Problematic Online Behaviours among University Students and Associations with Psychological Distress Symptoms and Emotional Role Limitations: A Network Analysis Approach

Vol.:(0123456789) International Journal of Mental Health and Addiction https://doi.org/10.1007/s11469-024-01296-y 1 3 ORIGINAL ARTICLE Problematic Online Behaviours amongUniversity Students andAssociations withPsychological Distress Symptoms andEmotional Role Limitations: ANetwork Analysis Approach MagdalenaSánchez‑Fernández1 · MercedesBorda‑Mas1 · FranciscoRivera2 · MarkD.Griffiths3 Accepted: 30 March 2024 © The Author(s) 2024 Abstract Very little research has simultaneously explored the interactions between generalized problematic internet use (GPIU), problematic social media use (PSMU), problematic online gaming (POG), psychological distress, and emotional well-being among university students. Therefore, the present study aimed to determine (i) the associations between GPIU, PSMU, and POG symptoms, (ii) whether symptoms of these three problematic online behaviours form distinct entities, and (iii) whether there are associations between problematic online behaviours, psychological distress symptoms, and emotional role limitations using network analysis. A total of 807 Spanish university students participated (57.7% female; Mage = 21.22years [SD = 3.68]). Two network models were computed. Network 1 showed a complex interaction of nodes, with particularly strong connections between analogous symptoms of GPIU and PSMU. Symptoms organised into distinct dimensions, featuring a unique dimension for POG symptoms, one that includes preoccupation and a conflict symptom of GPIU, and two other dimensions with symptoms of GPIU and PSMU. Network 2 showed significant connections between GPIU and depression, GPIU and emotional role limitations, PSMU and anxiety, PSMU and emotional role limitations, POG and depression, and POG and anxiety. The findings support the conceptualization of GPIU as a nonspecific disorder, the independence of PSMU and POG as distinct constructs, and aligning with perspectives that separate POG from the GPIU spectrum. The study reinforces the model of compensatory internet use and emphasizes the impact of problematic online behaviours on emotional well-being. The findings have practical implications for the assessment and intervention of problematic online behaviours. Keywords Problematic online behaviours· Psychological distress· Emotional well-being· College students· Network analysis Extended author information available on the last page of the article International Journal of Mental Health and Addiction 1 3 Introduction During the past two decades, there has been a marked increase in the number of internet users alongside the democratisation of internet access. While the internet has brought numerous advantages to society, for a small minority of users, its problematic use has emerged as a significant public health concern (World Health Organization, 2015). Such problematic use has become evident through a range of potentially problematic online activities such as online gaming, online gambling, social media use, online shopping, and online pornography use (Hussain & Starcevic, 2020; Lopez-Fernandez etal., 2016; Mauer-Vakil & Bahji, 2020; Mora-Salgueiro etal., 2021; Müller etal., 2021). University students are a population of special interest with regard to these problems because they are ‘digital natives’, who have grown up surrounded by technology and have never known a world without the internet (Anderson etal., 2017), and need such technology for their optimal academic, personal, and social development (Sánchez-Caballé etal., 2020; Zhao etal., 2021). Previous research has reported a clear association between problematic use of online activities and health and functional impairment among students (e.g., Chang etal., 2022; Kwok etal., 2021; Wong etal., 2020). Regarding the diagnostic criteria that have described these problem behaviours, Griffiths (2005) proposed a biopsychosocial model of addiction, common to substance and behavioural addictions (such as internet addiction), based on six components: salience, mood modification, tolerance, withdrawal, relapse, and conflict. This model has been tested not only for generalized problematic internet behaviour (Meerkerk etal., 2009), but also for specific behaviours such as social media use (Andreassen etal., 2012) and online videogame playing (Demetrovics etal., 2012). More recently, applicable only to problematic behaviour associated with videogame playing, the latest (fifth) edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013) included within the section Emerging Measures and Models (Section III), ‘internet gaming disorder’ (IGD), with nine diagnostic criteria adapted from those used for substance use disorders: preoccupation, withdrawal, tolerance, loss of control, loss of previous interests, continuation despite problems, deception, mood modification, and jeopardization. Similarly, the 11th revision of the International Classification of Diseases (ICD-11; World Health Organization, 2018) included ‘gaming disorder’ (GD), and proposed in its description the criteria of salience, loss of control, losing interest in and reducing other recreational activities, continuation of the playing behaviour despite negative consequences, and risking/losing relationships and opportunities. Although there is no official diagnostic recognition for other online problem behaviours (apart from ‘gambling disorder’ which can occur both online and/or offline), current research has focused on broadening the understanding of the nature and scope of these behaviours and determining whether they represent separate psychopathological conditions that merit further investigation and consideration in clinical practice (Baggio etal., 2022). In terms of conceptualising these behaviours, much of the literature has used terms such as ‘internet addiction’, ‘compulsive internet use’ and ‘problematic internet use’ to refer to maladaptive online behaviour manifested through different problematic online behaviours associated with different activities (Fineberg etal., 2018). However, this conception has been subject to criticism supported by the idea that the internet is only a means to access different online activities (Griffiths, 2000; Meerkerk etal., 2009) and that online activities are not substituted for each other when individuals cannot perform their favourite activity International Journal of Mental Health and Addiction 1 3 (Griffiths & Szabo, 2014; Pontes etal., 2015). As a possible solution to these criticisms, the spectrum hypothesis argues that problematic online behaviours can be defined as a spectrum of related but distinctive behaviours associated with common and specific aetiological factors (Billieux, 2012; Starcevic & Billieux, 2017). Previous research has supported this last conceptualisation of problematic online behaviours, demonstrating these behaviours are correlated and the magnitude of this correlation is more pronounced between specific problematic behaviours (e.g., problematic social media use [PSMU] or problematic online gaming [POG]) and generalized problematic internet use (GPIU) than it is between individual behaviours themselves (Rigó etal., 2023; Sánchez-Fernández & Borda-Mas, 2024; Van Rooij etal., 2017). Moreover, studies have found similarities and differences in the impact of specific risk factors on different problematic online behaviours (Akbari etal., 2023; Chang etal., 2022; Naidu etal., 2023; Peris etal., 2020; Sayili etal., 2023; Van Rooij etal., 2017). An effective way to test the spectrum hypothesis is through the network analysis approach. In this approach, disorders are considered complex networks where symptoms are represented as ‘nodes’ connected by ‘edges’ (Borsboom, 2017; Schmittmann etal., 2013). The strength of these connections reflects the probability that symptoms appear together, identifying the core and peripheral symptoms. This approach captures the complexity and dynamics of disorders and allows the analysis of relationships between them. Unlike the latent variable approach, where an underlying factor is assumed, network analysis considers the network itself as the main construct, exploring dynamic causal relationships without assumptions of local independence (Guyon etal., 2017). It also facilitates the identification of ‘bridging symptoms’ that connect seemingly distinct disorders (Baggio etal., 2016; Cramer etal., 2010), and therefore provides evidence for the spectrum hypothesis. Following this approach, previous studies have analysed the relationships between symptoms of different problematic online behaviours and found that these form distinct entities (e.g., Baggio etal., 2018, 2022; Li etal., 