically disadvantaged families (Ibrahim et al., 2022), contrasted with increased susceptibility among affluent demographics (Amano et al., 2023). Developmental vulnerabilities surface prominently among freshmen and unmarried younger students (Khazaie et al., 2023), while behavioral risk escalates with entertainment-focused usage exceeding two daily hours (e.g., gaming, social media; Mboya et al., 2020; Amano et al., 2023), though educational/informational engagement demonstrates protective qualities (Tenzin et al., 2018). Pandemic-related remote learning infrastructures have further entrenched excessive internet consumption patterns (Shehata and Abdeldaim, 2021; Zhang et al., 2022).

A bidirectional relationship exists between IA and psychological morbidity, characterized by reciprocal reinforcement mechanisms. Clinical data indicate 59.7% depression rates among IA-diagnosed students (Taha et al., 2019), alongside 2–3 fold increased risks for psychological distress (Hossin et al., 2022). Core psychobehavioral determinants include comorbid depression, anxiety disorders, impulse control deficits, and inadequate social support systems (Latifeh et al., 2022; Zhang et al., 2021; Liu and Lin, 2019). Personality architecture, particularly within Costa and McCrae’s Five-Factor Model framework, reveals critical associations: high neuroticism correlates with IA susceptibility through impaired social communication capacitie (Khosravi et al., 2022; Al-Khadher et al., 2024; Shi and Du, 2019; Koktas et al., 2024), while extraversion may predispose individuals to compensatory internet overuse as an avoidance mechanism against real-world stressors (Koktas et al., 2024; Tian et al., 2019). These dynamics underscore the multidimensional nature of IA-personality interactions, mediated through complex interplays between mental health status, lifestyle patterns, sociocultural contexts, and support network quality.

Therefore, investigating the mediating role of personality in IA is of critical scientific and practical importance. This perspective posits that various external risk factors (e.g., academic pressure, social isolation) or internal psychological distress (e.g., anxiety, depression) may not lead directly to addiction. Instead, their influence is likely channeled through specific personality dispositions (such as neuroticism), which in turn manifest as compulsive internet use. Establishing this mediating model would consolidate disparate risk factors into a coherent theoretical framework, elucidating the pathways through which specific individuals (specific personality), under certain conditions (risk exposure), develop IA via distinct psychological processes (psychological mechanism). Such a model would not only facilitate the early identification of at-risk populations through more precise psychological profiling but also pave the way for targeted, personalized interventions (for instance, emotion-regulation training for highly neurotic individuals), thereby enhancing the efficacy of preventative strategies and clinical management.

So, to elucidate these intricate relationships, the current investigation employs cross-sectional study (Li, 2023; Fryer and Vermunt, 2018; Bourchtein et al., 2017), a longitudinal analytical approach enabling identification of developmental IA trajectories and their multivariate predictors. We identified that high neuroticism, openness, extraversion and agreeableness were obvious associated with IA, but during the process, extraversion and agreeableness individuals were easily influenced by other demographic and behavioral factors, especially, the agreeableness individuals.

2 Materials and methods

2.1 Participant recruitment

The study population comprised undergraduate students from Hubei University of Medicine, Shiyan, China. Eligibility criteria required participants to demonstrate effective communication capacity and provide informed consent after comprehensive study briefing. Qualified participants subsequently completed web-based questionnaires through a secure online survey platform. This research protocol received ethical approval from the Institutional Review Board of Hubei University of Medicine (HUMIRB2022RE049), adhering strictly to the ethical principles outlined in the Declaration of Helsinki (7th revision, 2013).

From an initial pool of 1,216 submitted questionnaires, 1,182 valid responses met quality control thresholds (response rate: 97.2%), exceeding the minimum required sample size. The final cohort included 612 males (51.8%) and 570 females (48.2%), distributed across four academic years. Grade-level composition consisted of 462 freshmen (39.1%), 291 sophomores (24.6%), 270 juniors (22.8%), and 159 seniors (13.5%).

2.2 Assessment instruments

To examine the interactive roles of family environment, academic engagement, sex differences, social support systems, mental health status, lifestyle patterns, and related factors in the relationship between internet addiction (IA) and personality traits among students, we collected comprehensive participant data encompassing basic demographic information, IA characteristics, and personality profiles. Following established methodologies from prior research (Zhang et al., 2020, 2023; Pu et al., 2023; Yao et al., 2023), we developed a structured questionnaire incorporating three primary components: (1) basic personal information (sex, academic grade, social support, mental health status, lifestyles etc.), (2) IA-related factors, and (3) personality trait assessments.

Internet addiction severity was assessed using the Chinese Internet Addiction Scale (CIAS), a validated instrument that enables quantitative assessment of internet addiction disorder in both clinical and research contexts (Pu et al., 2023). This 26-item scale employs a 5-point Likert scoring system (1–5 points per item), with total scores calculated through summation of item responses. The composite scores categorize participants into three distinct groups: no obvious internet addiction (26–53 points, NIA), at potential risk of internet addiction (54–80 points, RIA), and internet addiction (81–104 points, IA) (Zhang et al., 2020). This stratification allows for objective evaluation of individual internet dependency levels and addiction potential.

