Virtual worlds and mental health: A scoping review

Virtual worlds and mental health: A scoping review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Virtual worlds and mental health: A scoping review Miguel Barreda-Ángeles, Simone Amendola, Tilo Hartmann, Adrian Meier, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7561416/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The constant increase in the use of virtual worlds in recent decades has raised concerns about their potential effects on users’ mental health. However, the great heterogeneity of the scientific literature on this topic makes it difficult to identify existing evidence, knowledge gaps, and the most relevant future research directions. To address this issue, the present scoping review maps the current research landscape by analyzing 198 selected articles. The results reveal a growing interest in this topic across various disciplines, as well as the multiple assumptions and approaches adopted by different research communities. Existing research predominantly focuses on specific branded applications such as Second Life or World of Warcraft. However, there is very little research on some of the virtual world platforms that currently have the largest user bases (e.g., Roblox, Fortnite). Importantly, little attention has been paid to the shared features across virtual world platforms (e.g., technological affordances), which could help build more generalizable theories. The availability of causal evidence on the impact of virtual world use on users’ mental health is very limited, as is the diversity of participant samples used in studies. Addressing these issues will be key to advancing research in this field and to understanding the multiple ways in which virtual world applications may affect users’ psychological well-being, particularly regarding vulnerable users like children and adolescents. Biological sciences/Psychology Social science/Psychology Social science/Science technology and society virtual worlds mental health well-being metaverse Web 4.0 video games Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction In recent decades, the number of users of virtual worlds –understood here as three-dimensional digital environments where users can interact with one another and engage in a wide range of activities– has skyrocketed, with applications such as Roblox and Fortnite, for instance, attracting tens of millions of users each month (Kumar, 2025; Statista, 2025 ). The emergence of concepts such as metaverse (Ball, 2024 ), spatial computing (Apple, 2023 ) and Web 4.0 (Almeida, 2017 ) suggests that virtual worlds may be progressively gaining a more prevalent role in the evolution of digital connectivity over the coming years (Hupont Torres et al., 2023 ). As a response to the increasing presence of virtual worlds in people's digital lives, scholars (e.g., Paquin et al., 2023 ), policymakers (e.g., European Commission, 2023a ) and citizens (e.g., European Commission, 2023b ) have stressed the need to understand the potential impact of these applications on their users, and particularly on users' mental health. Interest in the mental health impact of digital media in general (Orben, 2020 ; Meier & Reinecke, 2021 ), and virtual worlds, more specifically, is not new (e.g., Gilbert et al., 2013 ; Photiadis & Zaphiris, 2016 ). However, past research on digital media and mental health has been conducted across different disciplines, using diverse conceptual and methodological frameworks, and often employing very heterogeneous and inconsistent terminology and approaches (cf. King et al., 2013 ; Meier & Reinecke, 2021 ). The concept of virtual worlds, in particular, brings together elements from both multiplayer video games and social media (Messinger et al., 2008 ). Together, these characteristics of research on virtual worlds and mental health might make it challenging to compare results, synthesize evidence, and draw conclusions that can effectively guide practitioners and policymakers. When the existing literature on a topic is so heterogeneous, mapping the current body of evidence through a scoping review can help clarify the concepts used and identify research gaps. This constitutes a necessary first step before moving on to more focused approaches to evidence synthesis (e.g., meta-analysis) (Khalil et al., 2016 ; M. D. Peters et al., 2020 ; M. D. J. Peters et al., 2021 ). The main contribution of this work is therefore to provide a first scoping review exploring the nature and extent of existing scientific research on the impact of virtual worlds on users’ mental health and to summarize the current evidence to inform future research in this area. 1.1 Defining the scope 1.1.1 Conceptualizing virtual worlds and mental health Throughout the long history of the term, numerous definitions of virtual worlds have been proposed, both in academic contexts (Bainbridge, 2007 ; Bell, 2008 ; Girvan, 2018 ; Messinger et al., 2008 ; Nevelsteen, 2018 ; Schroeder, 2008 ) and by private and public institutions (e.g., Cook & Kuczer, 2020 ; European Commission, 2023a ). Although there does not appear to be a single commonly shared definition, most definitions do share several core elements. The characteristics of these definitions reflect different research traditions, as well as the context and purpose of the authors in different domains, ranging from academic research to dissemination to the formulation of public policy. For a review work such as this, it is necessary to adopt a definition that captures the essence of how the concept of virtual worlds is understood across these different areas, while at the same time allowing for clear inclusion and exclusion criteria. To this end, we focus on the elements shared by most existing definitions (we do not, however, intend for these to replace the existing definitions, which are appropriate to the different contexts in which they have been proposed). First, a virtual world involves the digital representation of a three-dimensional space, which has at least some degree of persistence (i.e., “continues to exist and function after the participant has left”, Bell, 2008 ; see also Ball, 2024 ; Messinger et al., 2008 ; Nevelsteen, 2018 ). This space can take various forms, from a 2D projection (e.g., Habbo Hotel) to a fully immersive 3D virtual reality (e.g., VRChat). However, there are some definitional discrepancies (e.g., Nevelsteen, 2018 ; Schroeder, 2008 ) as to whether this spatial representation must be iconic (cf. Greenberg, 2023 ), or whether more symbolic representations (e.g., textual descriptions of space in a multi-user dungeon; cf. Downey, 2014 ) may also fall under this category. Another issue on which there is no full agreement is the extent to which augmented reality (AR) applications (e.g., Pokémon Go) can be considered virtual worlds. For some authors, the virtuality of a virtual world implies that it “lacks physical properties beyond the screen” (Girvan, 2018 , p. 1092), which would exclude those AR applications that integrate physical reality with digital representations. However, various sources accept the blending of material and digital realities as one among several possible configurations within the broader concept of a virtual world (e.g., European Commission, 2023a ; see also Nevelsteen, 2018 ). Given that the purpose of this review is to comprehensively map the multiple research efforts in this field, we follow here a broad definition of virtual worlds that includes AR. A second common element in many definitions of virtual worlds is the social nature of these applications (e.g., Bainbridge, 2007 ; Girvan, 2018 ; Messinger et al., 2008 ; Schroeder, 2008 ), which allow for synchronous social interactions between multiple users, enabled by some form of user representation or avatar within the digital environment (Bainbridge, 2007 ; Bell, 2008 ; Girvan, 2018 ). This can take different forms, for example, as a computer-generated imagery (CGI) avatar in VR applications, or as a video (e.g., of the user’s hands) in pass-through AR applications, among others. Third, most authors assume –explicitly or implicitly– that virtual worlds grant users a certain degree of freedom of action within the environment (e.g., European Commission, 2023a ; Girvan, 2018 ; Messinger et al., 2008 ; Nevelsteen, 2018 ). This contrasts with other digital environments where the user's range of possible actions is very limited (e.g., shooting games). Synthesizing these three common elements from prior definitions, we state that a virtual world is a digital simulation of a persistent 3D spatial environment (which can integrate physical reality to varying degrees) within which users, through a digital representation of (all or part of) their own body, can interact with other users in diverse ways and perform multiple activities . This inclusive definition considers applications that have historically been classified as virtual worlds (e.g., Habitat, Habbo Hotel, Second Life), various modern 3D gaming-centered worlds (e.g., World of Warcraft, Roblox, Fortnite), social VR (e.g., VRChat), and AR (e.g., Pokémon Go) applications, as well as immersive collaboration tools (e.g., Spatial.io), among other examples. Regarding mental health, we rely on the broad definition by the World Health Organization (WHO) where it is defined as "a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community." (World Health Organization, 2005 ). Drawing on an Extended Two-Continua Model of Mental Health (Meier & Reinecke, 2021 ), we consider both psychopathology and psychological well-being as crucial dimensions. Research often treats these dimensions as separate, yet both comprise unique aspects and indicators of mental health. Psychopathology refers to psychological dysfunction and distress, whereas psychological well-being captures how much an individual thrives psychologically. These two phenomena are distinct yet interrelated: Well-being is not simply the absence of psychopathology, and vice versa. Meier and Reinecke ( 2021 ) further extended this classic two-continua model by incorporating risk and resilience factors for psychopathology and psychological well-being, respectively, which we also consider in this review. 1.1.2. Research questions on Virtual Worlds and Mental Health Concerns about the potential impact of new media technologies on users’ mental health have been a recurring theme every time a new medium becomes widely adopted by the population (Orben, 2020 ). Since the earliest studies on the impact of the internet (e.g., Kraut et al., 1998 ) researchers have shown growing interest in the mental health and well-being effects of digital media, and numerous literature reviews have appeared in recent years focusing on related digital media phenomena that may be relevant for a deeper understanding how using virtual worlds can affect mental health. Arguably, the two most prominent research lines that may potentially inform investigations into virtual worlds are studies on social media (Mansfield et al., 2025 ; Sala et al., 2024 ; Valkenburg et al., 2022 ) and video games (Boldi & Rapp, 2022 ; Pine et al., 2020 ). Additional research intersecting with virtual worlds has examined the impact of AR applications on user well-being (Lee et al., 2019 ) or the use of VR in mental health treatment (Emmelkamp & Meyerbröker, 2021 ). In the case of virtual world applications, previous research provides numerous examples of the ways in which using virtual worlds can have either positive or negative effects on users’ mental health. For example, online multiplayer games or social VR platforms can help users connect with others who share similar interests or preferences, thereby expanding their social capital and even providing social support in situations when it may not be readily available in their physical environment. This can have a clearly positive impact on their psychological well-being (e.g., van Brakel et al., 2023 ; Huvila et al., 2014 ; Maloney & Freeman, 2020 ; Mandryk et al., 2020 ). Socializing and engaging in entertainment on these platforms may also help mitigate feelings of loneliness or symptoms related to anxiety or depression, particularly among vulnerable users (e.g., Gilbert et al., 2013 ). The satisfaction of basic psychological needs (e.g., need for autonomy; Partala, 2011 ) beyond socialization, or the escape from a stressful physical reality (e.g., Barreda-Ángeles & Hartmann, 2022 ), may also underlie some of the potential positive impacts of these applications on users’ psychological well-being. On the other hand, the literature has explored numerous potential detrimental effects on users’ mental health. These range from the development of problematic or addictive use of virtual world applications (e.g., Billieux et al., 2013 ; Kirby et al., 2014 ), to being harassed, which can lead to highly distressing emotional experiences (Schulenberg et al., 2023 ), to experiencing symptoms of depersonalization following the use of immersive virtual worlds (Barreda-Ángeles & Hartmann, 2023 ; Peckmann et al., 2022 ), or poorer sleep quality (e.g., Smyth, 2007 ), among other possible effects. However, to date, there are no comprehensive literature reviews available on the many potential impacts of virtual worlds on users’ mental health. While initial reviews on related media (e.g., Lee et al., 2019 ) may capture disjunct parts of the relevant research, they do not focus on the integrated concept of virtual worlds and therefore do not fulfill the broader aim of mapping research in this domain or capturing its complexity and existing research gaps. Accordingly, this scoping review had several main goals. First, we intended to examine the structural properties of the research field (cf. Arenz et al., 2023 ) –including publication rate and outlets, disciplines involved, and authors’ countries, etc.