2023b; Rozgonjuk etal., 2021; Zarate etal., 2022). However, to date, no studies have examined the relationship between GPIU, PSMU, and POG symptoms among the university student population using the network analysis approach. It is essential to advance research specifically within this population and these three behaviours to enable the development of targeted interventions. In addition, this approach has been commonly used in recent research demonstrating the relationship between problematic online behaviours and various symptoms of psychological distress. For example, some studies showed that GPIU was related to depression symptoms (Cai etal., 2022; Zhao etal., 2023). Relationships have been reported between GPIU and suicidal ideation (Yang etal., 2023), and between GPIU and anxiety (Cai etal., 2021). Specific studies have shown that depression is strongly associated with PSMU and POG (Li etal., 2023b; Sit etal., 2023). Other relationships have been reported between PSMU and psychological distress (Peng & Liao, 2023; Tullett-Prado etal., 2023; Wang etal., 2022), and between PSMU and different psychopathological symptoms (Fournier et al., 2023). Focusing on the university student population, significant relationships have been reported between gaming disorder, depression, alexithymia, boredom and loneliness (Li etal., 2021), and between gaming disorder, rumination, and sleep quality (Li etal., 2023a, b). However, to date, no previous study has analysed the relationships between different problematic online behaviours and psychological distress symptoms in the same network, which may have important implications for the treatment of these behaviours. If significant relationships are established, changes in these psychological distress variables can activate and/or inhibit problem behaviours in the network (Borsboom, 2017). Furthermore, although PIU has been shown to have a negative impact on well-being (Dienlin & International Journal of Mental Health and Addiction 1 3 Johannes, 2022; Machimbarrena etal., 2019), no previous studies have examined the relationship between these variables using network analysis. The Present Study Based on the spectrum hypothesis and previous empirical evidence, the objectives of the present study were to survey a sample of university students and to determine (i) to what extent symptoms of online problem behaviours, such as GPIU, PSMU and POG, are associated with each other; (ii) whether symptoms of these three problematic online behaviours form distinct entities; and (iii) whether there is a relationship between problematic online behaviours, symptoms of psychological distress (i.e., stress, anxiety, depression), and an indicator of well-being (i.e., emotional role limitations). With regard to these objectives, the following hypotheses (Hs) were proposed. It was hypothesised that: • The symptoms of GPIU, PSMU, and POG would be positively associated with each other (H1). • The symptoms of PSMU and POG would form distinct clusters of problematic online behaviour symptoms, and the umbrella constructs of GPIU would not constitute a specific disorder (H2). • GPIU, PSMU, and POG would be positively associated with depression, anxiety, stress and emotional role limitations (H3). Method andMaterials Participants andProcedure Between October 2022 and May 2023, a cross-sectional survey study was conducted at a university in Andalusia, Spain. University students were recruited using convenience sampling. The surveys were distributed online by the university’s teaching staff. The inclusion criteria were: (i) being older than 17years, (ii) being enrolled in a degree programme at this university, (iii) having a smartphone or any other device with internet access, and (iv) giving informed consent to participate. Before completion of the survey, the participants were informed about the background and purpose of the study, along with instructions on an information sheet. No credit or course remuneration was given for participation. The study was approved by the Ethics Committee of the university of the first three authors and adhered to the Declaration of Helsinki. A total of 807 students (330 males, 466 females, and 11 nonbinaries) participated in the survey. The mean age of the students was 21.22years (SD = 3.68, range = 17–41years). Table1 shows the characteristics of the participants. The sample comprised a higher proportion of females, bachelor students, those under 20years of age, engineering and architecture students, those with a medium economic level, and those living with a family member. Measures In addition to the sociodemographic information shown in Table1, the following psychometric measures were included in the survey. International Journal of Mental Health and Addiction 1 3 Compulsive Internet Use Scale (CIUS‑14) The CIUS-14 (Meerkerk etal., 2009; Spanish version: Lopez-Fernandez etal., 2019) was used to assess GPIU. The scale comprises 14 items focussing on lack of control, intrapersonal and interpersonal conflicts, cognitive and behavioural preoccupation, impaired mood, and withdrawal symptoms. Items (e.g., “Do you think you should use the internet less often?”) are responded to on a five-point Likert scale from 0 (never) to 4 (very frequently). Higher scores refer to a greater severity of GPIU. Items and associated symptoms can be found in TableS1 of the Supplementary Material. The CIUS-14 has been translated into many languages and has used in cross-cultural research showing robust psychometric qualities (Lopez-Fernandez etal., 2019). In the present study, CIUS-14 showed high levels of internal consistency (α = 0.89, ω = 0.89) and an adequate fit (χ2 = 393.12, df = 76; χ2/ df = 5.17; CFI = 0.98; IFI = 0.98; NFI = 0.97; TLI = 0.97; RMSEA = 0.07). Table 1 Characteristics of the study participants (N = 807) a Levels determined based on an item ("Considering your household income level") with three response options: “We struggle to make ends meet or barely manage without additional expenses” (low), “We live comfortably but without luxuries” (medium), and “We are financially comfortable” (high) Characteristics Frequency % Gender  Male  Female  Non-binary 330 466 11 40.9 57.7 1.4 Age (years) < 20  20–22  22–24  > 24 313 305 93 96 38.8 37.8 11.5 11.9 Educational degree (currently enrolled)  Bachelor  Master  Doctoral 720 58 29 89.2 7.2 3.6 Field of knowledge  Sciences  Sciences Health sciences  Social sciences  Arts and humanities  Engineering and Architecture 130 124 196 88 269 16.1 15.4 24.3 10.9 33.3 Income levela  Low  Medium  High 174 385 248 21.5 47.7 30.7 Residence  With a family member  In a student residence/flat  With a couple/alone 458 287 622 56.6 35.8 7.6 International Journal of Mental Health and Addiction 1 3 Bergen Social Media Addiction Scale (BSMAS) The BSMAS (Andreassen etal., 2016; Spanish version: Vallejos-Flores etal., 2018) was used to assess PSMU over the past year. The scale comprises six items reflecting core addiction elements (i.e., salience, mood modification, tolerance, withdrawal, conflict, and relapse) proposed by Griffiths (2005). Items (e.g., “Do you feel an urge to use social media more and more?”) are responded to on a five-point Likert scale ranging from 1 (very rarely) to 5 (very often). Higher scores refer to a greater severity of PSMU. Items and associated symptoms can be found in TableS1 of the Supplementary Material. Its robust psychometric properties have been verified in different language versions (e.g., Andreassen etal., 2016; Monacis etal., 2017; Pontes etal., 2016; Zarate etal., 2023). In the present study, BSMAS showed high levels of internal consistency (α = 0.81, ω = 0.81) and an adequate fit (χ2 = 15.70, df = 8; χ2/df = 1.96; CFI = 1.00; IFI = 1.00; NFI = 0.99; TLI = 0.99; RMSEA = 0.03). Internet Gaming Disorder Scale–Short Form (IGDS9‑SF) The IGDS9-SF (Pontes & Griffiths, 2015; Spanish version: Beranuy etal., 2020) was used to assess POG over the past year. The scale comprises nine items based on the criteria for internet gaming disorder in the DSM-5 (American Psychiatric Association, 2013). Items (e.g., “Do you feel preoccupied with your gaming behavior?”) are responded to on a fivepoint Likert scale from 1 (never) to 5 (very often). Higher scores refer to a higher severity of POG. Items and associated symptoms can be found in TableS1 of the Supplementary Material. The IGDS9-SF has been found to have excellent psychometric properties in different languages (Poon etal., 2021). The Spanish IGDS9-SF has been shown to have robust psychometric properties (Beranuy etal., 2020; Maldonado-Murciano etal., 2020; Sánchez-Iglesias etal., 2020). In the present study, IGDS9-SF showed high levels of internal consistency (α = 0.87, ω = 0.87) and an adequate fit (χ2 = 76.16, df = 27; χ2/df = 