Personality assessment was conducted using the Chinese Big Five Personality Inventory Brief Version (CBF-PI-B), a psychometric tool grounded in the established Big Five personality framework encompassing Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness (Yao et al., 2023). The 40-item inventory features eight items per personality dimension, utilizing a 6-point response scale ranging from 1 (“completely inconsistent”) to 6 (“completely consistent”). Notably, items 2 and 5 employ reverse scoring to enhance measurement validity. Designed for efficient personality profiling, the CBF-PI-B demonstrates particular utility in large-scale research settings requiring time-efficient administration (Zhao et al., 2022).

2.3 Methodological procedures

Following ethical approval, data collection was conducted via encrypted electronic questionnaires to ensure efficiency and data integrity (Zhang et al., 2023). Participants accessed the survey through QR code authentication, with explicit assurances of anonymity and data confidentiality. A pilot study (n = 100) preceded full implementation to optimize questionnaire clarity and technical functionality.

Statistical analyses were performed using IBM SPSS Statistics 22.0. Continuous variables were expressed as mean ± standard deviation and compared via independent samples t-tests. Categorical data were presented as percentages (%) and analyzed using chi-square tests. All statistical inferences employed two-tailed tests with α = 0.05 significance threshold.

The mediating effects of demographic and behavioral factors, personality and IA were analyzed using Process V4.0 in SPSS 22.0 and Andrew F. Hayes’ Process Model 4, which allows for sequential linkage of up to three mediators in a chain. A bootstrap method with 5,000 resampled iterations was employed to calculate the 95% confidence intervals. If the 95% confidence interval included zero, the indirect effect was deemed statistically non-significant. At the same time, the p-values less than 0.05 were considered statistically significant.

3 Results

3.1 Sample size determination and Bias mitigation

The minimum required sample size was calculated using the formula N = Z²_α/2*p(1-p)/δ², where parameters were derived from prior evidence: the prevalence of internet addiction (IA) among Chinese college students (p = 0.077) (Shen et al., 2020), a confidence level of 99% (Z_α/2 = 2.58), and a margin of error (δ = 0.05). This yielded a minimum sample size of 190. To enhance data quality, we implemented two attention-check questions and expanded sampling to account for potential invalid responses, ultimately securing 1,182 valid questionnaires for analysis. Given the questionnaire-based data collection method, we assessed potential common method bias using Harman’s single-factor test via non-rotated principal component analysis. Results indicated no dominant factor confirming the absence of significant common method bias in the dataset (Podsakoff et al., 2003). Cronbach’s alpha (=0.867), calculated using SPSS, demonstrated good internal consistency for the scale.

3.2 Participant stratification

Participants were classified into three IA categories using CIAS-R thresholds: non-addicted (NID: 26–52), at-risk (RID: 53–67), and addicted (ID: 68–104) (Shen et al., 2020; Wu et al., 2015; Ko et al., 2009). Personality dimensions were dichotomized per Costa and McCrae’s Five-Factor Model, with high/low groupings determined by median splits (cutoff = 10) across openness, conscientiousness, extraversion, agreeableness, and neuroticism.

3.3 Demographic/behavioral variables and IA correlates

Chi-square analysis revealed significant associations between demographic/behavioral factors and IA prevalence. The cohort exhibited a balanced sex distribution (male: 51.8%). While initial analysis suggested higher IA susceptibility among male students (χ² = 9.005, p = 0.011; Table 1 and Figure 1), this sex difference became non-significant when applying the standard diagnostic cutoff (score <53: χ² = 1.671, p = 0.196). Academic standing demonstrated significant associations with IA (χ² = 28.549, p < 0.001; Figure 1), showing peak prevalence among sophomores (26.8%) with gradual decline through subsequent academic years (Table 1 and Figure 2A).

Table 1

Table 1. Demographic/behavioral variables and Internet addiction.

Figure 1

Figure 1. Demographic/behavioral variables and internet addiction correlates. NID, no obvious internet addiction; RID, at potential risk of internet addiction; ID, internet addiction; FFS, Frequency of turning to family for support; ST, solo entertainment or team activities.

Figure 2

Figure 2. The prevalence trend chart of internet addiction among different groups of medical students. (A) Sophomore students showed peak vulnerability, with decreasing rates through subsequent years. (B) Proactive family support-seeking behavior reduced IA risk.

Family structure analyses indicated multi-child family students exhibited higher rates of non-internet addiction (NID: 57.1%, p = 0.043), whereas single-child family members showed elevated at-risk (RID: 30.5%) and addicted (ID: 19.7%) proportions (Table 1 and Figure 1). While frequency of family care (FFC) showed no significant IA correlation (χ² = 13.572, p = 0.094), proactive family support-seeking (FFS) behavior significantly reduced IA likelihood (χ² = 15.847, p = 0.045; Figure 1 and Figure 2B). Academic coping strategies (active problem-solving vs. procrastination) demonstrated no IA association (RAD, χ² = 2.269, p = 0.322).

