Gaming disorder (GD) has been categorized in the 11th edition of the International Classification of Diseases (ICD-11) as a behavioral addiction, included in the broader category of “disorders due to addictive behaviors” (Brand et al., 2020, 2025a; World Health Organization, 2019). It is defined by significant functional impairments resulting from continuous or recurrent gaming over a prolonged period. This includes impaired control over gaming, prioritizing gaming over other interests and activities, and continuing or increasing gaming despite impairments (World Health Organization, 2019). Children and adolescents are at an increased risk of developing GD (Király et al., 2023). Previous meta-analyses and literature reviews estimated the prevalence of GD risk among children and adolescents to be 3–6 %, exceeding the prevalence rates observed in adults (Fam, 2018; Griffiths et al., 2025; Kim et al., 2022; Király et al., 2023).

However, simply distinguishing between at-risk adolescents and those not at risk fails to fully capture the heterogeneity in GD. The risk of GD is often determined using a screening questionnaire’s cut-off score, which offers limited insight into individual symptom patterns within both at-risk and non-risk groups of GD (Musetti et al., 2019). Classification models and typologies of GD in adolescents provide a more nuanced understanding of its heterogeneity, moving beyond the simplistic binary classification of at-risk and non-risk gamers. Person-centered analytical approaches (e.g., latent class analysis, latent profile analysis, cluster analysis) can help identify distinct subgroups of individuals who share similar GD symptom patterns.

Several previous studies have sought to describe distinct subgroups of gamers based on their GD symptom profiles, utilizing data derived partially or entirely from children and adolescents. A summary of these studies, identified through a narrative literature review, is provided in Table 1. These studies demonstrated considerable variability in sample characteristics (e.g., representative vs. non-representative samples, or samples comprising fully or partially adolescent participants), measurements of GD symptom severity, statistical approaches, and the number of identified classes or typologies (i.e., ranging from 2 to 5) (Table 1). Furthermore, the classification models of GD symptom severity differed in how they distinguished the identified subgroups of gamers. Some studies outlined quantitatively distinct classes based on GD symptom severity (e.g., low-, moderate-, and high-symptom severity groups) without emphasizing the presence of specific GD symptoms within each class (Fuster et al., 2016; Pontes et al., 2014). In other classification models, certain subgroups were distinguished not only by differences in GD symptom severity but also by the specific presence of one or more GD symptoms (e.g., preoccupation, loss of control, escapism, negative consequences) (Chang et al., 2023; Colder Carras and Kardefelt-Winther, 2018; Faulkner et al., 2015; Király et al., 2017; Stavropoulos et al., 2021). The reviewed studies consistently identified a subgroup of normative gamers with very low symptom severity and an at-risk GD subgroup characterized by high severity across multiple GD symptoms (Table 1). These studies have also demonstrated that groups with more severe GD symptom profiles are characterized by poorer mental health outcomes (e.g., depressive and anxiety symptoms) and higher rates of potentially addictive online behaviors (e.g., smartphone addiction) (Chang et al., 2023; Colder Carras and Kardefelt-Winther, 2018; Musetti et al., 2019; Stavropoulos et al., 2021).

Further investigation is needed into certain aspects of GD risk typologies. For example, only one classification model in the reviewed literature analyzed subgroups separately for females and males (Colder Carras and Kardefelt-Winther, 2018). In this multinational study of adolescents, significant differences emerged between female and male typologies in GD symptom severity. Some subgroups were observed in both genders, such as normative players with low GD symptom severity and concerned players who primarily experienced negative gaming-related social consequences. However, subgroups with higher GD symptom severity were more common among males (e.g., an at-risk class unique to males, and a more severe internet GD class among males compared to females), whereas an interference class, characterized by negative gaming-related social consequences, was identified only among females (Colder Carras and Kardefelt-Winther, 2018). Consistent with this, previous meta-analyses and classification models demonstrated significant gender differences in GD risk, with higher prevalence rates reported among males (Fam, 2018; Kim et al., 2022) and higher proportions of males in the classes with more severe GD symptoms (Table 1).