2.82; CFI = 1.00; IFI = 1.00; NFI = 0.99; TLI = 0.99; RMSEA = 0.05). Depression, Anxiety, andStress Scale‑21 (DASS‑21) The DASS-21 (Lovibond & Lovibond, 1995; Spanish version: Daza et al., 2002) was used to assess psychological distress. The 21-item scale comprises three subscales, each containing seven items related to depression (DASS-D), anxiety (DASS-A), and stress (DASS-S). Items (e.g., “I felt that life was meaningless”) are responded to on a four-point scale from 0 (did not apply to me at all) to 3 (applied to me very much, or most of the time). A higher score indicates greater symptoms of psychological distress. The Spanish DASS-21 has been shown to have robust psychometric properties (Daza etal., 2002). In the present study, the three DASS-21 subscales showed high levels of internal consistency (depression: α = 0.90, ω = 0.90; anxiety: α = 0.88, ω = 0.88; stress: α = 0.88, ω = 0.88) and an adequate fit (depression: χ2 = 27.69, df = 14, χ2 / df = 1.98, CFI = 1.00; IFI = 1.00; NFI = 1.00; TLI = 1.00; RMSEA = 0.04; anxiety: χ2 = 14.41, df = 14; χ2/df = 1.03; CFI = 1.00; IFI = 1.00; NFI = 1.00; TLI = 1.00; RMSEA = 0.03; stress: χ2 = 23.10, df = 14; χ2/df = 1.65; CFI = 1.00; IFI = 1.00; NFI = 0.99; TLI = 1.00; RMSEA = 0.04). International Journal of Mental Health and Addiction 1 3 Short Form 36 Health Survey (SF‑36) (Ware andSherbourne, 1992) The SF-36 (Ware and Sherbourne, 1992; Spanish version: Vilagut etal., 2005) comprises 36 items and assesses physical and mental health. In the present study, the three-item emotional role limitations (ER) subscale was used. Items (e.g., “Accomplished less than you would like”) are responded to on a five-point scale from 1(strongly disagree) to 5(strongly agree). A higher score indicates a higher level of emotional role limitations. The psychometric properties of SF-36 has been found to have robust psychometric properties among general populations (Su etal., 2014), university students (Zhang etal., 2012), and Spanish populations (Giraldo-Rodríguez and López-Ortega, 2024). In the present study, the ER subscale of the SF-36 showed high levels of internal consistency (α = 0.92, ω = 0.93) and an adequate fit (χ2 = 393.12, df = 76; χ2/df = 5.17; CFI = 1.00; IFI = 1.00; NFI = 1.00; TLI = 1.00; RMSEA = 0.04). Assessment criteria Table2 shows the criteria used in the assessment of problematic online behaviours (GPIU, PSMU, and POG). Parallels can be seen between the first five criteria (i.e., loss of control relapse, conflict/jeopardization, preoccupation/salience, mood modification, and withdrawal) in all three cases and between the first six criteria (the previous five and the addition of tolerance) in the case of PSMU and POG. The scale used to assess POG includes three further criteria (i.e., loss of interests, continuation despite problems, and deception). Statistical Analysis Descriptive and bivariate data analysis were performed in Jeffreys’ Amazing Statistics Program (JASP) version 0.17.1 (Intel) statistical software (JASP Team, 2023). Network data analysis was performed with R version 4.3.0 (R Core Team, 2023). For the distribution of variables, the absolute values of skewness ranged from 0.24 (ER) to 2.69 (POG), and the kurtosis values of kurtosis ranged from 0.16 (GPIU) to 9.38 (POG). Given the Table 2 Assessment tools and criteria used for generalized problematic internet use, problematic social media use and problematic online gaming GPIU: generalized problematic internet use, PSMU: problematic social media use, POG: problematic online gaming GPIU PSMU POG Compulsive Internet Use Scale (CIUS-14) Bergen Social Media Addiction Scale (BSMAS) Internet Gaming Disorder Scale–Short Form (IGDS9-SF) Loss of control Relapse Loss of control Conflict Conflict Jeopardization Preoccupation Salience Preoccupation Coping or mood modification Mood modification Mood modification Withdrawal Withdrawal Withdrawal Tolerance Tolerance Loss of interests Continuation despite problems Deception International Journal of Mental Health and Addiction 1 3 criteria of absolute skewness ≤ 2.0 and absolute kurtosis ≤ 7.0 (Kim, 2013), the distribution can be considered normal for all variables except for POG. First, means, standard deviations, range, reliabilities (Cronbach’s alphas and McDonald’s omegas), confirmatory factor analysis (CFA), and correlations between study variables were calculated. Multiple criteria were used to evaluate the CFA fit: χ2/df ≤ 2 (Cole, 1987), the comparative fit index (CFI) ≥ 0.90, an incremental fit index (IFI) ≥ 0.90, normed fit index (NFI) ≥ 0.90, Tucker Lewis index (TLI) ≥ 0.90, and the root-mean-square error of approximation (RMSEA) < 0.08 (Kline, 2015). As χ2 is a sensitive sample size, the interpretation of the fit of the model was based on an overall assessment of the general pattern of all fit indices (Alavi etal., 2020). Pearson’s correlations were used for all variables except POG, which used Spearman’s correlation. The following cut-off points were used to interpret the strength of the associations: small < 0.3, medium > 0.3, and large > 0.5 (Hemphill, 2003). To examine the relationships between the problematic online behaviours studied, a network model was calculated with the 14 GPIU symptoms present in CIUS-14, the six PSMU symptoms present in BSMAS, and the nine POG symptoms present in IGDS9-SF (Network 1). To examine the relationship between GPIU, PSMU, and POG, psychological distress, and emotional role limitations, a second network model was generated (Network 2). To simplify the model, the sum scores of CIUS-14, BSMAS, IGDS9-SF, three DASS subscales and the ER subscale of SF-36 were used instead of the symptoms scores. The qgraph package (Epskamp etal., 2023) was applied to network visualisation and analysis. A Gaussian graphical model was computed using Least Absolute Shrinkage and Selection Operator (GLASSO) based on the Extended Bayesian Information Criterion (EBIC). GLASSO reduces spurious connections between nodes (symptoms in Network 1 and variables in Network 2) by minimising small correlations to zero. The EBIC is a goodness of fit metric for model selection, which is adjusted by a hyperparameter γ. This hyperparameter was set to 0.5 to achieve an optimal balance between sensitivity and specificity (Foygel & Drton, 2010). The ‘Exclude pairwise method’ was used to manage missing data. Network visualisation followed the Fruchterman–Reingold algorithm (Fruchterman & Reingold, 1991). Symptoms (Network 1) and variables (Network 2) were represented by ‘nodes’ and the relationships between them by ‘edges’. Blue edges indicate positive links, while red edges indicate negative links. Moreover, the strength of the link between nodes is presented through the thickness and colour density of the connecting edge, where thicker and denser lines indicate stronger weights. In addition, the distance between the nodes showed the relationship between them. Finally, the centre of the network holds the nodes with higher correlations, whereas the edges of the network have the nodes with lower correlations. In addition, edge weights and centrality of nodes were used to describe the network. The strength of the relationship between the nodes is indicated by an edge weight. The minimum absolute value of the edge weight of 0.03 is deemed interpretable (Isvoranu etal., 2017). Centrality describes the relative significance of the various nodes within a network. A central node is closely related to other nodes, and, when activated, other symptoms are likely to be affected as well. On the other hand, a low centrality symptom has fewer links to other nodes and less impact on the network. Strength, betweenness, closeness, and expected influence are frequently cited measures of centrality (Opsahl etal., 2010). Following this, the edge accuracy and the stability of the centrality coefficients of Network 1 were evaluated using the bootnet package (Epskamp, 2023). The accuracy of edge weights was examined using bootstrap 95% non-parametric confidence intervals (CIs). Narrower CIs imply a more accurate estimate of the edge (Epskamp etal., 2018). The stability of the centrality indices was estimated using case-dropping bootstrapping (Epskamp International Journal of Mental Health and Addiction 1 3 & Fried, 2018). This coefficient reflects the correlation between the original centrality indices (based on the full data) and the correlation obtained from the subset of data representing different percentages of the overall sample. Epskamp etal. (2018) suggested that the correlation stability coefficient should not be below 0.25, and preferably it should be above 0.5. In the present study, the stability of the centrality indices and the edge accuracy of the network were examined using the aforementioned procedures. Both procedures