Regarding recreational preferences, solitary versus social tendencies (AF) showed marginal non-significance (χ² = 5.492, p = 0.064). Team activity participation correlated with reduced IA risk (ST, 57.4% vs. 45.9%), while solitary entertainment preference predicted higher ID susceptibility (ST, 34.4% vs. 24.5%; Table 1 and Figure 1).

3.4 Personality traits and IA associations

To investigate relationships between personality dimensions and IA, participants were stratified into low/high groups across five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Chi-square analyses revealed distinct patterns of association (Table 2).

Table 2

Table 2. Personality traits of medical students and Internet addiction.

Openness demonstrated significant associations with IA severity (χ² = 13.134, p = 0.001), where high openness correlated with reduced non-addiction rates (NID) and elevated at-risk (RID) proportions. Conscientiousness showed marginal non-significance (p = 0.082), though heightened conscientiousness trended toward higher RID/ID prevalence. Extraversion exhibited the strongest statistical association (χ² = 32.624, p = 8.24 × 10⁻⁸), with high scorers showing nearly doubled ID rates compared to low scorers (22.5% vs. 13.3%).

Agreeableness displayed pronounced significance (χ² = 32.168, p = 1.04 × 10⁻⁷), revealing an inverse pattern: low agreeableness corresponded to elevated RID (14.5% vs. 26.8%) but reduced ID percentages relative to high scorers. Neuroticism emerged as the most robust predictor (χ² = 33.447, p = 5.46 × 10⁻⁸), with high neuroticism individuals exhibiting over twice the ID prevalence of their low-scoring counterparts (22.1% vs. 9.6%).

These findings position Extraversion, Agreeableness, and Neuroticism as strongly associated with IA severity, while Openness shows moderate correlations. Conscientiousness failed to reach statistical significance, suggesting limited direct involvement in IA progression within this cohort (Table 2).

3.5 Mediation analysis of personality traits

To elucidate the mediating role of personality traits in the relationship between demographic/behavioral variables and ID, we conducted mediation analyses incorporating four personality dimensions (Openness, Extraversion, Agreeableness, Neuroticism) and key covariates (sex, academic year, single-child status, proactive family support-seeking (FFS), solitary entertainment or team activity participation (ST)).

The analyses revealed no significant mediation effects for Openness or Neuroticism across all variables, as indicated by bootstrap confidence intervals encompassing zero (BootLLCI-BootULCI; Table 3). In contrast, Extraversion and Agreeableness emerged as significant mediators between behavioral patterns (FFS and ST) and IA severity (Table 3). Notably, Agreeableness demonstrated greater susceptibility to demographic/behavioral moderation, particularly through sex differences, proactive family support-seeking (FFS), solitary entertainment or team activity participation (ST) (Table 3 and Figure 3).

Table 3

Table 3. Results of indirect effect.

Figure 3

Figure 3. Extraversion and agreeableness emerged as significant mediators between demographic/behavioral patterns (sex, FFS and ST) and internet addiction severity.

These findings elucidate the complex mediation mechanisms underlying IA development, with Extraversion and Agreeableness exerting substantial indirect effects through distinct environmental and behavioral channels. The results highlight the critical mediating role of intrinsic personality characteristics and external moderators in shaping IA susceptibility.

4 Discussion

This study investigates how internet addiction (IA) characteristics and personality traits vary across demographic and behavioral variables. Our findings demonstrate that agreeableness personality exhibited greater susceptibility to demographic and behavioral influences on IA development. In contrast, those with openness and neuroticism personality traits showed relative stability against such external factors in the context of IA.

The detrimental effects of IA on collegiate academic performance and mental health are well-documented. Affected students frequently experience academic impairment through diminished time management capacity and cognitive dysfunction (Amano et al., 2023; Kumar et al., 2023). Left unaddressed, this condition may ultimately compromise career prospects and contribute to societal human resource losses (Amano et al., 2023; Anand et al., 2018). Our survey of 1,182 medical students in Shiyan, China revealed an 18.8% internet addiction (IA) prevalence rate, with 28.4% at potential risk. While these figures align with global prevalence ranges (0.9–33%, Ibrahim et al., 2022), they exceed earlier regional reports (Taha et al., 2019; Shen et al., 2020; Liu et al., 2023). These variations may stem from differences in assessment tools, socioeconomic factors, and pandemic-related influences. Notably, increased electronic device usage during COVID-19 has been associated with elevated rates of digital addiction and psychological distress (Dong et al., 2020).

Multiple contributory factors emerged from our analysis. Male students demonstrated higher IA susceptibility when categorized into normal (NID), at-risk (RID), and addicted (ID) groups, though sex differences became non-significant when using the 53-point cutoff criterion. This discrepancy suggests sex effects may manifest primarily in early-stage addiction development. Beyond sex, academic standing, peer relationships, and parental supervision significantly influenced IA incidence (Liu et al., 2023; Esen et al., 2021; Kim et al., 2022). Sophomore students showed peak vulnerability (26.8%), with decreasing rates through subsequent years, potentially reflecting academic pressure escalation in senior years (Liu et al., 2023). Social engagement patterns revealed protective effects: team activity preference correlated with lower IA risk (57.4% vs. 45.9%), while solitary entertainment predicted higher susceptibility (34.4% vs. 24.5%).