– which are essential to understand how knowledge production in this area is currently organized (Whitley, 2000 ). Second, we aimed to synthesize the key concepts, theories, and methods employed in the broad and heterogeneous research on virtual worlds and mental health. Finally, to facilitate future research in this domain, we analyzed the types of evidence available and the main gaps in the current literature. Thus, we addressed the following three research questions (RQs): RQ1 What are the structural characteristics of existing research on virtual worlds and mental health (in terms of the distribution of studies across disciplines, countries, and publication outlets, and the temporal evolution of the study of this topic)? RQ2 What conceptualizations, variables, and methodological approaches have been used to study the impact of virtual worlds on mental health? RQ3 What is the available evidence on the impact of the use of virtual worlds on users' mental health, and what mechanisms explain it? 2. Methods 2.1 Population, Concept and Context We relied on the commonly recommended “population, concept, and context” (PCC) mnemonic to clearly define the scope of relevant studies for this review (M. D. Peters et al., 2020 ; Tricco et al., 2018 ). The target population included individuals using virtual world applications, regardless of gender, age or geographical background. This primarily referred to users of virtual worlds but also encompassed others who may be directly or indirectly impacted by someone else’s use of these environments. Regarding the concept, the review included studies that examined the impact of virtual world use on mental health, even if the term “virtual worlds” was not explicitly used in a study. This encompassed research focusing on applications that may fall under our working definition of virtual worlds, which in some cases may be referred to as social virtual reality platforms (Barreda-Ángeles & Hartmann, 2022 ), Massively Multiplayer Online Role-Playing Games (MMORPGs; Cole & Griffiths, 2007 ), or sandbox video games (Rose, 2025 ), among others. To define the scope concerning mental health, we included all dimensions covered in the Extended Two-Continua Model of Mental Health (Meier & Reinecke, 2021 ). These included indicators of psychopathology (e.g., depressive symptoms, anxiety, eating disorder symptoms, etc.); psychological well-being, both hedonic (e.g., life satisfaction, affect, and discrete emotions) and eudaimonic (e.g., feelings of meaning, authenticity, and mastery); as well as risk factors (e.g., loneliness, stress, poor sleep) and resilience factors (e.g., self-esteem, social support, social capital, good sleep). After consulting an expert (a video gamer and content streamer with several years of experience on the field, who also collaborates with an NGO working on safer online gaming environments), we decided to also include other additional indicators related to problematic use (addictive behaviors, cyberbullying, harassment, and toxicity). In terms of context, the review covered both studies on the everyday use of virtual worlds (e.g., user surveys) and those conducted in experimental settings (e.g., laboratory studies). See the review protocol for further details [LINK REMOVED FOR ANONYMOUS REVIEW]. 2.2 Inclusion criteria The review was conducted following the previously published protocol [LINK REMOVED FOR ANONYMOUS REVIEW], which was designed by integrating various existing recommendations (Khalil and colleagues; 2016 ); M. D. Peters et al., 2020 ; Westphaln et al., 2021 ). We decided to include studies: examining the impact of virtual worlds (as defined in Section 1.1.1 ) on dimensions and indicators of mental health (as described in section 2.1 ); providing primary quantitative or qualitative data from users (or participants in laboratory studies); published as journal articles, conferences proceedings, or pre-prints; and written in English. 2.3 Search strategy, paper screening and selection We systematically searched the Web of Science and Scopus databases, and the PubMed search engine. The search strategy was developed in a three-step, iterative approach (Khalil et al., 2016 ). It included, first, an initial search using a search string containing main keywords related to virtual worlds and mental health (Step 1); second, reviewing the titles, abstracts, and keywords of the retrieved references to identify additional potentially relevant keywords (Step 2); and, third, analyzing the reference lists of the retrieved studies to identify other potentially relevant studies (Step 3). For Step 1, we combined a list of keywords related to virtual worlds with a list of keywords related to mental health. The initial list of keywords for virtual worlds included the term “virtual worlds” as well as potentially overlapping terms such as “metaverse”, “massively multiplayer online role-playing game”, “social virtual reality [platform]”, “sandbox video game” and “open-world video game”. We also included an extensive list of (potentially) related branded applications, such as World of Warcraft, Fortnite and Roblox (see Supplementary Materials for the complete list). Once studies addressing any of these applications were found, we analyzed the application in question to determine whether it indeed met our definition of virtual worlds and revised the search string accordingly. Regarding the list of keywords related to mental health, we used the terms associated with the dimensions and indicators included in the Extended Two-Continua Model of Mental Health (cf. Meier & Reinecke, 2021 ) and problematic use, as described in section 3.1 . The final search string is reported in the Supplementary Material. The initial search yielded a total of 10,393 records, of which 9,354 remained after removing duplicates. Title and abstract screening was conducted by two authors, who used ASReview to apply a “human-in-the-loop” machine learning approach, which means that human screening decisions trained an active learning model to identify relevant papers in the 9,354 records, prioritize those records for screening, and thereby reduce overall screening load (Van De Schoot et al., 2021 ). A composite stopping rule was defined for this process, following recommendations from Boetje and Van De Schoot ( 2024 ) (see Supplementary Material for details). A total of 992 out of the 9,354 records were reviewed by both authors in ASReview before the stopping rule criteria were met, resulting in 307 records selected for full-text screening. Regarding interrater reliability, the kappa value for these two coders, considering all records in the sample and the use of the ASReview tool, was 0.77 (95% confidence interval: 0.73–0.80), which corresponds to very good accuracy (98%) according to the interpretation proposed by Bakeman ( 2022 ). In cases of disagreement, the two reviewers discussed the application of the inclusion criteria until reaching a consensus. Subsequently, a complementary search (using the same databases as in the original search) was conducted (Step 2), incorporating additional terms that emerged from the initial screening or variations of those previously used. The search strings used for updating the literature search are reported in Supplementary Material. The newly identified articles were again screened for inclusion based on titles and abstracts, leading to 172 additional records (after removing duplicates). Finally, we conducted forward and backward literature searches based on all so far identified records (Step 3). To do so, first, references citing the articles in our sample of 307 records were identified using the OpenAlex database and screened for title and abstract. Then, references cited in included sample of 307 articles were examined using ResearchRabbit ( https://www.researchrabbit.ai/ ) (forward and backward searches) and screened. After removing duplicates, this process produced another 64 new records. In total, we identified 543 records, of which we could retrieve 522 full text articles. To conduct a final round of in-depth screening based on the 522 retrieved full texts, we first piloted our coding sheet to fully align individual reviewers’ understanding of the inclusion and exclusion criteria. Full-text screening of the identified articles was then carried out by all co-authors, with each article being reviewed by two authors. After discussing iteratively the results (e.g. in cases of disagreement) among all co-authors, the final sample consisted of 198 included articles, containing 201 different studies. Figure 1 summarizes the search and screening process following PRISMA guidelines (Page et al., 2021 ). 2.4 Data extraction and analysis We iteratively developed and piloted a data extraction sheet, included on our protocol [LINK REMOVED FOR ANONYMOUS REVIEW]. The data extraction project involved, first, to extract information on whether the study was grounded in an explicitly mentioned theoretical framework or conceptual model and the type of conceptual and operational approach adopted. For the latter two, we relied on the classification proposed by Meier and Reinecke ( 2021 ). Their review organizes concepts in digital technology effects research based on whether studies follow a channel-centered or communication-centered approach. In the channel-centered approach, the focus is on technology variables (including devices, types of applications, specific branded applications, or app features). In the communication-centered approach, the focus is on variables related to social interaction via a technology (e.g., type of social tie involved or content of an interaction). Finally, to organize different operational approaches, their classification further distinguishes between technology-centered studies, which measure variables directly linked to technology use (e.g., time or frequency of use), and user-centered studies, which focus on variables related to user experience (e.g., motivations, sense of presence, enjoyment). In terms of mental health variables, we coded whether the study measured any of the indicators or dimensions included in the Extended Two-Continua Model of Mental Health (see section 3.3 ; cf. Meier & Reinecke, 2021 ), as well as variables related to problematic use, including addictive behaviors, cyberbullying, harassment, and toxicity. Regarding the methodology, we first coded whether the study design was quantitative, qualitative, or mixed-methods, along with the specific type of design within these categories (e.g., cross-sectional surveys, interviews, etc.); second, whether the study focused on a specific application; third, the characteristics of the sample used (e.g., sample size, geographic location, age group, whether the sample was gender-balanced, and other attributes); and, finally, the type of analysis conducted. At the level of the evidence provided, we coded whether the study employed a design capable of providing causal evidence, and if so for which variables; whether mechanisms of the observed effects were explored (through mediation analysis); and whether effect heterogeneity was analyzed (via moderation analysis). The final list of extracted and analyzed variables can be found in the Supplementary Material. Two pilot tests were conducted for coding the articles. In each pilot, the same two coders applied the data extraction form to a random set of ten articles, different in each pilot. The pilots allowed to identify and discuss discrepancies among coders, refine the data extraction process, and fully align the coders’ criteria. In the second pilot, the two coders achieved a kappa value of 0.69 (95% confidence interval: 0.47–0.92), corresponding to an uncorrected agreement rate of 88%. To produce a kappa of this magnitude, simulated observers would need to be 92% accurate, which exceeds the recommended target of 85–90% (Bakeman, 2022 ). Therefore, after the second pilot and additional discussion to resolve disagreement and increase alignment in data coding/extraction, all remaining articles were coded by one of the two coders. In the following, we present both a descriptive analysis of the full dataset, based on the variables included in the data extraction form, and a bibliometric analysis using the R package bibliometrix (Aria & Cuccurullo, 2017 ). The descriptive analysis was based on all 198 articles, including 201 empirical studies, while the bibliometric analysis was conducted on the 194 articles available in a format compatible with bibliometrix (Scopus format). 