were estimated with 1000 bootstraps. To investigate the communities between the different symptoms of PIU, exploratory graph analysis (EGA) (Golino and Epskamp, 2017) was used. This network analytic approach allows the implementation of a community detection algorithm, facilitating the empirical identification of clusters in multidimensional data (Christensen, 2020). The Louvain community detection algorithm was used since it has been shown to perform well with ordinal data (Christensen, 2020). TheEGAnetpackage (Golino & Christensen, 2023) was used to replicate EGA networks 1,000 times with random sample permutation. The qgraph packagewas used to visualise the median EGA network. Finally, to examine the bridge centrality between PIU symptoms, the networktools package (Jones, 2023)was used to estimate the bridge strength and bridge expected influence (both 1-step and 2-step). Results Descriptive Statistics Table3 presents descriptive statistics and bivariate correlations between the psychological variables. GPIU, PSMU, and POG were positively correlated, with a very strong correlation between GPIU and PSMU, a medium correlation between GPIU and POG, and a small correlation between PSMU and POG. GPIU and PSMU showed positive medium correlations with all three subscales of psychological distress and with emotional role limitations. Table 3 Means, standard deviations (SDs) and Pearson/Spearman correlations of the study variables (N = 807) GPIU: generalized problematic Internet use, PSMU: problematic social media use, POG: problematic online gaming, Dep: depression, Anx: anxiety, Str: stress, ER: emotional role functioning, α: Cronbach’s alpha coefficient, ω: omega coefficient, M: mean, SD: standard deviation, Rg: range. *p < 0.05 **p < 0.01 ***p < 0.001 (2-tailed) Variable 1 2 3 4 5 6 7 1. GPIU — 2. PSMU 0.72*** — 3. POG 0.36*** 0.21*** — 4. Dep 0.38*** 0.33*** 0.16*** — 5. Anx 0.33*** 0.37*** 0.08* 0.68*** — 6. Str 0.32*** 0.34*** 0.07* 0.71*** 0.81*** — 7. ER 0.38*** 0.38*** 0.06 0.60*** 0.57*** 0.58*** — M17.67 11.66 11.61 5.99 5.94 8.23 8.23 SD 9.85 4.68 4.44 5.07 5.14 4.90 4.03 Rg 0 – 52 6 – 30 9—42 0—21 0—21 0—21 3—15 International Journal of Mental Health and Addiction 1 3 On the other hand, negative connections were found between the POG items and some GPIU and PSMU items. The more pronounced connection between GPIU and PSMU, compared to these two forms of PIU and POG, could indicate that, despite the presence of shared elements between these problematic behaviours (Brand etal., 2019; Epskamp etal., 2018), there is some variability between them. This assertion is further supported by H2. Regarding the associations between the different symptoms of each of the disorders, the strongest connections were established between the symptoms of loss control/relapse and conflict, and between the symptoms of withdrawal and mood modification in GPIU and PSMU, between the symptoms of salience and tolerance in PSMU, and between withdrawal and tolerance, preoccupation and withdrawal, loss of control and loss of previous interests, loss of previous interests and deception, and preoccupation and loss of control in POG. These findings suggest that there are greater similarities between GPIU and PSMU than between them and POG. On the other hand, the assessment of centrality indices showed the relative importance of the different symptoms of GPIU, PSMU, and POG within the broader network of the three analysed behaviours (Epskamp etal., 2018). Symptoms with more frequent connections and shorter trajectories included: “Use the internet to escape from your sorrows or get relief from negative feelings” (coping or mood modification), “Feel restless, frustrated, or irritated when you cannot use the internet” (withdrawal), “Look forward to your next internet session” (preoccupation) in GPIU, “Tried to cut down on the use of social media without success” (relapse) and “Become restless or troubled if you have been prohibited from using social media” (withdrawal) in PSMU, and “Continued your gaming activity despite knowing it was causing problems between you and other people” (continuation despite problems) in POG. These symptoms are more frequently and strongly connected to other symptoms of problematic online behaviours and may increase the risk of current symptomatology, while increasing the likelihood of developing further problem behaviours (Fried etal., 2017). Moreover, even the less impactful symptoms of problematic online behaviours in the overall network could offer meaningful insights. For example, the less influential symptoms identified in the present study included “Do others (e.g., partner, children, parents) say you should use the internet less” (conflict) and “Are you short of sleep because of the internet” (loss of control) in GPIU, and “Spent a lot of time thinking about social media or planned use of social media” (salience) in PSMU. This means that these symptoms may be more peripheral than in the core of their disorder (West & Brown, 2013). It was also hypothesised that each specific problematic behaviour investigated in the study would form distinct clusters of symptom severity at the item-level responses, while the umbrella constructs of GPIU would not constitute a specific disorder (H2). The community detection algorithm showed the existence of four clusters. One of them corresponded to the nine items of the POG scale, another corresponded to the preoccupation items and one of the conflict items of the GPIU, while two other communities had mixed items from the GPIU and PSMU scales, one focussing on the items of loss of control/relapse and conflict, and the others on salience, tolerance, withdrawal, and mood modification. These findings indicate that H2 was partially supported. First, it was shown that the PSMU and POG items formed distinctive clusters. This result demonstrates that although the specific problematic online behaviours analysed were associated with each other, they were independent psychopathological entities, which is also in line with previous research using the same methodological approach (Li etal., 2023b; Rozgonjuk etal., 2021; Zarate etal., 2022). Furthermore, the finding of two clusters consisting of GPIU and PSMU items also supports the spectrum hypothesis by suggesting that GPIU and PSMU are not separate entities, International Journal of Mental Health and Addiction 1 3 with the former acting as a nonspecific disorder. This result supports the idea that problematic online behaviours are specific (Brand etal., 2019; Montag etal., 2015) and that the internet is only the medium that facilitates them (Griffiths, 2000; Meerkerk etal., 2009; Shaffer etal., 2000; Starcevic & Billieux, 2017). The identification of a cluster containing GPIU-specific items (Dimension 2) requires an explanation. It might have been expected that the preoccupation symptoms of GPIU would align with the salience symptoms of PSMU, given their analogous nature as discussed in the literature (e.g., Griffiths, 2000, 2005; Meerkerk etal. (2009). However, the formulation of these items differs, possibly referring to related but distinct symptoms. On the other hand, by identifying a specific cluster with POG items, the results provide evidence to affirm that POG is a disorder independent of GPIU. This result is in line with the authors who consider these disorders as separate constructs (Griffiths, 2018; Király etal., 2014; Pontes & Griffiths, 2014), as well as with previous evidence using the network analysis approach (Baggio etal., 2018; Li etal., 2023b). Overall, these results suggest that GPIU being a generic concept that includes other specific behaviours such as PSMU (Billieux, 2012; Starcevic & Billieux, 2017), POG would not be included among them. Therefore, GPIU and POG, while showing significant connections between some of their symptoms, are different entities. Symptoms with the highest bridge centrality indices were continuation despite problems for POG and symptoms associated with conflict, preoccupation, coping, and mood modification for GPIU. These symptoms could heighten the chances of adopting crossproblematic online behaviours and/or adopting a new form of problematic behaviour while disengaging from a preexisting one (Zarate etal., 2022). Finally, it was hypothesised that GPIU, PSMU, and POG would be positively associated with depression, anxiety, stress, and emotional role limitations (H3). The results showed that H3 was partially supported. Network 2 was reasonably dense, with 61.9% of the nodes showing significant connections with other nodes. In terms of significantly connected nodes, (i) GPIU was positively associated with depression and emotional role limitations, (ii) PSMU was positively associated with anxiety and emotional role limitations; and (iii) POG was positively associated with depression. In addition, the nodes for depression, anxiety, and stress were connected to each other and to the node for emotional role limitations. Furthermore, it was found that the nodes corresponding to the variables of depression, anxiety, and stress were central in the network, while emotional role limitations had a low value in the strength centrality coefficient. With these results and theoretical models that explain the development of problematic online behaviours (e.g., Brand etal., 2019; Kardefelt-Winther, 2014), it could be interpreted that an increase in emotional distress would be associated with the risk of problematic use of different internet activities, with depressive symptoms associated with GPIU and POG and anxiety symptoms with PSMU (Chang etal., 2022; Lai etal., 2023; Liu etal., 2021). GPIU and PSMU would, in turn, be associated with the emotional role as an indicator of mental health (Machimbarrena etal., 2019). Social relationships are predictors of positive development at this stage of life (O’Connor etal., 2011). Therefore, emotional health could be affected when they are mainly located in the virtual context. Furthermore, the finding of a connection of emotional role limitations with GPIU and PSMU, but not POG, could also be related to the nature of the activity. In their habitual connection to social networks, students often engage passively, which has been shown to have a more pronounced impact on mental health than active use (Verduyn etal., 2021). On the contrary, many videogame genres require significant cognitive International Journal of Mental Health and Addiction 1 3 resources (Dale etal., 2020), posing challenges to meet these demands within specific daily contexts, such as professional or academic settings, which could act as protective factors for emotional well-being. Limitations The present study has several limitations that should be considered when interpreting the results. First, the sample used, although relatively large in size, came from a single Spanish university. This university has specific characteristics (i.e., large size, public teaching, bilingual Spanish–English instruction), and is situated within a specific demographic context (i.e., urban population concentration, a high proportion of young residents, significant national and international migration, widespread access to tertiary education, and medium socioeconomic backgrounds), that make it serve as a representative example of institutions sharing similar sociocultural features. However, the limitations related to the generalisability of the results make it advisable to replicate the study among samples of university students from other regions with different sociocultural characteristics. For example, future studies could investigate similar relationships within populations with distinct characteristics, such as rural communities. In these settings, individuals may employ different coping mechanisms to deal with negative emotional states, which diverge from the reliance on the internet and its functions. Moreover, to address the limitations associated with convenience sampling, future studies could employ more rigorous sampling methods, such as random sampling or a combination of multiple sampling techniques to enhance the representativeness of the sample. Second, only self-report instruments were used to collect the data. These techniques may have some limitations related to insufficiency and confusion in the understanding of the questions, social desirability, distortion of the truth, and/or memory bias (Ibáñez Aguirre, 2016). For future research, qualitative methods, such as interviews and focus groups, are recommended, which could improve the completeness of the variables’ assessments, by providing a nuanced and contextual understanding of experiences, perceptions, and meanings associated with the studied variables. Furthermore, the assessment of variables could benefit from the use of daily logs to reduce recall bias. Moreover, considering the tendency to normalise problematic online behaviour, it is recommended that future studies complement self-report assessments with an external evaluation. For example, the perspective of a roommate or partner could offer additional insights, facilitating the cross-verification of the information provided by the individual in question. Third, in the present study, only one variable assessing well-being (i.e., emotional role limitations) was considered. Future research could examine the relationships between problematic online behaviours and other potentially-related mental health variables such as life satisfaction, perceived social support, or self-esteem. Fourth, network analysis embraces a formative perspective in understanding mental disorders. Consequently, the connections between variables are interpreted as causal systems (van Borkulo etal., 2015). However, given the use of cross-sectional data in the present study, the assumption of causality is precluded. Subsequent investigations may seek to tackle this issue by utilising longitudinal or experimental designs. This would facilitate the examination of directionality in the relationships among problematic online behaviour symptoms and psychological variables. For example, future research could investigate the relationships between PIU symptoms and mental health variables at various points in time, International Journal of Mental Health and Addiction 1 3 spanning from childhood to university years, in order to establish the temporal sequence between them. Finally, three problematic online behaviours were assessed, which, although they are the most studied in the university student population, are not the only ones on the spectrum. Future studies could include the analysis of other behaviours such as problematic online gambling, shopping, or pornography. Theory‑Based Implications The present study makes important theoretical contributions to the field of online problem behaviours. The application of the network analysis approach to the symptoms of three problematic online behaviours provided support for conceptions that argue that GPIU is a nonspecific disorder, encompassing the entire spectrum of online problem behaviours (Fineberg etal., 2018). The present study also supports the idea that PSMU and POG are independent constructs that, although they share the same medium (i.e., the internet), they possess distinctive characteristics that make them worthy of detailed analysis (Billieux, 2012; Starcevic & Billieux, 2017). The present study also provided additional evidence for conceptions that separate POG from the PIU spectrum (Király etal., 2014). Although GPIU and POG are disorders that share elements, such as the common elements of addictive disorders (Griffiths, 2005), gaming is an activity that does not need the online component to be realised, and its problematic use develops from different motivations and processes. Moreover, the similarities between GPIU and PSMU may reflect greater overlaps between these problematic behaviours. That is, the functions that social media enable (e.g., maintaining social relationships) are one of the main reasons why individuals use the internet (Statista, 2024), so it may be that when answering CIUS-14, when assessing their internet problem behaviour, they had social media networks largely in mind. For all these reasons, for future research, abandoning the study of generalised behaviour in favour of the analysis of specific behaviours is advocated. Practice‑Based Implications The findings have practical implications for the assessment and intervention of these problem behaviours. In terms of assessment, the results suggest that although these behaviours have common elements (Griffiths, 2005), they need to be specifically assessed, both in research and in clinical practice. Therefore, it is recommended, for future diagnostic manuals, to include different internet-mediated behaviours, as they present characteristics that make them distinctive. In the framework of the intervention, the results of the present study may be beneficial in the treatment of problematic online behaviours among university students. More specifically, based on the results presented and taking into account the applications in the network approach field (Borsboom, 2017), it is necessary to treat symptomatology, focusing on symptoms of coping or mood modification, withdrawal and preoccupation of GPIU, relapse and withdrawal in PSMU, and continuation despite problems in POG, as they are central in the network. Additionally, to avoid comorbidity and transmission of symptoms from one form to another, it is recommended to treat bridging symptoms, continuation despite problems for POG, and symptoms associated with conflict, preoccupation, and International Journal of Mental Health and Addiction 1 3 coping/mood modification for GPIU. Finally, evidence of relationships between problematic online behaviours and symptoms of depression, anxiety, and stress, as well as the centrality of these variables in the joint network, suggests that treatment of these symptoms could have positive effects on these problematic behaviours. The ultimate purpose would be to contribute to the well-being of these students. Conclusion The present study identified a network of interrelated nodes between the symptoms of GPIU, PSMU, and POG, with the most pronounced associations observed between the analogous symptoms of GPIU and PSMU. These symptoms were grouped into four distinct dimensions, including one exclusive to the nine symptoms of POG, another that comprised preoccupation and a conflict symptom of GPIU, a dimension that comprised loss of control/relapse and conflict symptoms of GPIU and PSMU, and a final dimension that comprised salience and tolerance symptoms of PSMU and withdrawal and mood modification symptoms of GPIU and PSMU. Finally, relevant connections between depression and both GPIU and POG, anxiety and PSMU, and emotional role limitations and both GPIU and PSMU were established. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1146902401296-y. Author Contribution Magdalena Sánchez-Fernández: Conceptualization, Methodology, Formal analysis, Investigation, Writing—Original Draft, Funding acquisition. Mercedes Borda-Mas: Conceptualization, Methodology, Investigation, Writing—Review & Editing, Supervision; Francisco Rivera: Methodology, Formal analysis, Writing—Review & Editing; Mark D. Griffiths: Conceptualization, Writing—Review & Editing, Supervision. Funding Funding for open access publishing: Universidad de Sevilla/CBUA. This study was funded by a research grant from the “VI Plan Propio de Investigación y Transferencia” of the Universidad de Sevilla (VI-PPITUS) awarded to the first author (MSF). Data Availability Data available on request due to privacy/ethical restrictions. Declarations Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. MDG has received research funding from Norsk Tipping (the gambling operator owned by the Norwegian government). MDG has received funding for a number of research projects in the area of gambling education for young people, social responsibility in gambling and gambling treatment from Gamble Aware (formerly the Responsibility in Gambling Trust), a charitable body which funds its research program based on donations from the gambling industry. MDG undertakes consultancy for various gambling companies in the area of player protection and social responsibility in gambling. Ethical Approval The study was approved by the University of Seville Research Ethics Committee (Comité de Ética de Investigaciónde la Universidad de Sevilla, CEIUS) and adhered to the tenets of the Declaration of Helsinki (Internal code: 1346-N-22; Date of approval: 28 September 2022). Informed Consent All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all participants for being included in the study. International Journal of Mental Health and Addiction 1 3 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References Akbari, M., Bahadori, M. H., Khanbabaei, S., Milan, B. B., Horvath, Z., Griffiths, M. D., & Demetrovics, Z. (2023). Metacognitions as a predictor of problematic social media use and internet gaming disorder: Development and psychometric properties of the metacognitions about social media use scale (MSMUS). Addictive Behaviors, 137, 107541. https:// doi. org/ 10. 1016/j. addbeh. 2022. 107541 Alavi, M., Visentin, D. C., Thapa, D. K., Hunt, G. E., Watson, R., & Cleary, M. (2020). Chi-square for model fit in confirmatory factor analysis. Journal of Advanced Nursing, 76(9), 2209–2211. https:// doi. org/ 10. 1111/ jan. 14399 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM5). American Psychiatric Publishing. Anderson, E. L., Steen, E., & Stavropoulos, V. (2017). Internet use and problematic internet use: A systematic review of longitudinal research trends in adolescence and emergent adulthood. International Journal of Adolescence and Youth, 22(4), 430–454. https:// doi. org/ 10. 1080/ 02673 843. 2016. 12277 16 Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a Facebook addiction scale. Psychological Reports, 110(2), 501–517. https:// doi. org/ 10. 2466/ 02. 09. 18. PR0. 110.2. 501517 Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252–262. https:// doi. org/ 10. 1037/ adb00 00160 Baggio, S., Gainsbury, S. M., Berchtold, A., & Iglesias, K. (2016). Co-morbidity of gambling and internet use among internet and land-based gamblers: Classic and network approaches. International Gambling Studies, 16(3), 500–517. https:// doi. org/ 10. 1080/ 14459 795. 2016. 12421 48 Baggio, S., Starcevic, V., Studer, J., Simon, O., Gainsbury, S. M., Gmel, G., & Billieux, J. (2018). Technology-mediated addictive behaviors constitute a spectrum of related yet distinct conditions: A network perspective. Psychology of Addictive Behaviors, 32(5), 564–572. https:// doi. org/ 10. 1037/ adb00 00379 Baggio, S., Starcevic, V., Billieux, J., King, D. L., Gainsbury, S. M., Eslick, G. D., & Berle, D. (2022). Testing the spectrum hypothesis of problematic online behaviors: A network analysis approach. Addictive Behaviors, 135, 107451. https:// doi. org/ 10. 1016/j. addbeh. 2022. 107451 Beranuy, M., Machimbarrena, J. M., Vega-Osés, M. A., Carbonell, X., Griffiths, M. D., Pontes, H. M., & González-Cabrera, J. (2020). Spanish validation of the Internet Gaming Disorder - Short Form(IGDS9-SF): Prevalence and relationship with online gambling and quality of life. International Journal of Environmental Research and Public Health, 17(5), 1562. https:// doi. org/ 10. 3390/ ijerp h1705 1562 Billieux, J. (2012). Problematic use of the mobile phone: A literature review and a pathways model. Current Psychiatry Reviews, 8(4), 299–307. https:// doi. org/ 10. 2174/ 15734 00128 03520 522 Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5–13. https:// doi. org/ 10. 1002/ wps. 20375 Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience & Biobehavioral Reviews, 104, 1–10. https:// doi. org/ 10. 1016/j. neubi orev. 2019. 06. 032 Cai, H., Xi, H. T., An, F., Wang, Z., Han, L., Liu, S., ... & Xiang, Y. T. (2021). The association between internet addiction and anxiety in nursing students: a network analysis.Frontiers in Psychiatry,12, 723355. https:// doi. org/ 10. 3389/ fpsyt. 2021. 723355 International Journal of Mental Health and Addiction 1 3 Cai, H., Bai, W., Sha, S., Zhang, L., Chow, I. H., Lei, S. M., ... & Xiang, Y. T. (2022). Identification of central symptoms in Internet addictions and depression among adolescents in Macau: A network analysis.Journal of Affective Disorders,302, 415–423. https:// doi. org/ 10. 1016/j. jad. 2022. 01. 068 Chang, C. W., Huang, R. Y., Strong, C., Lin, Y. C., Tsai, M. C., Chen, I. H., ... & Griffiths, M. D. (2022). Reciprocal relationships between problematic social media use, problematic gaming, and psychological distress among university students: a 9-month longitudinal study.Frontiers in Public Health,10, 858482. https:// doi. org/ 10. 3389/ fpubh. 2022. 858482 Christensen, A. P. (2020).Towards a network psychometrics approach to assessment: Simulations for redundancy, dimensionality, and loadings.[Doctoral dissertation, University of North Carolina atGreensboro]. Retrieved November 17, 2023,fromhttps:// www. proqu est. com/ disse rtati onstheses/ towar dsnetwo rkpsych ometr icsappro achasses sment/ docvi ew/ 24285 52190/ se-2? accou ntid= 14744 Cole, D. A. (1987). Utility of confirmatory factor analysis in test validation research. Journal of Consulting and Clinical Psychology, 55(4), 584–594. https:// doi. org/ 10. 1037/ 0022006X. 55.4. 584 Cramer, A. O., Waldorp, L. J., Van Der Maas, H. L., & Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and Brain Sciences, 33(2–3), 137–150. https:// doi. org/ 10. 1017/ S0140 525X0 99915 67 Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29. Dale, G., Joessel, A., Bavelier, D., & Green, C. S. (2020). A new look at the cognitive neuroscience of video game play. Annals of the New York Academy of Sciences, 1464(1), 192–203. https:// doi. org/ 10. 1111/ nyas. 14295 Daza, P., Novy, D. M., Stanley, M. A., & Averill, P. (2002). The Depression Anxiety Stress Scale-21: Spanish translation and validation with a Hispanic sample. Journal of Psychopathology and Behavioral Assessment, 24, 195–205. https:// doi. org/ 10. 1023/A: 10160 14818 163 Demetrovics, Z., Urbán, R., Nagygyörgy, K., Farkas, J., Griffiths, M. D., Pápay, O., ... & Oláh, A. (2012). The development of the Problematic Online Gaming Questionnaire (POGQ). PloS One, 7(5), e36417. https:// doi. org/ 10. 