Parental supervision emerged as a complex protective factor. While non-only children showed higher NID rates (57.1%), no significant IA differences emerged across family structures. Proactive family support-seeking behavior reduced IA risk, suggesting parental influence operates through stress modulation mechanisms involving self-efficacy and self-control (Chen and Zhang, 2024). This aligns with evidence that electronic device time restrictions effectively curb IA rates (Martins et al., 2020).

Personality analysis revealed neuroticism’s strong association with IA, consistent with its established link to emotional instability and escapist internet use (Khosravi et al., 2022; Yan et al., 2014). The openness dimension presented paradoxical findings: while theoretically protective through curiosity-driven self-regulation (Kayis et al., 2016; Zhou et al., 2021), our data and other studies (Servidio, 2014; Xiao et al., 2019; Zhou et al., 2017; Miskulin et al., 2022) suggest its association with IA risk through novelty-seeking behaviors.

Although we observed significant associations between openness/neuroticism and IA severity, no significant mediation effects were found for openness/neuroticism across any of the demographic/behavioral variables. This paradoxical role of openness may be attributed to the fact that higher levels of openness were correlated with both lower rates of non-addictive internet use (NID) and higher rates of at-risk internet use (RID). Combined with the correlation analysis between sex and internet addiction presented in this study, these findings suggest that the classification methods and threshold criteria for internet addiction could influence the final outcomes. It is also plausible that these two personality traits are inherently more stable and less susceptible to the influence of demographic or behavioral factors, hence their reduced suitability as mediating variables.

Extraversion and agreeableness showed positive IA associations (Zhou et al., 2017; Miskulin et al., 2022; Öztürk et al., 2015; Atroszko et al., 2018), though these traits demonstrated greater susceptibility to external moderators like proactive family support-seeking (FFS) and social activity patterns (ST). Individuals with an extroverted personality are typically characterized by an orientation toward the external world, enthusiasm for social activities, cheerfulness, decisiveness, and a direct approach to expressing emotions, alongside considerable independence and adaptability. However, this personality type is also associated with a tendency toward impulsivity and a susceptibility to environmental influences. This suggests that family support-seeking (FFS) and positive social activity patterns (ST) may help reduce IA behaviors by mitigating the impulsivity associated with high extroversion and by shaping a more supportive environment.

Notably, agreeable individuals’ social compliance created dual effects, cooperative tendencies potentially reduced escapism, yet difficulty refusing online invitations increased usage time (Cybulska et al., 2023; Rachubinska et al., 2021). Similarly, extraversion’s social motivation paradox emerged: while physical social preference might protect against IA (Rachubinska et al., 2021), associated impulsivity could increase addiction risk (Brand et al., 2014). These findings elucidate conflicting literature regarding these traits’ protective/risk roles (Yan et al., 2014; Rachubinska et al., 2021; Brand et al., 2014; Sellbom and Bagby, 2008; McCrae and John, 1992). This was in line with our findings: Agreeableness displayed an inverse pattern: low agreeableness corresponded to elevated RID (14.5% vs. 26.8%) but reduced ID percentages relative to high scorers.

The IA-mental health relationship appears bidirectional, with depression, anxiety, stress functioning as both risk factors and consequences (Latifeh et al., 2022; Tian et al., 2019; Gupta et al., 2021). Personality-mental health interactions further complicate this relationship, such as neuroticism amplifies negative emotional responses (Liu and Lin, 2019), while extraversion correlates with positive psychological adjustment (Al-Khadher et al., 2024; Tian et al., 2019). Our results highlight the need to consider trait-environment interactions, as extraversion and agreeableness demonstrated particular sensitivity to demographic or behavioral moderators.

This investigation into the relationship and mediating effects between personality traits and internet addiction in medical students is subject to several limitations. Primarily, the cross-sectional nature of the design impedes the derivation of causal relationships, a constraint that holds even as the study identifies associated trends and factors, necessitating future research for confirmation. Furthermore, despite the implementation of measures to filter out invalid responses, the reliance on self-administered questionnaires means that bias stemming from participants’ social desirability and response rigor cannot be entirely eliminated. Furthermore, the findings and conclusions of this study are contingent on the specific sample employed. The statistical significance of the results may be subject to influences from factors such as sample size and measurement error of the variables. Therefore, these preliminary findings necessitate further verification and analysis through collaborative research with the scientific community. Nevertheless, the study retains significance for informing clinical prevention and intervention strategies and enhances our comprehension of the mediating mechanisms linking personality traits to IA.