Moreover, gender differences in the psychopathological mechanisms of GD can also be detected. For instance, males may exhibit greater disinhibition in response to game-related stimuli and cravings, as well as heightened sensitivity to game-related rewards and positive reinforcement motives for gaming. In contrast, females may be more likely to engage in gaming for coping, escapism or emotion regulation purposes (Dong and Potenza, 2022; Király et al., 2015). Given these differences, it is important to further explore the similarities and differences between adolescent females and males regarding classification models of GD symptom severity.

In existing classification models, intrapersonal correlates have been examined more extensively, whereas limited data are available on whether subgroups with varying severity levels and symptom profiles differ in relation to interpersonal factors. The Interaction of Person–Affect–Cognition–Execution (I-PACE) model (Brand et al., 2025b, 2019, 2016) provides a comprehensive theoretical framework for understanding the intra- and interpersonal processes underlying behavioral addictions, including GD. The model emphasizes the interplay of predisposing characteristics with cognitive and affective responses to internal and external stimuli in the decision to engage in a potentially addictive behavior, such as gaming, which may lead to disordered gaming over time. Within the category of predisposing characteristics, internal triggers may include intrapersonal risk factors such as psychopathologies and mental health problems (e.g., depression, anxiety, ADHD symptoms), personality traits (e.g., impulsivity), and general stress or emotion dysregulation. In addition, external triggers can involve adverse interpersonal conditions, such as social isolation, loneliness, and low perceived social support. These internal and external triggers can promote engagement in gaming by influencing circular cognitive and affective reinforcement processes, including attentional focus on gaming-related stimuli and short-term gratifications, motivation to engage in gaming for the purpose of alleviating intra- and interpersonal distress, cue-reactivity and craving, and disinhibition. Over time, this reinforcement process may shift the function of gaming from gratification-seeking toward compensatory use, becoming increasingly habitual and compulsive. This stage is typically characterized by diminished control and negative intra- and interpersonal consequences (e.g., negative emotions, social withdrawal, peer and family conflict). The I-PACE framework therefore highlights that intrapersonal and interpersonal difficulties may not only precede excessive gaming (e.g., gaming as a means of coping with intrapersonal symptoms and interpersonal difficulties) but may also emerge as its consequences. Specifically regarding interpersonal risk characteristics, this perspective supports the notion of a bidirectional causal relationship with GD: factors such as increased involvement in bullying (as perpetrator and/or victim), reduced sense of social connection, and impaired relationship quality and conflicts with peers, family members, and teachers can operate both as predisposing vulnerabilities and as adverse outcomes of the gaming behavior (Brand et al., 2019, 2016).

In line with the I-PACE framework, meta-analyses have also underscored the relevance of assessing interpersonal risk factors alongside intrapersonal correlates (e.g., internalizing psychopathological symptoms, hyperactivity/inattention, well-being, self-esteem, internet or social media addiction, personality traits, and motives for gaming) (Cheng et al., 2018; Gao et al., 2022; Ropovik et al., 2023; Zhuang et al., 2023). These meta-analyses indicated positive associations between GD risk and various interpersonal factors, including involvement in bullying (as perpetrator and/or victim), interpersonal difficulties, diminished social support, lower quality of parental and peer relationships, and reduced school engagement (Cheng et al., 2018; Gao et al., 2022; Zhuang et al., 2023). Overall, the inclusion of interpersonal factors appears to be important for the comprehensive characterization of GD symptom severity-based typologies. Furthermore, gender may serve as a moderating variable in the relationship between typologies of GD symptom severity and interpersonal factors, potentially revealing distinct patterns between females and males (Koncz et al., 2024; Zhuang et al., 2023).

Against this background, the present study had two main objectives. First, to identify distinct latent classes of GD symptom severity among adolescents, with separate classification models constructed for females and males. Second, to compare the identified latent classes across a broad spectrum of intrapersonal factors (e.g., life satisfaction, psychosomatic symptoms, problematic social media use [PSMU]) and interpersonal factors (e.g., involvement in school and cyberbullying, and perceived support from family, friends, and teachers). As the statistical procedure used for the first objective was exploratory, no specific hypotheses were proposed. For the second objective, we hypothesized that adolescents in classes with more severe GD symptoms would report lower life satisfaction and perceived social support from family, friends, and teachers, as well as higher psychosomatic symptoms, PSMU, and involvement in school and cyberbullying in perpetrator and/or victim roles, compared with those in the lowest-severity class.