1371/ journ al. pone. 00364 17 Dienlin, T., & Johannes, N. (2022). The impact of digital technology use on adolescent well-being. Dialogues in Clinical Neuroscience, 22(2), 135–142. https:// doi. org/ 10. 31887/ DCNS. 2020. 22.2/ tdien lin Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617. https:// doi. org/ 10. 1037/ met00 00167 Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50, 195–212. https:// doi. org/ 10. 3758/ s134280170862-1 Epskamp, S., Costantini, G., Haslbeck, J., & Isvoranu, A. (2023). Qgraph: Graph plotting methods, psychometric data visualization and graphical model estimation. (Version 1.9.5) [R package]. https:// cran.rproje ct. org/ web/ packa ges/ qgraph/ index. html. Accessed 17 Nov 2023 Epskamp, S. (2023). Bootnet: bootstrap methods for various network estimation routines. (Version 1.5.5) [R package]. https:// cran.rproje ct. org/ web/ packa ges/ bootn et/ index. html. Accessed 17 Nov 2023 Fineberg, N., Demetrovics, Z., Stein, D. J., Ioannidis, K., Potenza, M. N., Grünblatt, E., Brand, M., Billieux, J., Carmi, L., King, D. L., Grant, J. E., Yücel, M., Dell’Osso, B., Rumpf, H. J., Hall, N., Hollander, E., Goudriaan, A., Menchon, J., Zohar, J., ... Chamberlain, S. (2018). Manifesto for a European research network into problematic usage of the internet. European Neuropsychopharmacology, 28(11), 1232–1246. https:// doi. org/ 10. 1016/j. euron euro. 2018. 08. 004 Fournier, L., Schimmenti, A., Musetti, A., Boursier, V., Flayelle, M., Cataldo, I., ... & Billieux, J. (2023). Deconstructing the components model of addiction: An illustration through “addictive” use of social media.Addictive Behaviors,143, 107694. https:// doi. org/ 10. 1016/j. addbeh. 2023. 107694 Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. In: J. Lafferty, C. Williams, J. Shawe-Taylor, R. Zemel, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 23 (NIPS 2010) (Vol. 1, pp. 604–612). Fried, E. I., van Borkulo, C. D., Cramer, A. O., Boschloo, L., Schoevers, R. A., & Borsboom, D. (2017). Mental disorders as networks of problems: A review of recent insights. Social Psychiatry and Psychiatric Epidemiology, 52, 1–10. https:// doi. org/ 10. 1007/ s001270161319-z Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129–1164. https:// doi. org/ 10. 1002/ spe. 43802 11102 Giraldo-Rodríguez, L., & López-Ortega, M. (2024). Validation of the Short-Form 36 Health Survey (SF36) for use in Mexican older persons. Applied Research in Quality of Life, 19(1), 269–292. https:// doi. org/ 10. 1007/ s1148202310240-6 International Journal of Mental Health and Addiction 1 3 Golino, H., & Christensen, A. (2023). EGAnet: Exploratory graph analysis – a framework for estimating the number of dimensions in multivariate data using network psychometrics. (Version 2.0.1) [R package]. https:// cran.rproje ct. org/ web/ packa ges/ EGAnet/ index. html. Accessed 17 Nov 2023 Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research.PloS One, 12(6), e0174035. https:// doi. org/ 10. 1371/ journ al. pone. 01740 35 Griffiths, M. D. (2000). Internet addiction - Time to be taken seriously? Addiction Research, 8, 413–418. https:// doi. org/ 10. 3109/ 16066 35000 90055 87 Griffiths, M. D. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191–197. https:// doi. org/ 10. 1080/ 14659 89050 01143 59 Griffiths, M. D. (2018). Conceptual issues concerning internet addiction and internet gaming disorder: Further critique on Ryding and Kaye (2017). International Journal of Mental Health and Addiction, 16, 233–239. https:// doi. org/ 10. 1007/ s114690179818-z Griffiths, M. D., & Szabo, A. (2014). Is excessive online usage a function of medium or activity? An empirical pilot study. Journal of Behavioral Addictions, 3(1), 74–77. https:// doi. org/ 10. 1556/ jba.2. 2013. 016 Guyon, H., Falissard, B., & Kop, J. L. (2017). Modeling psychological attributes in psychology–an epistemological discussion: network analysis vs. latent variables. Frontiers in Psychology, 8, 798. https:// doi. org/ 10. 3389/ fpsyg. 2017. 00798 Hemphill, J. F. (2003). Interpreting the magnitudes of correlation coefficients. American Psychologist, 58(1), 78–79. https:// doi. org/ 10. 1037/ 0003066X. 58.1. 78 Hussain, Z., & Starcevic, V. (2020). Problematic social networking site use: A brief review of recent research methods and the way forward. Current Opinion in Psychology, 36, 89–95. https:// doi. org/ 10. 1016/j. copsyc. 2020. 05. 007 Ibáñez Aguirre, C. (2016). Técnicas de autoinforme en evaluación psicológica: La entrevista clínica. Universidad del País Vasco. Isvoranu, A. M., Boyette, L. L., Guloksuz, S., Borsboom, D., Tamminga, C. A., Ivleva, E. I., ... & van Os, J. (2017). Symptom Network models of psychosis. In: Tamminga, C. A., Ivleva, E. I., Reininghaus, U., & van Os, J. (Eds.), Psychotic disorders: Comprehensive conceptualization and treatments(pp. 70–78). Oxford University Press. JASP Team (2023). JASP (Version 0.16.4) [Computer software]. https:// jaspstats. org/. Accessed 10 Nov 2023 Jones, P. (2023). Networktools: Tools for identifying important nodes in networks (Version 1.5.1) [R package]. https:// cran.rproje ct. org/ web/ packa ges/ netwo rktoo ls/ index. html. Accessed 17 Nov 2023 Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351–354. https:// doi. org/ 10. 1016/j. chb. 2013. 10. 059 Kim, H. Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52–54. https:// doi. org/ 10. 5395/ rde. 2013. 38.1. 52 Király, O., Griffiths, M. D., Urbán, R., Farkas, J., Kökönyei, G., Elekes, Z., ... & Demetrovics, Z. (2014). Problematic internet use and problematic online gaming are not the same: Findings from a large nationally representative adolescent sample. Cyberpsychology, Behavior, and Social Networking, 17(12), 749–754. https:// doi. org/ 10. 1089/ cyber. 2014. 0475 Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford Press. Krämer, N., Schäfer, J., & Boulesteix, A. L. (2009). Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics, 10, 384. https:// doi. org/ 10. 1186/ 1471210510384 Kwok, C., Leung, P. Y., Poon, K. Y., & Fung, X. C. (2021). The effects of internet gaming and social media use on physical activity, sleep, quality of life, and academic performance among university students in Hong Kong: A preliminary study. Asian Journal of Social Health and Behavior, 4(1), 36–44. https:// doi. org/ 10. 4103/ shb. shb_ 81_ 20 Lai, W., Wang, W., Li, X., Wang, H., Lu, C., & Guo, L. (2023). Longitudinal associations between problematic Internet use, self-esteem, and depressive symptoms among Chinese adolescents. European Child & Adolescent Psychiatry, 32(7), 1273–1283. https:// doi. org/ 10. 1007/ s0078702201944-5 Li, L., Niu, Z., Griffiths, M. D., Wang, W., Chang, C., & Mei, S. (2021). A network perspective on the relationship between gaming disorder, depression, alexithymia, boredom, and loneliness among a sample of Chinese university students. Technology in Society, 67, 101740. https:// doi. org/ 10. 1016/j. techs oc. 2021. 101740 Li, L., Liu, L., Niu, Z., Zhong, H., Mei, S., & Griffiths, M. D. (2023a). Gender differences and left-behind experiences in the relationship between gaming disorder, rumination and sleep quality among a International Journal of Mental Health and Addiction 1 3 sample of Chinese university students during the late stage of the COVID-19 pandemic. Frontiers in Psychiatry, 14, 1108016. https:// doi. org/ 10. 3389/ fpsyt. 2023. 11080 16 Li, Y., Mu, W., Xie, X., & Kwok, S. Y. (2023b). Network analysis of internet gaming disorder, problematic social media use, problematic smartphone use, psychological distress, and meaning in life among adolescents. Digital Health, 9, 20552076231158036. https:// doi. org/ 10. 1177/ 20552 07623 11580 36 Liu, Y., Gong, R., Yu, Y., Xu, C., Yu, X., Chang, R., ... & Cai, Y. (2021). Longitudinal predictors for incidence of internet gaming disorder among adolescents: The roles of time spent on gaming and depressive symptoms.Journal of Adolescence,92, 1–9. https:// doi. org/ 10. 1016/j. adole scence. 2021. 06. 008 Lopez-Fernandez, O., Kuss, D., Pontes, H., & Griffiths, M. (2016). Video game addiction: Providing evidence for internet gaming disorder through a systematic review of clinical studies. European Psychiatry, 33(S1), S306. https:// doi. org/ 10. 1016/j. eurpsy. 2016. 01. 1047 Lopez-Fernandez, O., Griffiths, M. D., Kuss, D. J., Dawes, C., Pontes, H. M., Justice, L., ... & Billieux, J. (2019). Cross-cultural validation of the compulsive internet use scale in four forms and eight languages.Cyberpsychology, Behavior, and Social Networking,22(7), 451–464. https:// doi. org/ 10. 1089/ cyber. 2018. 0731 Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 33(3), 335–343. https:// doi. org/ 10. 