3. Results 3.1 Structural characteristics of the field (RQ1) Our RQ1 focused on the structural characteristics of the research, particularly on the temporal evolution of literature and the distribution of studies across disciplines, countries, and publication outlets. The bibliometric analysis shows that annual scientific production has experienced a steady increase, particularly from 2010 onwards. Notable peaks were observed in 2011, 2014, 2020 and 2023, with the highest number of articles published in 2023 (25 papers). Average citations per year suggest that, not surprisingly, earlier publications, particularly those from 2005 to 2007, have received slightly more attention over time (see Fig. 2 for details). The sample included 111 unique publication outlets (academic journals and conference proceedings). To analyze the distribution of studies across disciplines, we used the subject areas assigned to each publication outlet in the Scimago Journal & Country Rank ( https://www.scimagojr.com/ ) (two of the outlets were not listed in this database). A total of 14 disciplines were represented, and approximately half (58 out of 111, i.e., 52.3%) of the outlets were associated with more than one discipline. The most common subject area among the articles was Computer Science (45 records, 40.5% of the total unique publication outlets), followed by Medicine (37, 33.3%), Social Sciences (36, 32.4%), Psychology (27, 24.3%), and Business, Management and Accounting (12, 10.8%). Core sources in the field include Computers in Human Behavior (33 documents), Cyberpsychology, Behavior and Social Networking (9), Proceedings of the ACM on Human-Computer Interaction (7), and Frontiers in Psychology (5). These venues combine psychological and technological perspectives, which is consistent with the highly interdisciplinary nature of the field. The United States leads in overall publication volume with 72 articles, including 17 involving international collaboration as lead author (MCP leading), but with no recorded participation as a non-leading collaborator. This reflects a strong but self-driven research presence. The United Kingdom follows with 24 articles, showing six MCPs as lead and 1 as collaborator. Countries like Switzerland stand out for their collaborative role: although leading only one article nationally, it appears as lead in 12 international publications (11 of them with European countries) and as co-author in two more, confirming its central position in European collaboration networks. Germany, Belgium, Canada and Norway also appear frequently as non-leading contributors (four or more co-authored articles each), indicating their strong integration in multilateral research. These patterns suggest that while countries like the United States and China dominate in volume, European countries show greater interconnectedness in the virtual worlds and well-being research landscape. The analysis of authors’ keywords (Fig. 3 ) shows the dominant concepts of the field. The most frequent terms include “online games” (32 occurrences), “MMORPG” (29), “virtual worlds” (29), “gaming disorder” (27) and “addiction” (23). Other relevant concepts include “social VR” (22), “Pokemon Go” (19), “world of warcraft” (17) and “metaverse” (16), suggesting growing diversification in platforms and modalities of interaction under study, ranging from traditional gaming environments to newer immersive VR and location-based AR technologies. The keyword co-occurrence network based on authors’ keywords (generated using the Walktrap algorithm; Pons et al., 2005) reveals five well-defined thematic clusters (see Fig. 4 ). Two large central clusters are closely interconnected: the blue cluster (right-center) appears to reflect early research on virtual environments (“online games,” “virtual worlds,” “Second Life”) with a focus on their social and psychological impact (“social capital,” “online community,” “psychological needs”); the orange cluster (upper-center) represents the abundant research activity on problematic use (“gaming disorder,” “problematic internet use,” “stress”) related to gaming platforms (“MMORPGs”), particularly studies focusing on World of Warcraft. A smaller and relatively isolated red cluster (lower-center) captures a distinct research stream focused on the positive psychosocial and health effects (“well-being,” “mental health,” “life satisfaction”) of AR and physical movement embedded in game mechanics (“Pokémon Go,” “augmented reality,” “location-based games”). Interestingly, the presence of “COVID-19” in this cluster may reflect studies that examined AR games as tools for maintaining well-being, social interaction, or physical activity during lockdowns or periods of restricted mobility. The small green cluster (left-center) focuses on immersive experiences mediated through VR and metaverse platforms, with frequent connections to terms such as “loneliness”, “social support”, and “social presence.” The emphasis on these constructs suggests that this research stream explores how immersive technologies may offer avenues for enhancing connectedness or compensating for social deficits, particularly in vulnerable populations. Finally, an even smaller violet cluster (bottom-left), partially overlapping with the green one, reflects emerging literature on the risks and challenges (“harassment”) of embodied interactions (“presence,” “avatars”) in social VR spaces. For additional thematic analysis, see the Supplementary Material. Furthermore, we analyzed how research interests have evolved over time within the specific corpus of 194 publications included in our study on virtual worlds and mental health. Figure 5 presents the temporal distribution of the most frequent author keywords across the period 2005–2024, capturing both the emergence of new concepts and the continuity of foundational themes. Several terms show early relevance in the literature, notably “online community”, “problematic internet use”, and “second life”, which appear from 2008, 2010, and 2011, respectively, to 2014. These themes reflect an initial scholarly focus on the social dynamics within early virtual platforms. However, their term frequency remains relatively low, suggesting niche attention or a subsequent decline in interest. Between 2012 and 2018, keywords such as “virtual worlds”, “MMORPG”, “addiction”, “social capital” and “online games” dominate the landscape, with terms like “online games” (32 occurrences) and “MMORPG” (29) sustaining visibility over an extended period. This era likely represents the consolidation of research on the psychological and behavioral impacts of massively multiplayer online environments, including concerns around gaming disorder (27) and addiction (23). However, a noticeable thematic shift emerges around 2019, with the rise of health-related terms such as “well-being”, “mental health”, “depression”, “social support” and “covid-19”. These terms indicate a growing interest in the potential therapeutic or harmful effects of digital environments on psychological outcomes, possibly accelerated by the pandemic’s impact on digital socialization and health. In recent years, technologically updated concepts such as the “metaverse” and “social VR” gained prominence, particularly since 2021. These terms are among the most recent yet also the most frequent, indicating a rapidly growing subfield focused on immersive, next-generation virtual environments. Notably, “metaverse” appears with high frequency in a short span, suggesting its centrality in current discourse and potential as a future anchor for scholarly debates about virtual worlds and mental health. 3.2 Conceptualizations, variables, and methodological approaches (RQ2) Our RQ2 inquired about the conceptualizations, variables used, and methodological approaches adopted in studies on virtual worlds and mental health. The results presented in this section are based on our manual descriptive annotation of the 198 articles. Regarding the conceptualization of the studies, the majority (70.6%) of the analyzed research adopts a quantitative approach. Among these, approximately three-quarters (72.5%) rely on cross-sectional surveys, while experimental, quasi-experimental, and longitudinal designs are scarce. One-fifth (21.4%) of all reviewed studies are qualitative in nature, and a small proportion (8%) uses a mixed-methods approach combining quantitative and qualitative methods (see Table 1 for more details). Almost half of the qualitative studies (44.2%) are based on interviews; one fifth employs more than one qualitative method (20.9%), and smaller proportions use content analysis (11.6%) or ethnography/participant observation (7%). Around half of the studies do not explicitly rely on any specific theoretical framework or conceptual model. Among those that do (48.3% of the total), the most frequently used theories are self-determination theory (Deci & Ryan, 2000 ) and theories of social capital (e.g., the theoretical approach by Putnam, 1995 ). In terms of levels of analysis and following the classification proposed by Meier and Reinecke ( 2021 ), a clear majority of studies (60.7%) are channel-centered, and among these, the focus tends to be on specific branded applications. The most common are, in descending order, World of Warcraft (24.4% of all studies focus on this application), Second Life (15.4%), and Pokémon Go (13.4%). Communication-centered studies represent around 27% of all reviewed studies. They focus on variables such as the specific activities that the participants do in a virtual world platform, although no particular variable clearly dominates. About 22% of the studies, in turn, combine channel-centered and communication-centered approaches. At the operational level, we find a similar percentage of studies that adopt technology-based operationalizations (e.g., measuring variables such as time spent on virtual worlds) and user-centered operationalizations (e.g., user motivations), while approximately one-third of studies (35.3%) combine both measurement approaches. When looking at the variables used to measure user mental health, we find a relatively balanced distribution across different dimensions. The most frequent approach uses indicators of hedonic well-being, with around 40% of studies analyzing aspects such as quality of life or emotions, followed by studies examining problematic use (e.g., addiction) (36.8%), and resilience factors (35.3%) such as social capital. Table 1 Descriptive statistics of the included studies (overall and by methodological approach) Variable Total sample (N = 201) n (%) Quantitative (n = 142) n (%) Qualitative (n = 43) n (%) Mixed (quantitative and qualitative) (n = 16) n (%) Study design a Cross-sectional 103 (51.2) 101 (72.5) - - Mixed (any) b 29 (14.4) 4 (2.8) 9 (20.9) 16 (100) Interviews 19 (9.5) - 19 (44.2) - Quasi-experimental 16 (8) 16 (11.3) - - Experimental 12 (6) 12 (8.5) - - Longitudinal 5 (2.5) 5 (3.5) - - Content