In conclusion, this study elucidates the mediating role of demographic/behavioral factors in personality-IA relationships among medical students. Key associated variables included sex, academic year, family structure, family support dynamics, and social activity preferences. While neuroticism, openness, extraversion, and agreeableness all showed significant IA associations, the latter two traits exhibited particular susceptibility to external moderators, especially agreeableness. These findings emphasize the importance of personalized intervention strategies accounting for both inherent personality characteristics and environmental influences.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Ethics statement

This research protocol received ethical approval from the Institutional Review Board of Hubei University of Medicine (HUMIRB2022RE049). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

QZ: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Software, Writing – review & editing. LH: Conceptualization, Data curation, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing. FeiL: Conceptualization, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing – review & editing. ZW: Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. WP: Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. YL: Investigation, Methodology, Software, Supervision, Writing – review & editing. JY: Investigation, Resources, Software, Supervision, Visualization, Writing – review & editing. YZ: Investigation, Methodology, Resources, Software, Supervision, Writing – review & editing. XZ: Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing – review & editing. MZ: Writing – original draft, Methodology, Supervision, Investigation. XL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing – review & editing. FeifL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by grants from Cultivating Project for Young Scholars at Hubei University of Medicine (2020QDJZR025); Provincial Advantage Characteristic Subject Group of University of Medicine (2023PHXKQ3); Hubei Provincial Department of Education project (B2023105); Educational Research Program at Hubei University of Medicine (YJ2024033, YHJ2024005, 2024022); Soft science project at Shiyan (202406); College Students Innovation and Entrepreneurship Training Program at Hubei University of Medicine (X202110929005, X202110929007, S202310929007, YSRTP202106); Provincial Teaching Research Project of Higher Education Institutions in Hubei Province In review (2022378); Research on the Whole Process Quality Evaluation and Guarantee System of Online Courses for Master of Pharmacy Professional Degree Postgraduates (YXC2024-02-02_10). The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

The authors thank the participants for their cooperation and participation in this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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References

Al-Khadher, M. A., Alghamdi, A. A., Saraa, N., Ismael, S. A., Salaheldin, F. B., and Abdo Hasan, A.-Q. (2024). The dimensional validity and reliability of the italian smartphone addiction inventory of yemeni university students. Acta Psychol. 250:104493. doi: 10.1016/j.actpsy.2024.104493

Crossref Full Text | Google Scholar

Amano, A., Ahmed, G., Nigussie, K., Asfaw, H., Fekadu, G., Hiko, A., et al. (2023). Internet addiction and associated factors among undergraduate students of Jimma University; Jimma, south West Ethiopia, institutional based cross-sectional study. BMC Psychiatry 23:721. doi: 10.1186/s12888-023-05197-7,

PubMed Abstract | Crossref Full Text | Google Scholar

Anand, N., Jain, P. A., Prabhu, S., Thomas, C., Bhat, A., Prathyusha, P. V., et al. (2018). Internet use patterns, internet addiction, and psychological distress among engineering university students: a study from India. Indian J. Psychol. Med. 40, 458–467. doi: 10.4103/IJPSYM.IJPSYM_135_18,

PubMed Abstract | Crossref Full Text | Google Scholar

Ariyadasa, G., De Silva, C., and Gamagedara, N. S. (2022). Educational intervention for the prevention of internet addiction disorder among 15-19-year-old adolescents in Colombo district, Sri Lanka. Ceylon Med. J. 67, 131–137. doi: 10.4038/cmj.v67i4.9740,

PubMed Abstract | Crossref Full Text | Google Scholar

Atroszko, P. A., Balcerowska, J. M., Bereznowski, P., Biernatowska, A., Pallesen, S., and Schou Andreassen, C. (2018). Facebook addiction among polish undergraduate students: validity of measurement and relationship with personality and well-being. Comput. Hum. Behav. 85, 329–338. doi: 10.1016/j.chb.2018.04.001

Crossref Full Text | Google Scholar

Bourchtein, E., Langberg, J. M., Owens, J. S., Evans, S. W., and Perera, R. A. (2017). Is the positive illusory bias common in young adolescents with ADHD? A fresh look at prevalence and stability using latent profile and transition analyses. J. Abnorm. Child Psychol. 45, 1063–1075. doi: 10.1007/s10802-016-0248-3,

PubMed Abstract | Crossref Full Text | Google Scholar

Brand, M., Young, K. S., and Laier, C. (2014). Prefrontal control and internet addiction: a theoretical model and review of neuropsychological and neuroimaging findings. Front. Hum. Neurosci. 8:375. doi: 10.3389/fnhum.2014.00375,

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, M., and Zhang, X. Y. (2024). Factors influencing internet addiction among university students: the mediating roles of self-control and anxiety. Acta Psychol. 250:104535. doi: 10.1016/j.actpsy.2024.104535,

PubMed Abstract | Crossref Full Text | Google Scholar

Cybulska, A. M., Rachubinska, K., Schneider-Matyka, D., Grochans, S., and Grochans, E. (2023). Analysis of the relationship between personality traits and internet addiction. Eur. Psychiatry 66, S139–S140. doi: 10.1192/j.eurpsy.2023.350,

PubMed Abstract | Crossref Full Text | Google Scholar

Dong, H. X., Yang, F. R., Lu, X. Z., Dong, H., Yang, F., Lu, X., et al. (2020). Internet addiction and related psychological factors among children and adolescents in China during the coronavirus disease 2019 (COVID-19) epidemic. Front. Psych. 11:751. doi: 10.3389/fpsyt.2020.00751,