1016/ 00057967(94) 00075-U Machimbarrena, J. M., González-Cabrera, J., Ortega-Barón, J., Beranuy-Fargues, M., Álvarez-Bardón, A., & Tejero, B. (2019). Profiles of problematic internet use and its impact on adolescents’ health-related quality of life. International Journal of Environmental Research and Public Health, 16(20), 3877. https:// doi. org/ 10. 3390/ ijerp h1620 3877 Maldonado-Murciano, L., Pontes, H. M., Griffiths, M. D., Barrios, M., Gómez-Benito, J., & Guilera, G. (2020). The Spanish version of the Internet Gaming Disorder - Short Form(IGDS9-SF): Further examination using item response theory. International Journal of Environmental Research and Public Health, 17(19), 7111. https:// doi. org/ 10. 3390/ ijerp h1719 7111 Mauer-Vakil, D., & Bahji, A. (2020). The addictive nature of compulsive sexual behaviours and problematic online pornography consumption: A review. Canadian Journal of Addiction, 11(3), 42–51. https:// doi. org/ 10. 1097/ CXA. 00000 00000 000091 Meerkerk, G. J., Van Den Eijnden, R. J., Vermulst, A. A., & Garretsen, H. F. (2009). The Compulsive Internet Use Scale (CIUS): Some psychometric properties. Cyberpsychology & Behavior, 12(1), 1–6. https:// doi. org/ 10. 1089/ cpb. 2008. 0181 Monacis, L., De Palo, V., Griffiths, M. D., & Sinatra, M. (2017). Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale. Journal of Behavioral Addictions, 6(2), 178–186. https:// doi. org/ 10. 1556/ 2006.6. 2017. 023 Montag, C., Bey, K., Sha, P., Li, M., Chen, Y.-F., Liu, W.-Y., Zhu, Y.-K., Li, C.-B., Markett, S., Keiper, J., & Reuter, M. (2015). Is it meaningful to distinguish between generalized and specific Internet addiction? Evidence from a cross-cultural study from Germany, Sweden, Taiwan and China: Specific forms of Internet addiction. Asia-Pacific Psychiatry, 7(1), 20–26. https:// doi. org/ 10. 1111/ appy. 12122 Mora-Salgueiro, J., García-Estela, A., Hogg, B., Angarita-Osorio, N., Amann, B. L., Carlbring, P., ... & Colom, F. (2021). The prevalence and clinical and sociodemographic factors of problem online gambling: A systematic review.Journal of Gambling Studies,37(3), 899–926. https:// doi. org/ 10. 1007/ s1089902109999-w Müller, A., Laskowski, N. M., Wegmann, E., Steins-Loeber, S., & Brand, M. (2021). Problematic online buying-shopping: Is it time to considering the concept of an online subtype of compulsive buyingshopping disorder or a specific internet-use disorder? Current Addiction Reports, 8, 494–499. https:// doi. org/ 10. 1007/ s4042902100395-3 Naidu, S., Chand, A., Pandaram, A., & Patel, A. (2023). Problematic internet and social network site use in young adults: The role of emotional intelligence and fear of negative evaluation. Personality and Individual Differences, 200, 111915. https:// doi. org/ 10. 1016/j. paid. 2022. 111915 O’Connor, M., Sanson, A., Hawkins, M. T., Letcher, P., Toumbourou, J. W., Smart, D., ... & Olsson, C. A. (2011). Predictors of positive development in emerging adulthood.Journal of Youth and Adolescence,40, 860–874. https:// doi. org/ 10. 1007/ s109640109593-7 Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. https:// doi. org/ 10. 1016/j. socnet. 2010. 03. 006 Peng, P., & Liao, Y. (2023). Six addiction components of problematic social media use in relation to depression, anxiety, and stress symptoms: A latent profile analysis and network analysis. BMC Psychiatry, 23(1), 321. https:// doi. org/ 10. 1186/ s1288802304837-2 Peris, M., de la Barrera, U., Schoeps, K., & Montoya-Castilla, I. (2020). Psychological risk factors that predict social networking and internet addiction in adolescents. International Journal of Environmental Research and Public Health, 17(12), 4598. https:// doi. org/ 10. 3390/ ijerp h1712 4598 International Journal of Mental Health and Addiction 1 3 Pontes, H. M., & Griffiths, M. D. (2014). Internet addiction disorder and internet gaming disorder are not the same. Journal of Addiction Research & Therapy, 5(4), e124. https:// doi. org/ 10. 4172/ 21556105. 1000e 124 Pontes, H. M., & Griffiths, M. D. (2015). Measuring DSM-5 internet gaming disorder: Development and validation of a short psychometric scale. Computers in Human Behavior, 45, 137–143. https:// doi. org/ 10. 1016/j. chb. 2014. 12. 006 Pontes, H. M., Szabo, A., & Griffiths, M. D. (2015). The impact of Internet-based specific activities on the perceptions of Internet addiction, quality of life, and excessive usage: A cross-sectional study. Addictive Behaviors Reports, 1, 19–25. https:// doi. org/ 10. 1016/j. abrep. 2015. 03. 002 Pontes, H. M., Andreassen, C. S., & Griffiths, M. D. (2016). Portuguese validation of the bergen facebook addiction scale: An empirical study. International Journal of Mental Health and Addiction, 14(6), 1062–1073. https:// doi. org/ 10. 1007/ s114690169694-y Poon, L. Y., Tsang, H. W., Chan, T. Y., Man, S. W., Ng, L. Y., Wong, Y. L., ... & Pakpour, A. H. (2021). Psychometric properties of the Internet Gaming Disorder - Short Form (IGDS9-SF): Systematic review.Journal of Medical Internet Research,23(10), e26821. https:// doi. org/ 10. 2196/ 26821 R Core Team (2023).R: A language and environment for statistical computing(4.3.0). https:// www.rproje ct. org/. Accessed 17 Nov 2023 Rigó, A., Tóth-Király, I., Magi, A., Eisinger, A., Griffiths, M. D., & Demetrovics, Z. (2023). Morningnesseveningness and problematic online activities.International Journal of Mental Health and Addiction. https:// doi. org/ 10. 1007/ s1146902301017-x Rozgonjuk, D., Schivinski, B., Pontes, H. M., & Montag, C. (2021). Problematic online behaviors among gamers: The links between problematic gaming, gambling, shopping, pornography use, and social networking. International Journal of Mental Health and Addiction, 21, 240–257. https:// doi. org/ 10. 1007/ s1146902100590-3 Sánchez-Caballé, A., Gisbert Cervera, M., & Esteve-Mon, F. M. (2020). The digital competence of university students: A systematic literature review. Aloma: Revista de Psicologia, Ciències de l’Educació i de l’Esport, 38(1), 63–74.http:// hdl. handle. net/ 10234/ 191134. Accessed 6 Oct 2023 Sánchez-Fernández, M., & Borda-Mas, M. (2024). Motor impulsivity and problematic online behaviours among university students: the potential mediating role of coping style. Current Psychology. Advance online publication. https:// doi. org/ 10. 1007/ s1214402405766-3 Sánchez-Iglesias, I., Bernaldo-de-Quirós, M., Labrador, F. J., Puig, F. J. E., Labrador, M., & FernándezArias, I. (2020). Spanish validation and scoring of the internet gaming disorder scale-short-form (IGDS9-SF). Spanish Journal of Psychology, 23, e22. https:// doi. org/ 10. 1017/ SJP. 2020. 26 Sayili, U., Pirdal, B. Z., Kara, B., Acar, N., Camcioglu, E., Yilmaz, E., ... & Erginoz, E. (2023). Internet addiction and social media addiction in medical faculty students: Prevalence, related factors, and association with life Satisfaction.Journal of Community Health, 48, 189–198. https:// doi. org/ 10. 1007/ s1090002201153-w Schmittmann, V. D., Cramer, A. O., Waldorp, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31(1), 43–53. https:// doi. org/ 10. 1016/j. newid eapsy ch. 2011. 02. 007 Shaffer, H. J., Hall, M. N., & Vander Bilt, J. (2000). “Computer addiction”: A critical consideration. American Journal of Orthopsychiatry, 70(2), 162–168. https:// doi. org/ 10. 1037/ h0087 741 Sit, H. F., Chang, C. I., Yuan, G. F., Chen, C., Cui, L., Elhai, J. D., & Hall, B. J. (2023). Symptoms of internet gaming disorder and depression in Chinese adolescents: A network analysis. Psychiatry Research, 322, 115097. https:// doi. org/ 10. 1016/j. psych res. 2023. 115097 Starcevic, V., & Billieux, J. (2017). Does the construct of Internet addiction reflect a single entity or a spectrum of disorders? Clinical Neuropsychiatry, 14(1), 5–10. Statista (2024). Most popular reasons for using the internet worldwide as of 3rd quarter 2023. https:// www. stati sta. com/ st ati stics/ 13873 75/ inter netusingglobalreaso ns/#: ~: text= As% 20of% 20the% 20thi rd% 20qua rter,shows% 2C% 20or% 20mov ies% 20ran ked% 20thi rd. Accessed 8 Mar 2024 Su, C.-T., Ng, H.-S., Yang, A.-L., & Lin, C.-Y. (2014). Psychometric evaluation of the short form 36 health survey (SF-36) and the world health organization quality of life scale brief Version (WHOQOL-BREF) for patients with schizophrenia. Psychological Assessment, 26(3), 980–989. https:// doi. org/ 10. 1037/ a0036 764 Tereshchenko, S., Kasparov, E., Semenova, N., Shubina, M., Gorbacheva, N., Novitckii, I., ... & Lapteva, L. (2022). Generalized and specific problematic internet use in central Siberia adolescents: A school-based study of prevalence, age–sex depending content structure, and comorbidity with psychosocial problems. International Journal of Environmental Research and Public Health, 19(13), 7593. https:// doi. org/ 10. 3390/ ijerp h1913 7593