analysis 5 (2.5) - 5 (11.6) - Ethnography/participant observation 3 (1.5) - 3 (7) - Theoretical framework and/or conceptual model a 97 (48.3) 77 (54.2) 12 (27.9) 8 (50) Self-determination 15 (7.5) 13 (9.2) 2 (4.7) 0 Social capital 10 (5) 8 (5.6) 2 (4.7) 0 Need satisfaction 5 (2.5) 4 (2.8) 0 1 (6.3) Motivations 5 (2.5) 4 (2.8) 0 1 (6.3) Conceptualization c Channel-centered 122 (60.7) 112 (78.9) 2 (4.7) 8 (50) • device 2 (1.6) 2 (1.8) 0 0 • type of application 27 (22.1) 26 (23.2) 0 1 (12.5) • branded application 76 (62.3) 69 (61.6) 2 (100) 5 (62.5) • features 17 (13.9) 15 (13.4) 0 2 (25) Communication-centered 54 (26.9) 47 (33.1) 1 (2.3) 6 (37.5) Channel- and communication-centered 44 (21.9) 41 (28.9) 0 3 (18.8) Most studied platforms World of Warcraft 49 (24.4) 36 (25.4) 6 (14) 7 (43.8) Second Life 31 (15.4) 23 (16.2) 7 (16.3) 1 (6.3) Pokémon Go 27 (13.4) 24 (16.9) 3 (7) 0 MMORPGs 17 (8.5) 15 (10.6) 2 (4.7) 0 Social VR 15 (7.5) 3 (2.1) 11 (26.6) 1 (6.3) Operationalization c Technology-centered 113 (56.2) 100 (70.4) 2 (4.7) 11 (68.8) User-centered 103 (51.2) 90 (63.4) 2 (4.7) 11 (68.8) Technology- and user-centered 71 (35.3) 62 (43.7) 1 (2.3) 8 (50) Psychopathology d 62 (30.8) 47 (33.1) 13 (30.2) 2 (12.5) Anxiety 24 (11.9) 18 (12.7) 5 (11.6) 1 (6.3) Social anxiety 18 (9) 13 (9.2) 4 (9.3) 1 (6.3) Depression 33 (16.4) 27 (19) 5 (11.6) 1 (6.3) Hedonic well-being d 80 (39.8) 56 (39.4) 16 (37.2) 8 (50) Affect/emotions 39 (19.4) 23 (16.2) 10 (2.3) 6 (37.5) Life satisfaction 19 (9.5) 17 (12) 0 2 (12.5) Quality of life 9 (4.5) 6 (4.2) 3 (7) 0 Well-being 15 (7.5) 11 (7.7) 4 (9.3) 0 Eudaimonic well-being d 27 (13.4) 16 (11.3) 8 (18.6) 3 (18.8) Authenticity 5 (2.5) 3 (2.1) 1 (2.3) 1 (6.3) Meaningfulness 6 (3) 2 (1.4) 3 (7) 1 (6.3) Mastery 3 (1.5) 1 (0.7) 1 (2.3) 1 (6.3) Self-determination needs 8 (4) 6 (4.2) 1 (2.3) 1 (6.3) Risk factors d 44 (21.9) 29 (20.4) 10 (23.3) 5 (31.3) Loneliness 28 (13.9) 19 (13.4) 8 (18.6) 1 (6.3) Poor sleep 5 (2.5) 4 (2.8) 1 (2.3) 0 Stress 17 (8.5) 11 (7.7) 1 (2.3) 5 (31.3) Resilience factors d 71 (35.3) 45 (31.7) 20 (46.5) 6 (37.5) Self-esteem 14 (7) 11 (7.7) 2 (4.7) 1 (6.3) Good sleep 4 (2) 2 (1.4) 2 (4.7) 0 Social support 31 (15.4) 14 (9.9) 12 (27.9) 5 (31.3) Social capital 25 (12.4) 20 (14.1) 5 (11.6) 0 Problematic factors d 74 (36.8) 48 (33.8) 16 (37.2) 10 (62.5) Addiction 57 (28.3) 43 (30.3) 7 (16.3) 7 (43.8) Harassment 16 (8) 5 (3.5) 8 (18.6) 3 (18.8) Toxicity 5 (2.5) 1 (0.7) 2 (4.7) 2 (12.5) Sample size median (range: min. – max.) 218.5 (1–1,520,025) 312 (6–1,520,025) 19 (1–402) 129.5 (10–438) Age median (range: min. – max.) 26.7 (9–67) 26.8 (9–67) 25.7 (12–58.7) 29.8 (9.8–56) Location known 104 (51.7) 80 (56.3) 16 (37.2) 8 (50) • North America 50 (48.5) 36 (45.6) 6 (37.5) 8 (100) • Europe 27 (26.2) 23 (29.1) 4 (25) 0 • Australia 3 (2.9) 2 (2.5) 1 (6.3) 0 • Asia 17 (16.5) 13 (16.5) 4 (25) 0 • South America 1 (1) 1 (1.3) 0 0 • Mixed 5 (4.9) 4 (5.1) 1 (6.3) 0 Gender Balanced 53 (30.8) 42 (32.6) 7 (25.9) 4 (25) Males (60% or more) 88 (51.2) 64 (49.6) 15 (55.6) 9 (56.3) Females (60% or more) 31 (18) 23 (17.8) 5 (18.5) 3 (18.8) Mediation 30 (14.9) 28 (19.7) 0 2 (12.5) Moderation 26 (12.9) 24 (16.9) 1 (2.3) 1 (6.3) Note . a : the total does not correspond to N as the rest of the studies adopted distinct study designs. b : regardless of approaches used (only qualitative or quantitative, or both). c : coded only for studies using measures to analyze a specific level (i.e., information not coded for most qualitative studies). d : the total n of subdomains is not equal to the total n of mental health domains as a study could have explored different subdomains. The median sample size is 312 for quantitative studies and 19 for qualitative ones. Among the studies that report participants’ age the median is 26.7. Only 17 studies (8.5% of the sample) focus specifically on children and teenagers, and only five studies (2.5%) address older adults. Many studies recruit participants online, and for around half of the analyzed studies, the participants’ geographic origin is unknown. Among those that do report this information, participants from the United States clearly dominate (48.5%), followed by European participants (26.2%), while other regions are underrepresented. Around 30% of the studies use participant samples with balanced gender representation (no more than 60% of participants identifying as one gender). However, in more than half of the studies (51.2%), over 60% of the sample consists of men, indicating a predominance of male participants in this research field. The representation of minorities is relatively low: three studies (1.4% of the sample) focus on racial or ethnic minorities; two studies (0.9%) on LGBTQ + users; and ten studies (4.7%) focus on participants with physical or mental disabilities or neurodivergent individuals. Some of the studies analyzed (31 studies; 15.4% of the sample) focus on a specific area of application, in particular, mental healthcare (21; 10.4%) and, to lesser extent, education (4, 2%). In line with the proportion of quantitative and qualitative studies in our sample, the most common type of analysis is some form of inferential statistics (141 studies, 70.1% of the sample), followed by content or thematic analysis (56, 27.9%) and ethnographic analysis or narrative description (5, 2.5%). Nine studies (4.5% of the sample) do not conduct any form of in-depth analysis (e.g., they only reported descriptive statistics). 3.3 Evidence of effects and mechanisms (RQ3) Our RQ3 addressed the existing evidence regarding the impact of virtual worlds on users’ mental health and the mechanisms that may explain these effects. To examine this question, we first examined the availability of causal evidence in the studies analyzed. Only 35 out of 201 studies (i.e., 17.4%) employed methods that can provide causal evidence or approximate it, such as experimental, quasi-experimental, or longitudinal designs. Of these, roughly half investigate the use of virtual worlds as a tool in a therapeutic or psychological support context (e.g., for psychological therapy, support groups, etc.) or for training and educational purposes (e.g., in a behavioral skills training program), yet mental health effects of virtual worlds are rarely addressed in their primary context of use –namely, entertainment or socialization. Among the studies using experimental or longitudinal methods that do examine the impact of virtual worlds in this primary context, the examined applications and mental health outcomes are highly heterogeneous, making it difficult to synthesize evidence across studies (see Supplementary Material for additional details). Furthermore, although a full risk-of-bias assessment was beyond the scope of our review, a preliminary examination suggests that many of these studies have significant limitations, such as failing to control for potential confounders or relying on suboptimal methodological and analytical choices, which may undermine the quality of the evidence provided. From the studies in this group that do appear to provide initial causal evidence, the diversity of platforms and outcomes assessed prevents drawing firm conclusions regarding the direction (positive or negative) of the effects. For example, Gilbert et al. ( 2013 ) suggest that virtual worlds may help improve affective states in users with disabilities. However, the study by Zhang and colleagues ( 2023 ) shows that using avatars with disability signifiers in VRChat may expose users to various forms of embodied, explicit harassment. Therefore, considering the different applications and outcomes targeted, it is difficult to assess whether, overall, the use of these applications may pose more risks or benefits for this population at this point in the field. With respect to the mechanisms underlying the observed effects, it is noteworthy that among studies employing quantitative methods, only 19.7% of them (and just 3 studies, i.e., 8.5% of those using experimental or longitudinal designs) conducted mediation analyses, which has been a commonly used approach to examine causal mechanisms (Hayes, 2009 ; although this approach should be treated with caution, see Rohrer et al., 2022 ). Yet, the available evidence on the mechanisms underlying a potential link between the use of virtual worlds and mental health is particularly limited. 4. Discussion With digital technology becoming more immersive and widespread, users increasingly frequent virtual worlds, raising questions about potentially beneficial and detrimental effects on mental health. To map existing knowledge, we identified and reviewed 198 articles addressing many different angles of the topic. Our work presents the first integrative scoping review of research examining virtual worlds in conjunction with user mental health. Our bibliometric analysis reveals that research on virtual worlds and mental health is a field in active expansion, in terms of number of publications, thematic scope, and technological focus. Findings suggest rapidly increasing academic interest, likely catalyzed by developments in immersive technologies, societal interest in the metaverse (cf. Dolata & Schwabe, 2023 ; Jacobides et al., 2024 ), and broader societal shifts, among which the COVID-19 pandemic seems to have been central. While early publications laid the conceptual foundations around online communities and platforms like Second Life, the field has progressively moved toward more specialized concerns, such as gaming disorder and the psychosocial impacts of MMORPGs. We identify five clusters of keywords that represent interconnected but distinguishable research trajectories. While all clusters engage with the relationship between virtual environments and mental health, they do so from diverse conceptual angles: from community building and addiction to physical well-being and online safety. Notably, the clustering of problematic gaming with MMORPGs (rather than casual mobile or augmented reality games) versus positive well-being themes studied in an augmented reality context may suggest contrasting assumptions about the affordances and risks of different technological modalities, or how these are used. Overall, the field shows a progressive expansion from early sociological perspectives on online communities to more nuanced and diverse explorations including augmented reality (with keywords like “Pokémon Go”, “augmented reality”), self-perception (“self-esteem”, “social anxiety”, “avatar”), and emotional well-being. This evolution illustrates how research on virtual worlds is increasingly addressing broader societal concerns from mental health to identity and digital inclusion. Taken together, these findings suggest that research on virtual worlds and mental health is evolving toward greater complexity, increasingly embracing interdisciplinary dialogues at the intersection of psychology, human-computer interaction, and public health. Further research could explore to what extent these thematic separations reflect underlying disciplinary divides (e.g., clinical psychology vs. media studies) or methodological biases (e.g., qualitative vs. quantitative designs), and whether they are shifting with the emergence of new platforms. Among the studies analyzed, a predominant approach focuses primarily on the technologies themselves (channel-centered approach), and less on how these technologies are used for social interaction (communication-centered approach), which aligns with the general trend observed in research on digital media and mental health (Meier & Reinecke, 2021 ). This may indicate a predominantly techno-deterministic perspective (i.e., the tendency to expect a direct and uniform impact of technology on users) which is also present in much previous research on social media and mental health, as noted by Orben ( 2020 ). Similarly, most of the analyzed studies focus on specific, branded applications (e.g., World of Warcraft, VRChat) rather than on their design characteristics, affordances, or content. This type of approach often conflates the specific causes responsible for technology effects and stands in the way of robust comparisons across technologies and platforms (Orben, 2020 ). A stronger focus on the specific technological features and the subjective affordances they enable as well as key social interaction and content characteristics of virtual worlds could produce comparable and cumulative evidence regardless of which applications happen