PubMed Abstract | Crossref Full Text | Google Scholar

Duc, T. Q., Chi, V. T. Q., Huyen, N. T. H., Quang, P. N., Thuy, B. T., and Nguyen Di, K. (2024). Growing propensity of internet addiction among Asian college students: meta-analysis of pooled prevalence from 39 studies with over 50,000 participants. Public Health 227, 250–258. doi: 10.1016/j.puhe.2023.11.040,

PubMed Abstract | Crossref Full Text | Google Scholar

Esen, P. Y., Kutlu, R., and Cihan, F. G. (2021). Internet addiction, substance use and alexithymic dimensions in two different faculties' students. Cent. Eur. J. Public Health 29, 209–216. doi: 10.21101/cejph.a6786,

PubMed Abstract | Crossref Full Text | Google Scholar

Fryer, L. K., and Vermunt, J. D. (2018). Regulating approaches to learning: testing learning strategy convergences across a year at university. Br. J. Educ. Psychol. 88, 21–41. doi: 10.1111/bjep.12169,

PubMed Abstract | Crossref Full Text | Google Scholar

Guo, X. Y., Wang, L. N., Li, Z. C., Guo, X., Wang, L., Li, Z., et al. (2024). Factors and pathways of non-suicidal self-injury in children: insights from computational causal analysis. Front. Public Health 12:1305746. doi: 10.3389/fpubh.2024.1305746,

PubMed Abstract | Crossref Full Text | Google Scholar

Gupta, R., Taneja, N., Anand, T., Gupta, A., Jha, D., and Singh, S. (2021). Internet addiction, sleep quality and depressive symptoms amongst medical students in Delhi, India. Community Ment. Health J. 57, 771–776. doi: 10.1007/s10597-020-00697-2,

PubMed Abstract | Crossref Full Text | Google Scholar

Hossin, M. Z., Islam, A., Billah, M., Haque, M., and Uddin, J. (2022). Is there a gradient in the association between internet addiction and health? PLoS One 17:e0264716. doi: 10.1371/journal.pone.0264716,

PubMed Abstract | Crossref Full Text | Google Scholar

Ibrahim, A. K., Fouad, I., Kelly, S. J., El Fawal, B., and Ahmed, G. K. (2022). Prevalence and determinants of internet addiction among medical students and its association with depression. J. Affect. Disord. 314, 94–102. doi: 10.1016/j.jad.2022.07.007,

PubMed Abstract | Crossref Full Text | Google Scholar

Kayis, A. R., Satici, S. A., Yilmaz, M. F., Şimşek, D., Ceyhan, E., and Bakioglu, F. (2016). Big five-personality trait and internet addiction: a meta-analytic review Comput. Hum. Behav. 63, 35–40. doi: 10.1016/j.chb.2016.05.012

Crossref Full Text | Google Scholar

Khazaie, H., Lebni, J. Y., Abbas, J., Mahaki, B., Chaboksavar, F., Kianipour, N., et al. (2023). Internet addiction status and related factors among medical students: a cross-sectional study in western Iran. Commun. Health Equity Res. Policy 43, 347–356. doi: 10.1177/0272684X211025438,

PubMed Abstract | Crossref Full Text | Google Scholar

Khosravi, M., Khosrobaki, M., and Foroutan, A. (2022). Personality traits and college students' internet addiction: the mediating roles of general health and self-esteem. Scand. J. Psychol. 63, 689–697. doi: 10.1111/sjop.12848,

PubMed Abstract | Crossref Full Text | Google Scholar

Kim, D., Nam, J. K., and Keum, C. (2022). Adolescent internet gaming addiction and personality characteristics by game genre. PLoS One 17:e0263645. doi: 10.1371/journal.pone.0263645,

PubMed Abstract | Crossref Full Text | Google Scholar

Ko, C. H., Yen, J. Y., Chen, S. H., Yang, M.-J., Lin, H.-C., and Yen, C.-F. (2009). Proposed diagnostic criteria and the screening and diagnosing tool of internet addiction in college students. Compr. Psychiatry 50, 378–384. doi: 10.1016/j.comppsych.2007.05.019,

PubMed Abstract | Crossref Full Text | Google Scholar

Koktas, N. C., Keskin, G., and Yigitoglu, G. T. (2024). Evaluation of internet addiction and relational variables among nursing students in Turkey during the COVID-19 pandemic. Health Educ. Behav. 51, 388–399. doi: 10.1177/10901981241230497

Crossref Full Text | Google Scholar

Kumar, T., Rajendran, V., Dutta, G., Ambwani, S., Lal, H., Ram, K., et al. (2023). Prevalence of internet addiction and impact of internet socialization on professional, academic, social lives and sleep pattern among students and professionals from various fields across India. Adv. Med. Educ. Pract. 14, 1369–1378. doi: 10.2147/AMEP.S438215,

PubMed Abstract | Crossref Full Text | Google Scholar

Latifeh, Y., Alkhatib, Y., Hmidouch, M., Sarya, S., Wael, H., Bisher, S., et al. (2022). Prevalence of internet addiction among Syrian undergraduate medical students. Medicine 101:e32261. doi: 10.1097/MD.0000000000032261

Crossref Full Text | Google Scholar

Li, G. M. (2023). A latent profile transition analysis and influencing factors of internet addiction for adolescents: a short-term longitudinal study. Heliyon 9:e14412. doi: 10.1016/j.heliyon.2023.e14412,

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, W. Q., and Lin, J. Y. (2019). The role of meditation in college students' neuroticism and mental health. Transl. Neurosci. 10, 112–117. doi: 10.1515/tnsci-2019-0019,

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, Y., Wu, N. N., Yan, J., Wu, N., Yu, J., Liao, L., et al. (2023). The relationship between health literacy and internet addiction among middle school students in Chongqing, China: a cross-sectional survey study. PLoS One 18:e0283634. doi: 10.1371/journal.pone.0283634,

PubMed Abstract | Crossref Full Text | Google Scholar

Martins, M. V., Formiga, A., Santos, C., Sousa, D., Resende, C., Campos, R., et al. (2020). Adolescent internet addiction - role of parental control and adolescent behaviours. Int. J. Pediatr. Adolesc. Med. 7, 116–120. doi: 10.1016/j.ijpam.2019.12.003,

PubMed Abstract | Crossref Full Text | Google Scholar

Mboya, I. B., Leyaro, B. J., Kongo, A., Mkombe, C., Kyando, E., and George, J. (2020). Internet addiction and associated factors among medical and allied health sciences students in northern Tanzania: a cross-sectional study. BMC Psychol. 8:73. doi: 10.1186/s40359-020-00439-9,

PubMed Abstract | Crossref Full Text | Google Scholar

McCrae, R. R., and John, O. P. (1992). An introduction to the five-factor model and its applications. J. Pers. 60, 175–215. doi: 10.1111/j.1467-6494.1992.tb00970.x,

PubMed Abstract | Crossref Full Text | Google Scholar

Miskulin, I., Simic, I., Pavlovic, N., Kovacevic, J., Fotez, I., Kondza, G., et al. (2022). Personality traits of Croatian university students with internet addiction. Behav. Sci. 12:173. doi: 10.3390/bs12060173,

PubMed Abstract | Crossref Full Text | Google Scholar

Örnek, B. Y., and Gündogmus, I. (2022). The effects of smartphone and internet gaming addiction on eating attitudes among university students. Psychiat Invest 19, 1–8. doi: 10.30773/pi.2021.0230,

PubMed Abstract | Crossref Full Text | Google Scholar

Öztürk, C., Bektas, M., Ayar, D., Özgüven Öztornacı, B., and Yağcı, D. (2015). Association of personality traits and risk of internet addiction in adolescents. Asian Nurs. Res. 9, 120–124. doi: 10.1016/j.anr.2015.01.001,

PubMed Abstract | Crossref Full Text | Google Scholar

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., and Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903. doi: 10.1037/0021-9010.88.5.879,

PubMed Abstract | Crossref Full Text | Google Scholar

Pu, Y., Liu, Y. T., Qi, Y. W., Liu, Y., Qi, Y., Yan, Z., et al. (2023). Five-week of solution-focused group counseling successfully reduces internet addiction among college students: a pilot study. J. Behav. Addict. 12, 964–971. doi: 10.1556/2006.2023.00064,

PubMed Abstract | Crossref Full Text | Google Scholar

Rachubinska, K., Cybulska, A., Szkup, M., and Grochans, E. (2021). Analysis of the relationship between personality traits and internet addiction. Eur. Rev. Med. Pharm. 25, 2591–2599. doi: 10.26355/eurrev_202103_25422

Crossref Full Text | Google Scholar

Sellbom, M., and Bagby, R. M. (2008). The validity and utility of the positive presentation management and negative presentation management scales for the revised NEO personality inventory. Assessment 15, 165–176. doi: 10.1177/1073191107310301,

PubMed Abstract | Crossref Full Text | Google Scholar

Servidio, R. (2014). Exploring the effects of demographic factors, internet usage and personality traits on internet addiction in a sample of Italian university students. Comput. Human Behav. 35, 85–92. doi: 10.1016/j.chb.2014.02.024

Crossref Full Text | Google Scholar

Shehata, W. M., and Abdeldaim, D. E. (2021). Internet addiction among medical and non-medical students during covid-19 pandemic, Tanta University, Egypt. Environ. Sci. Pollut. Res. 28, 59945–59952. doi: 10.1007/s11356-021-14961-9,

PubMed Abstract | Crossref Full Text | Google Scholar

Shen, Y, Meng, F, Xu, H, Fanchao, M, Huiming, X, Xia, L, et al. 2020. Internet addiction among college students in a Chinese population: prevalence, correlates, and its relationship with suicide attempts. Depress Anxiety 37: 812–821. doi: 10.1002/da.23036