to be trendy at any given moment (Meier et al., 2024 ). One striking finding of our review is that, despite the tendency in the literature to focus on branded applications, there is a marked lack of studies targeting some of the most widely used platforms today, such as Roblox, Fortnite, or Minecraft. In particular, these three platforms are used by tens of millions of users, many of them, children and adolescents (Clement, 2025 ; Kumar, 2025b ). The lack of attention to these platforms is especially notable given media reports highlighting potential risks associated with them (e.g., in terms of exposure to inadequate content, addiction, etc.; e.g., Brooks & Otte, 2025 ). While we do not advocate for single-platform research, it appears urgent to include these more recent platforms in comparative studies of virtual worlds, ideally, as noted above, through the analysis of their technological features, affordances, and contents. Another notable aspect is the predominance of quantitative studies based on cross-sectional surveys, which offer limited capacity to provide causal tests of the effects of virtual worlds on users. There is a noticeable lack of experimental, quasi-experimental, or longitudinal studies. This trend is consistent with what has been observed in other areas of digital media and mental health research (cf. Mansfield et al., 2025 ). Combined with the high heterogeneity of studies in this field –in terms of both how technology use is conceptualized and the mental health outcomes assessed (reflecting the multiple disciplines involved in this research)–, this suggests that attempts to synthesize evidence through meta-analysis may still be premature. Relatedly, the relatively high number of qualitative studies may reflect the fact that this heterogeneous research community is still in the process of defining the key variables of interest, as a necessary step prior to measuring them quantitatively. This highlights the value of joint, interdisciplinary efforts to define key constructs and the ways in which they can be measured, for example through standardization initiatives (Meier et al., 2024 ), which could be inspired by standardization processes in the technology sector. In this context, collaboration with stakeholders who can offer perspectives beyond academic research may be a key success factor (Mansfield et al., 2025 ). The predominant focus of available studies on Western, young adult samples also underscores the need to pay greater attention to some specific and underrepresented populations of users. Greater inclusion of (or specific focus on) children and adolescents, women, users from disadvantaged backgrounds, and other populations (e.g., ethnic and racial minorities, LGBTQIA + users, users with disabilities) particularly vulnerable in online environments (e.g., Schulenberg et al., 2024 ) will be crucial for informing the design and regulation of virtual worlds to ensure they are inclusive and beneficial for all types of users. 4.1 Limitations As with any literature synthesis, one limitation of this scoping review is that it may have overlooked relevant work. However, our broad inclusion of search terms, the addition of new keywords after the initial search, and our thorough forward and backward searches helped minimize this risk, though we cannot rule out overlooking very recent work (especially studies published after February 2025, when our forward and backward searches were conducted). Furthermore, given the rapidly evolving nature of this field, it is possible that new relevant studies emerged during the production of this paper or shortly after its completion. Therefore, it is important to keep in mind the temporal scope of this review when interpreting the results. Additionally, our search employed commonly used general academic databases (Web of Science, Scopus, PubMed), so it is possible that some records appearing only in more specialized databases were not identified in our search. In addition, articles in languages other than English were excluded. Our findings also depend on the definition of virtual worlds that we adopted. Our focus has been mainly on consumer applications. Some other possible instances of virtual worlds (e.g., digital twins of cities or factories; Grieves, 2023 ), whose potential effects –if any– on users’ mental health are likely to be much more indirect, may have been left out of our review. On the other hand, we excluded some studies due to definitional ambiguity (e.g., studies evaluating video games without specifying which ones or their key characteristics), but we believe there is no substantive reason to expect that these would systematically diverge (conceptually or methodologically) from the studies included in our review. As such, their exclusion is unlikely to compromise the validity or generalizability of our findings. 5. Conclusions Our literature review shows that the growing role of virtual worlds in users' lives is indeed reflected in the expanding number and increasing complexity of scientific studies examining their impact on mental health. This impact has been addressed from numerous disciplines, perspectives, and viewpoints. While such richness of approaches is likely necessary to capture the complexity of these effects, it also complicates the comparison between studies and the accumulation of transferable evidence across them. Our results suggest that research streams on topics related to virtual worlds and mental health seem to emerge (and fade) in parallel with the popularity of specific platforms (e.g., Second Life, World of Warcraft, Pokémon Go). As a result, the existing literature appears to cluster around platforms and outcome types, with no integrated conceptualization of virtual worlds shared across different strands of research. Our work represents an important first step in this direction. In future studies, approaches centered on the technological characteristics and user affordances of various platforms may be key to generating valid and transferable evidence across contexts. Despite the diversity of theoretical and methodological perspectives used in the literature, a technocentric view currently prevails; a tendency also common in other areas of research on digital media and mental health. This view often downplays the role of specific patterns of use, individual characteristics, contexts, and other non-technological factors that may shape the impact of virtual worlds. Future research will need to pay closer attention to these dimensions, as well as to the experiences of minority groups and vulnerable users, in order to fully capture the complexity of potential effects and produce evidence that reflects users’ diversity. While virtual worlds are used by more and more people around the globe, our findings highlight the current lack of causal evidence regarding their potential mental health impacts. They also underscore the urgent need for multidisciplinary approaches and greater coordination among researchers to navigate the complexity of this issue. Within the context of the increasing public debate on the impact of digital media on mental health (Di Iorio et al., 2025 ; Stokel-Walker, 2025 ), we hope that our mapping of existing research can contribute to develop future studies more effectively, and facilitate the urgent scientific scrutiny of these platforms. Declarations Competing interests The authors declare no competing interests. Ethical statement This article does not contain any studies with human participants performed by any of the authors. Author Contribution M.B-Á. and I.H. carried out the initial conceptualization. All authors contributed to the development of the methodology and the formal analysis. M.B-Á., S.A., and I.H. were responsible for data curation. M.B-Á. and S.A. wrote the original draft. I.H. prepared the figures. All authors reviewed and edited the manuscript. Acknowledgments This work received funding from the Centre for Advanced Studies of the Joint Research Centre (JRC) of the European Commission, thought the project Virtual Worlds and Society (VirtueS) Data Availability The dataset containing the bibliographic references of the articles included in the review is publicly available at [BLINDED FOR ANONYMOUS REVIEW]. References Almeida F (2017) Concept and dimensions of web 4.0. Int J Comput Technol 16(7) https://doi.org/10.24297/ijct.v16i7.6446 Apple (2023). Introducing Apple Vision Pro: Apple’s first spatial computer. https://www.apple.com/uk/newsroom/2023/06/introducing-apple-vision-pro/ Accessed 7 Sep 2025 Arenz A, Meier A, Reinecke L (2023) Social comparison on social media and mental health: A scoping review. In: Ziegele M, Kümpel AS, Dienlin T (ed) Beiträge zur Jahrestagung der Fachgruppe Rezeptions- und Wirkungsforschung, Düsseldorf, 2022, p. 7 Aria M, Cuccurullo, C. 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2","display":"","copyAsset":false,"role":"figure","size":150692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResults of the bibliometric analysis: Annual scientific production; average citations per year; and most productive countries (where SCP are Single Country Publications and MCP are Multiple Country Publications, distinguishing between leader and collaborator roles).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7561416/v1/dc95e8c69c2d383c04400e9c.png"},{"id":92858753,"identity":"411d4f89-e63e-4a9a-8f25-1c27e891f2e9","added_by":"auto","created_at":"2025-10-06 11:55:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMost relevant authors’ keywords\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7561416/v1/f741d8c9aacb6678d415f8be.png"},{"id":92857807,"identity":"7668eefd-c942-4b6c-8c1a-764c6a236b03","added_by":"auto","created_at":"2025-10-06 11:47:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKeyword co-occurrence network (computed from authors’ keywords)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7561416/v1/be770214580ce29ffcab2321.png"},{"id":92857798,"identity":"9ef8ff39-e6d1-41e5-adc3-8476b274b8c0","added_by":"auto","created_at":"2025-10-06 11:47:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTrend topics over the years (2008-2024).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7561416/v1/9c7ea4bbf2b0cc49b74312c6.png"},{"id":92861030,"identity":"53f30a46-278c-4cee-88d4-f20f15463739","added_by":"auto","created_at":"2025-10-06 12:19:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1604546,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7561416/v1/d86979ac-06bc-4df7-9d95-ec14fe94a72a.pdf"},{"id":92859358,"identity":"af96a829-a967-40cc-82db-0036e4146baf","added_by":"auto","created_at":"2025-10-06 12:03:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":129350,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7561416/v1/16412867f31346ffa9df77c3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Virtual worlds and mental health: A scoping review","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent decades, the number of users of virtual worlds \u0026ndash;understood here as three-dimensional digital environments where users can interact with one another and engage in a wide range of activities\u0026ndash; has skyrocketed, with applications such as Roblox and Fortnite, for instance, attracting tens of millions of users each month (Kumar, 2025; Statista, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The emergence of concepts such as metaverse (Ball, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), spatial computing (Apple, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Web 4.0 (Almeida, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) suggests that virtual worlds may be progressively gaining a more prevalent role in the evolution of digital connectivity over the coming years (Hupont Torres et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a response to the increasing presence of virtual worlds in people's digital lives, scholars (e.g., Paquin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), policymakers (e.g., European Commission, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) and citizens (e.g., European Commission, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) have stressed the need to understand the potential impact of these applications on their users, and particularly on users' mental health.