Crossref Full Text | Google Scholar

Shen, Y. M., Wang, L., Huang, C. X., Guo, J., de Leon, S. A., Lu, J. P., et al. (2021). Sex differences in prevalence, risk factors and clinical correlates of internet addiction among Chinese college students. J. Affect. Disord. 279, 680–686. doi: 10.1016/j.jad.2020.10.054,

PubMed Abstract | Crossref Full Text | Google Scholar

Shi, M., and Du, T. J. (2019). Associations of personality traits with internet addiction in Chinese medical students: the mediating role of attention-deficit/hyperactivity disorder symptoms. BMC Psychiatry 19:183. doi: 10.1186/s12888-019-2173-9,

PubMed Abstract | Crossref Full Text | Google Scholar

Taha, M. H., Shehzad, K., Alamro, A. S., and Wadi, M. (2019). Internet use and addiction among medical students in Qassim university, Saudi Arabia. Sultan Qaboos Univ. Med. J. 19, e142–e147. doi: 10.18295/squmj.2019.19.02.010,

PubMed Abstract | Crossref Full Text | Google Scholar

Tenzin, K., Dorji, T., Gurung, M. S., Dorji, P., Tamang, S., Pradhan, U., et al. (2018). Prevalence of internet addiction and associated psychological co-morbidities among college students in Bhutan. Indian J. Public Health 56, 558–564. doi: 10.4103/0019-557X.157531,

PubMed Abstract | Crossref Full Text | Google Scholar

Tian, L. L., Jiang, S. Y., and Huebner, E. S. (2019). The big two personality traits and adolescents' complete mental health: the mediation role of perceived school stress. School Psychol. 34, 32–42. doi: 10.1037/spq0000257,

PubMed Abstract | Crossref Full Text | Google Scholar

Wu, C. Y., Lee, M. B., Liao, S. C., and Chang, L.-R. (2015). Risk factors of internet addiction among internet users: an online questionnaire survey. PLoS One 10:e0137506. doi: 10.1371/journal.pone.0137506,

PubMed Abstract | Crossref Full Text | Google Scholar

Xiao, J. L., Li, D. P., Jia, J. C., Wang, Y., Sun, W., and Li, D. (2019). The role of stressful life events and the big five personality traits in adolescent trajectories of problematic internet use. Psychol. Addict. Behav. 33, 360–370. doi: 10.1037/adb0000466,

PubMed Abstract | Crossref Full Text | Google Scholar

Yan, W., Li, Y., and Sui, N. (2014). The relationship between recent stressful life events, personality traits, perceived family functioning and internet addiction among college students. Stress. Health 30, 3–11. doi: 10.1002/smi.2490,

PubMed Abstract | Crossref Full Text | Google Scholar

Yao, S., Xu, M., and Sun, L. (2023). Five-factor personality dimensions mediated the relationship between parents' parenting style differences and mental health among medical university students. Int. J. Environ. Res. Public Health 20:4908. doi: 10.3390/ijerph20064908,

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, Z. H., Lin, Y., Liu, J., Zhang, Z., Zhang, G., Hou, X., et al. (2022). Relationship between behavioral inhibition/activation system and internet addiction among Chinese college students: the mediating effects of intolerance of uncertainty and self-control and gender differences. Front. Public Health 10:1047036. doi: 10.3389/fpubh.2022.1047036,

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, Y., Liu, Z., and Zhao, Y. H. (2021). Impulsivity, social support and depression are associated with latent profiles of internet addiction among male college freshmen. Front. Psych. 12:642914. doi: 10.3389/fpsyt.2021.642914,

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, X. H., Shi, X. X., Xu, S. W., Zhang, X., Shi, X., Xu, S., et al. (2020). The effect of solution-focused group counseling intervention on college students' internet addiction: a pilot study. Int. J. Environ. Res. Public Health 17:2519. doi: 10.3390/ijerph17072519,

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, Q., Yuan, Q. Z., Ma, P. Q., Li, Y., Zhao, M.-h., Chen, R.-x., et al. (2023). Perceptions towards online learning among medical students during the covid-19 pandemic. Heliyon 9:e13119. doi: 10.1016/j.heliyon.2023.e13119,

PubMed Abstract | Crossref Full Text | Google Scholar

Zhao, H. F., Shi, H., Ren, Z., Zhao, H., He, M., Li, X., et al. (2022). Gender and age differences in the associations between personality traits and depressive symptoms among Chinese adults: based on China family panel study. Health Soc. Care Community 30:E5482-E5494. doi: 10.1111/hsc.13972,

PubMed Abstract | Crossref Full Text | Google Scholar

Zhou, Y., Li, H., Han, L., and Yin, S. (2021). Relationship between big five personality and pathological internet use: mediating effects of loneliness and depression. Front. Psychol. 12:739981. doi: 10.3389/fpsyg.2021.739981,

PubMed Abstract | Crossref Full Text | Google Scholar

Zhou, Y. Y., Li, D. P., Li, X., Wang, Y., and Zhao, L. (2017). Big five personality and adolescent internet addiction: the mediating role of coping style. Addict. Behav. 64, 42–48. doi: 10.1016/j.addbeh.2016.08.009,

PubMed Abstract | Crossref Full Text | Google Scholar