\u003c/p\u003e\u003cp\u003eInterest in the mental health impact of digital media in general (Orben, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Meier \u0026amp; Reinecke, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and virtual worlds, more specifically, is not new (e.g., Gilbert et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Photiadis \u0026amp; Zaphiris, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, past research on digital media and mental health has been conducted across different disciplines, using diverse conceptual and methodological frameworks, and often employing very heterogeneous and inconsistent terminology and approaches (cf. King et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Meier \u0026amp; Reinecke, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The concept of virtual worlds, in particular, brings together elements from both multiplayer video games and social media (Messinger et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Together, these characteristics of research on virtual worlds and mental health might make it challenging to compare results, synthesize evidence, and draw conclusions that can effectively guide practitioners and policymakers.\u003c/p\u003e\u003cp\u003eWhen the existing literature on a topic is so heterogeneous, mapping the current body of evidence through a scoping review can help clarify the concepts used and identify research gaps. This constitutes a necessary first step before moving on to more focused approaches to evidence synthesis (e.g., meta-analysis) (Khalil et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; M. D. Peters et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; M. D. J. Peters et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The main contribution of this work is therefore to provide a first scoping review exploring the nature and extent of existing scientific research on the impact of virtual worlds on users\u0026rsquo; mental health and to summarize the current evidence to inform future research in this area.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Defining the scope\u003c/h2\u003e\u003cdiv id=\"Sec3\" class=\"Section3\"\u003e\u003ch2\u003e1.1.1 Conceptualizing virtual worlds and mental health\u003c/h2\u003e\u003cp\u003eThroughout the long history of the term, numerous definitions of \u003cem\u003evirtual worlds\u003c/em\u003e have been proposed, both in academic contexts (Bainbridge, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bell, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Girvan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Messinger et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Nevelsteen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schroeder, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and by private and public institutions (e.g., Cook \u0026amp; Kuczer, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; European Commission, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Although there does not appear to be a single commonly shared definition, most definitions do share several core elements. The characteristics of these definitions reflect different research traditions, as well as the context and purpose of the authors in different domains, ranging from academic research to dissemination to the formulation of public policy. For a review work such as this, it is necessary to adopt a definition that captures the essence of how the concept of virtual worlds is understood across these different areas, while at the same time allowing for clear inclusion and exclusion criteria. To this end, we focus on the elements shared by most existing definitions (we do not, however, intend for these to replace the existing definitions, which are appropriate to the different contexts in which they have been proposed).\u003c/p\u003e\u003cp\u003eFirst, a virtual world involves the digital representation of a three-dimensional space, which has at least some degree of persistence (i.e., \u0026ldquo;continues to exist and function after the\u003c/p\u003e\u003cp\u003eparticipant has left\u0026rdquo;, Bell, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; see also Ball, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Messinger et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Nevelsteen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This space can take various forms, from a 2D projection (e.g., Habbo Hotel) to a fully immersive 3D virtual reality (e.g., VRChat). However, there are some definitional discrepancies (e.g., Nevelsteen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schroeder, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) as to whether this spatial representation must be \u003cem\u003eiconic\u003c/em\u003e (cf. Greenberg, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), or whether more \u003cem\u003esymbolic\u003c/em\u003e representations (e.g., textual descriptions of space in a multi-user dungeon; cf. Downey, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) may also fall under this category. Another issue on which there is no full agreement is the extent to which augmented reality (AR) applications (e.g., Pok\u0026eacute;mon Go) can be considered virtual worlds. For some authors, the \u003cem\u003evirtuality\u003c/em\u003e of a virtual world implies that it \u0026ldquo;lacks physical properties beyond the screen\u0026rdquo; (Girvan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, p. 1092), which would exclude those AR applications that integrate physical reality with digital representations. However, various sources accept the blending of material and digital realities as one among several possible configurations within the broader concept of a virtual world (e.g., European Commission, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; see also Nevelsteen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Given that the purpose of this review is to comprehensively map the multiple research efforts in this field, we follow here a broad definition of virtual worlds that includes AR.\u003c/p\u003e\u003cp\u003eA second common element in many definitions of virtual worlds is the social nature of these applications (e.g., Bainbridge, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Girvan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Messinger et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schroeder, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which allow for synchronous social interactions between multiple users, enabled by some form of user representation or avatar within the digital environment (Bainbridge, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bell, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Girvan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This can take different forms, for example, as a computer-generated imagery (CGI) avatar in VR applications, or as a video (e.g., of the user\u0026rsquo;s hands) in pass-through AR applications, among others.\u003c/p\u003e\u003cp\u003eThird, most authors assume \u0026ndash;explicitly or implicitly\u0026ndash; that virtual worlds grant users a certain degree of freedom of action within the environment (e.g., European Commission, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Girvan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Messinger et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Nevelsteen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This contrasts with other digital environments where the user's range of possible actions is very limited (e.g., shooting games).\u003c/p\u003e\u003cp\u003eSynthesizing these three common elements from prior definitions, we state that \u003cem\u003ea virtual world is a digital simulation of a persistent 3D spatial environment (which can integrate physical reality to varying degrees) within which users, through a digital representation of (all or part of) their own body, can interact with other users in diverse ways and perform multiple activities\u003c/em\u003e. This inclusive definition considers applications that have historically been classified as virtual worlds (e.g., Habitat, Habbo Hotel, Second Life), various modern 3D gaming-centered worlds (e.g., World of Warcraft, Roblox, Fortnite), social VR (e.g., VRChat), and AR (e.g., Pok\u0026eacute;mon Go) applications, as well as immersive collaboration tools (e.g., Spatial.io), among other examples.\u003c/p\u003e\u003cp\u003eRegarding mental health, we rely on the broad definition by the World Health Organization (WHO) where it is defined as \"a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community.\" (World Health Organization, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Drawing on an Extended Two-Continua Model of Mental Health (Meier \u0026amp; Reinecke, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we consider both psychopathology and psychological well-being as crucial dimensions. Research often treats these dimensions as separate, yet both comprise unique aspects and indicators of mental health. Psychopathology refers to psychological dysfunction and distress, whereas psychological well-being captures how much an individual thrives psychologically. These two phenomena are distinct yet interrelated: Well-being is not simply the absence of psychopathology, and vice versa. Meier and Reinecke (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) further extended this classic two-continua model by incorporating risk and resilience factors for psychopathology and psychological well-being, respectively, which we also consider in this review.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e1.1.2. Research questions on Virtual Worlds and Mental Health\u003c/h2\u003e\u003cp\u003eConcerns about the potential impact of new media technologies on users\u0026rsquo; mental health have been a recurring theme every time a new medium becomes widely adopted by the population (Orben, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Since the earliest studies on the impact of the internet (e.g., Kraut et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) researchers have shown growing interest in the mental health and well-being effects of digital media, and numerous literature reviews have appeared in recent years focusing on related digital media phenomena that may be relevant for a deeper understanding how using virtual worlds can affect mental health. Arguably, the two most prominent research lines that may potentially inform investigations into virtual worlds are studies on social media (Mansfield et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sala et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Valkenburg et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and video games (Boldi \u0026amp; Rapp, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pine et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additional research intersecting with virtual worlds has examined the impact of AR applications on user well-being (Lee et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or the use of VR in mental health treatment (Emmelkamp \u0026amp; Meyerbr\u0026ouml;ker, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the case of virtual world applications, previous research provides numerous examples of the ways in which using virtual worlds can have either positive or negative effects on users\u0026rsquo; mental health. For example, online multiplayer games or social VR platforms can help users connect with others who share similar interests or preferences, thereby expanding their social capital and even providing social support in situations when it may not be readily available in their physical environment. This can have a clearly positive impact on their psychological well-being (e.g., van Brakel et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Huvila et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Maloney \u0026amp; Freeman, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mandryk et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Socializing and engaging in entertainment on these platforms may also help mitigate feelings of loneliness or symptoms related to anxiety or depression, particularly among vulnerable users (e.g., Gilbert et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The satisfaction of basic psychological needs (e.g., need for autonomy; Partala, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) beyond socialization, or the escape from a stressful physical reality (e.g., Barreda-\u0026Aacute;ngeles \u0026amp; Hartmann, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), may also underlie some of the potential positive impacts of these applications on users\u0026rsquo; psychological well-being.\u003c/p\u003e\u003cp\u003eOn the other hand, the literature has explored numerous potential detrimental effects on users\u0026rsquo; mental health. These range from the development of problematic or addictive use of virtual world applications (e.g., Billieux et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kirby et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), to being harassed, which can lead to highly distressing emotional experiences (Schulenberg et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), to experiencing symptoms of depersonalization following the use of immersive virtual worlds (Barreda-\u0026Aacute;ngeles \u0026amp; Hartmann, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peckmann et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or poorer sleep quality (e.g., Smyth, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), among other possible effects. However, to date, there are no comprehensive literature reviews available on the many potential impacts of virtual worlds on users\u0026rsquo; mental health. While initial reviews on related media (e.g., Lee et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) may capture disjunct parts of the relevant research, they do not focus on the integrated concept of virtual worlds and therefore do not fulfill the broader aim of mapping research in this domain or capturing its complexity and existing research gaps.\u003c/p\u003e\u003cp\u003eAccordingly, this scoping review had several main goals. First, we intended to examine the structural properties of the research field (cf. Arenz et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) \u0026ndash;including publication rate and outlets, disciplines involved, and authors\u0026rsquo; countries, etc.\u0026ndash; which are essential to understand how knowledge production in this area is currently organized (Whitley, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Second, we aimed to synthesize the key concepts, theories, and methods employed in the broad and heterogeneous research on virtual worlds and mental health. Finally, to facilitate future research in this domain, we analyzed the types of evidence available and the main gaps in the current literature.\u003c/p\u003e\u003cp\u003eThus, we addressed the following three research questions (RQs):\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRQ1\u003c/strong\u003e\u003cp\u003eWhat are the structural characteristics of existing research on virtual worlds and mental health (in terms of the distribution of studies across disciplines, countries, and publication outlets, and the temporal evolution of the study of this topic)?\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRQ2\u003c/strong\u003e\u003cp\u003eWhat conceptualizations, variables, and methodological approaches have been used to study the impact of virtual worlds on mental health?\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRQ3\u003c/strong\u003e\u003cp\u003eWhat is the available evidence on the impact of the use of virtual worlds on users' mental health, and what mechanisms explain it?\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Population, Concept and Context\u003c/h2\u003e\u003cp\u003eWe relied on the commonly recommended \u0026ldquo;population, concept, and context\u0026rdquo; (PCC) mnemonic to clearly define the scope of relevant studies for this review (M. D. Peters et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tricco et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The target population included individuals using virtual world applications, regardless of gender, age or geographical background. This primarily referred to users of virtual worlds but also encompassed others who may be directly or indirectly impacted by someone else\u0026rsquo;s use of these environments. Regarding the concept, the review included studies that examined the impact of virtual world use on mental health, even if the term \u0026ldquo;virtual worlds\u0026rdquo; was not explicitly used in a study. This encompassed research focusing on applications that may fall under our working definition of virtual worlds, which in some cases may be referred to as social virtual reality platforms (Barreda-\u0026Aacute;ngeles \u0026amp; Hartmann, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Massively Multiplayer Online Role-Playing Games (MMORPGs; Cole \u0026amp; Griffiths, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), or sandbox video games (Rose, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), among others. To define the scope concerning mental health, we included all dimensions covered in the Extended Two-Continua Model of Mental Health (Meier \u0026amp; Reinecke, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These included indicators of psychopathology (e.g., depressive symptoms, anxiety, eating disorder symptoms, etc.); psychological well-being, both hedonic (e.g., life satisfaction, affect, and discrete emotions) and eudaimonic (e.g., feelings of meaning, authenticity, and mastery); as well as risk factors (e.g., loneliness, stress, poor sleep) and resilience factors (e.g., self-esteem, social support, social capital, good sleep). After consulting an expert (a video gamer and content streamer with several years of experience on the field, who also collaborates with an NGO working on safer online gaming environments), we decided to also include other additional indicators related to problematic use (addictive behaviors, cyberbullying, harassment, and toxicity). In terms of context, the review covered both studies on the everyday use of virtual worlds (e.g., user surveys) and those conducted in experimental settings (e.g., laboratory studies). See the review protocol for further details [LINK REMOVED FOR ANONYMOUS REVIEW].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Inclusion criteria\u003c/h2\u003e\u003cp\u003eThe review was conducted following the previously published protocol [LINK REMOVED FOR ANONYMOUS REVIEW], which was designed by integrating various existing recommendations (Khalil and colleagues; \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); M. D. Peters et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Westphaln et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We decided to include studies:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eexamining the impact of virtual worlds (as defined in Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e1.1.1\u003c/span\u003e) on dimensions and indicators of mental health (as described in section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eproviding primary quantitative or qualitative data from users (or participants in laboratory studies);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003epublished as journal articles, conferences proceedings, or pre-prints; and\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ewritten in English.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Search strategy, paper screening and selection\u003c/h2\u003e\u003cp\u003eWe systematically searched the Web of Science and Scopus databases, and the PubMed search engine. The search strategy was developed in a three-step, iterative approach (Khalil et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It included, first, an initial search using a search string containing main keywords related to virtual worlds and mental health (Step 1); second, reviewing the titles, abstracts, and keywords of the retrieved references to identify additional potentially relevant keywords (Step 2); and, third, analyzing the reference lists of the retrieved studies to identify other potentially relevant studies (Step 3).\u003c/p\u003e\u003cp\u003eFor Step 1, we combined a list of keywords related to virtual worlds with a list of keywords related to mental health. The initial list of keywords for virtual worlds included the term \u0026ldquo;virtual worlds\u0026rdquo; as well as potentially overlapping terms such as \u0026ldquo;metaverse\u0026rdquo;, \u0026ldquo;massively multiplayer online role-playing game\u0026rdquo;, \u0026ldquo;social virtual reality [platform]\u0026rdquo;, \u0026ldquo;sandbox video game\u0026rdquo; and \u0026ldquo;open-world video game\u0026rdquo;. We also included an extensive list of (potentially) related branded applications, such as World of Warcraft, Fortnite and Roblox (see Supplementary Materials for the complete list). Once studies addressing any of these applications were found, we analyzed the application in question to determine whether it indeed met our definition of virtual worlds and revised the search string accordingly.\u003c/p\u003e\u003cp\u003eRegarding the list of keywords related to mental health, we used the terms associated with the dimensions and indicators included in the Extended Two-Continua Model of Mental Health (cf. Meier \u0026amp; Reinecke, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and problematic use, as described in section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe final search string is reported in the Supplementary Material. The initial search yielded a total of 10,393 records, of which 9,354 remained after removing duplicates. Title and abstract screening was conducted by two authors, who used ASReview to apply a \u0026ldquo;human-in-the-loop\u0026rdquo; machine learning approach, which means that human screening decisions trained an active learning model to identify relevant papers in the 9,354 records, prioritize those records for screening, and thereby reduce overall screening load (Van De Schoot et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A composite stopping rule was defined for this process, following recommendations from Boetje and Van De Schoot (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (see Supplementary Material for details). A total of 992 out of the 9,354 records were reviewed by both authors in ASReview before the stopping rule criteria were met, resulting in 307 records selected for full-text screening. Regarding interrater reliability, the kappa value for these two coders, considering all records in the sample and the use of the ASReview tool, was 0.77 (95% confidence interval: 0.73\u0026ndash;0.80), which corresponds to very good accuracy (98%) according to the interpretation proposed by Bakeman (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In cases of disagreement, the two reviewers discussed the application of the inclusion criteria until reaching a consensus.\u003c/p\u003e\u003cp\u003eSubsequently, a complementary search (using the same databases as in the original search) was conducted (Step 2), incorporating additional terms that emerged from the initial screening or variations of those previously used. The search strings used for updating the literature search are reported in Supplementary Material. The newly identified articles were again screened for inclusion based on titles and abstracts, leading to 172 additional records (after removing duplicates). Finally, we conducted forward and backward literature searches based on all so far identified records (Step 3). To do so, first, references citing the articles in our sample of 307 records were identified using the OpenAlex database and screened for title and abstract. Then, references cited in included sample of 307 articles were examined using ResearchRabbit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchrabbit.ai/\u003c/span\u003e\u003cspan address=\"https://www.researchrabbit.ai/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (forward and backward searches) and screened. After removing duplicates, this process produced another 64 new records. In total, we identified 543 records, of which we could retrieve 522 full text articles.\u003c/p\u003e\u003cp\u003eTo conduct a final round of in-depth screening based on the 522 retrieved full texts, we first piloted our coding sheet to fully align individual reviewers\u0026rsquo; understanding of the inclusion and exclusion criteria. Full-text screening of the identified articles was then carried out by all co-authors, with each article being reviewed by two authors. After discussing iteratively the results (e.g. in cases of disagreement) among all co-authors, the final sample consisted of 198 included articles, containing 201 different studies. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the search and screening process following PRISMA guidelines (Page et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data extraction and analysis\u003c/h2\u003e\u003cp\u003eWe iteratively developed and piloted a data extraction sheet, included on our protocol [LINK REMOVED FOR ANONYMOUS REVIEW]. The data extraction project involved, first, to extract information on whether the study was grounded in an explicitly mentioned theoretical framework or conceptual model and the type of conceptual and operational approach adopted. For the latter two, we relied on the classification proposed by Meier and Reinecke (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their review organizes \u003cem\u003econcepts\u003c/em\u003e in digital technology effects research based on whether studies follow a channel-centered or communication-centered approach. In the channel-centered approach, the focus is on technology variables (including devices, types of applications, specific branded applications, or app features). In the communication-centered approach, the focus is on variables related to social interaction via a technology (e.g., type of social tie involved or content of an interaction). Finally, to organize different \u003cem\u003eoperational\u003c/em\u003e approaches, their classification further distinguishes between technology-centered studies, which measure variables directly linked to technology use (e.g., time or frequency of use), and user-centered studies, which focus on variables related to user experience (e.g., motivations, sense of presence, enjoyment).\u003c/p\u003e\u003cp\u003eIn terms of mental health variables, we coded whether the study measured any of the indicators or dimensions included in the Extended Two-Continua Model of Mental Health (see section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e; cf. Meier \u0026amp; Reinecke, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), as well as variables related to problematic use, including addictive behaviors, cyberbullying, harassment, and toxicity. Regarding the methodology, we first coded whether the study design was quantitative, qualitative, or mixed-methods, along with the specific type of design within these categories (e.g., cross-sectional surveys, interviews, etc.); second, whether the study focused on a specific application; third, the characteristics of the sample used (e.g., sample size, geographic location, age group, whether the sample was gender-balanced, and other attributes); and, finally, the type of analysis conducted. At the level of the evidence provided, we coded whether the study employed a design capable of providing causal evidence, and if so for which variables; whether mechanisms of the observed effects were explored (through mediation analysis); and whether effect heterogeneity was analyzed (via moderation analysis). The final list of extracted and analyzed variables can be found in the Supplementary Material.\u003c/p\u003e\u003cp\u003eTwo pilot tests were conducted for coding the articles. In each pilot, the same two coders applied the data extraction form to a random set of ten articles, different in each pilot. The pilots allowed to identify and discuss discrepancies among coders, refine the data extraction process, and fully align the coders\u0026rsquo; criteria. In the second pilot, the two coders achieved a kappa value of 0.69 (95% confidence interval: 0.47\u0026ndash;0.92), corresponding to an uncorrected agreement rate of 88%. To produce a kappa of this magnitude, simulated observers would need to be 92% accurate, which exceeds the recommended target of 85\u0026ndash;90% (Bakeman, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, after the second pilot and additional discussion to resolve disagreement and increase alignment in data coding/extraction, all remaining articles were coded by one of the two coders.\u003c/p\u003e\u003cp\u003eIn the following, we present both a descriptive analysis of the full dataset, based on the variables included in the data extraction form, and a bibliometric analysis using the R package \u003cem\u003ebibliometrix\u003c/em\u003e (Aria \u0026amp; Cuccurullo, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The descriptive analysis was based on all 198 articles, including 201 empirical studies, while the bibliometric analysis was conducted on the 194 articles available in a format compatible with \u003cem\u003ebibliometrix\u003c/em\u003e (Scopus format).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Structural characteristics of the field (RQ1)\u003c/h2\u003e\u003cp\u003eOur RQ1 focused on the structural characteristics of the research, particularly on the temporal evolution of literature and the distribution of studies across disciplines, countries, and publication outlets. The bibliometric analysis shows that annual scientific production has experienced a steady increase, particularly from 2010 onwards. Notable peaks were observed in 2011, 2014, 2020 and 2023, with the highest number of articles published in 2023 (25 papers). Average citations per year suggest that, not surprisingly, earlier publications, particularly those from 2005 to 2007, have received slightly more attention over time (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe sample included 111 unique publication outlets (academic journals and conference proceedings). To analyze the distribution of studies across disciplines, we used the subject areas assigned to each publication outlet in the Scimago Journal \u0026amp; Country Rank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scimagojr.com/\u003c/span\u003e\u003cspan address=\"https://www.scimagojr.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (two of the outlets were not listed in this database). A total of 14 disciplines were represented, and approximately half (58 out of 111, i.e., 52.3%) of the outlets were associated with more than one discipline. The most common subject area among the articles was Computer Science (45 records, 40.5% of the total unique publication outlets), followed by Medicine (37, 33.3%), Social Sciences (36, 32.4%), Psychology (27, 24.3%), and Business, Management and Accounting (12, 10.8%). Core sources in the field include \u003cem\u003eComputers in Human Behavior\u003c/em\u003e (33 documents), \u003cem\u003eCyberpsychology, Behavior and Social Networking\u003c/em\u003e (9), \u003cem\u003eProceedings of the ACM on Human-Computer Interaction\u003c/em\u003e (7), and \u003cem\u003eFrontiers in Psychology\u003c/em\u003e (5). These venues combine psychological and technological perspectives, which is consistent with the highly interdisciplinary nature of the field.\u003c/p\u003e\u003cp\u003eThe United States leads in overall publication volume with 72 articles, including 17 involving international collaboration as lead author (MCP leading), but with no recorded participation as a non-leading collaborator. This reflects a strong but self-driven research presence. The United Kingdom follows with 24 articles, showing six MCPs as lead and 1 as collaborator. Countries like Switzerland stand out for their collaborative role: although leading only one article nationally, it appears as lead in 12 international publications (11 of them with European countries) and as co-author in two more, confirming its central position in European collaboration networks. Germany, Belgium, Canada and Norway also appear frequently as non-leading contributors (four or more co-authored articles each), indicating their strong integration in multilateral research. These patterns suggest that while countries like the United States and China dominate in volume, European countries show greater interconnectedness in the virtual worlds and well-being research landscape.\u003c/p\u003e\u003cp\u003eThe analysis of authors\u0026rsquo; keywords (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) shows the dominant concepts of the field. The most frequent terms include \u0026ldquo;online games\u0026rdquo; (32 occurrences), \u0026ldquo;MMORPG\u0026rdquo; (29), \u0026ldquo;virtual worlds\u0026rdquo; (29), \u0026ldquo;gaming disorder\u0026rdquo; (27) and \u0026ldquo;addiction\u0026rdquo; (23). Other relevant concepts include \u0026ldquo;social VR\u0026rdquo; (22), \u0026ldquo;Pokemon Go\u0026rdquo; (19), \u0026ldquo;world of warcraft\u0026rdquo; (17) and \u0026ldquo;metaverse\u0026rdquo; (16), suggesting growing diversification in platforms and modalities of interaction under study, ranging from traditional gaming environments to newer immersive VR and location-based AR technologies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe keyword co-occurrence network based on authors\u0026rsquo; keywords (generated using the Walktrap algorithm; Pons et al., 2005) reveals five well-defined thematic clusters (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Two large central clusters are closely interconnected: the blue cluster (right-center) appears to reflect early research on virtual environments (\u0026ldquo;online games,\u0026rdquo; \u0026ldquo;virtual worlds,\u0026rdquo; \u0026ldquo;Second Life\u0026rdquo;) with a focus on their social and psychological impact (\u0026ldquo;social capital,\u0026rdquo; \u0026ldquo;online community,\u0026rdquo; \u0026ldquo;psychological needs\u0026rdquo;); the orange cluster (upper-center) represents the abundant research activity on problematic use (\u0026ldquo;gaming disorder,\u0026rdquo; \u0026ldquo;problematic internet use,\u0026rdquo; \u0026ldquo;stress\u0026rdquo;) related to gaming platforms (\u0026ldquo;MMORPGs\u0026rdquo;), particularly studies focusing on World of Warcraft. A smaller and relatively isolated red cluster (lower-center) captures a distinct research stream focused on the positive psychosocial and health effects (\u0026ldquo;well-being,\u0026rdquo; \u0026ldquo;mental health,\u0026rdquo; \u0026ldquo;life satisfaction\u0026rdquo;) of AR and physical movement embedded in game mechanics (\u0026ldquo;Pok\u0026eacute;mon Go,\u0026rdquo; \u0026ldquo;augmented reality,\u0026rdquo; \u0026ldquo;location-based games\u0026rdquo;). Interestingly, the presence of \u0026ldquo;COVID-19\u0026rdquo; in this cluster may reflect studies that examined AR games as tools for maintaining well-being, social interaction, or physical activity during lockdowns or periods of restricted mobility. The small green cluster (left-center) focuses on immersive experiences mediated through VR and metaverse platforms, with frequent connections to terms such as \u0026ldquo;loneliness\u0026rdquo;, \u0026ldquo;social support\u0026rdquo;, and \u0026ldquo;social presence.\u0026rdquo; The emphasis on these constructs suggests that this research stream explores how immersive technologies may offer avenues for enhancing connectedness or compensating for social deficits, particularly in vulnerable populations. Finally, an even smaller violet cluster (bottom-left), partially overlapping with the green one, reflects emerging literature on the risks and challenges (\u0026ldquo;harassment\u0026rdquo;) of embodied interactions (\u0026ldquo;presence,\u0026rdquo; \u0026ldquo;avatars\u0026rdquo;) in social VR spaces. For additional thematic analysis, see the Supplementary Material.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurthermore, we analyzed how research interests have evolved over time within the specific corpus of 194 publications included in our study on virtual worlds and mental health. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the temporal distribution of the most frequent author keywords across the period 2005\u0026ndash;2024, capturing both the emergence of new concepts and the continuity of foundational themes. Several terms show early relevance in the literature, notably \u0026ldquo;online community\u0026rdquo;, \u0026ldquo;problematic internet use\u0026rdquo;, and \u0026ldquo;second life\u0026rdquo;, which appear from 2008, 2010, and 2011, respectively, to 2014. These themes reflect an initial scholarly focus on the social dynamics within early virtual platforms. However, their term frequency remains relatively low, suggesting niche attention or a subsequent decline in interest.\u003c/p\u003e\u003cp\u003eBetween 2012 and 2018, keywords such as \u0026ldquo;virtual worlds\u0026rdquo;, \u0026ldquo;MMORPG\u0026rdquo;, \u0026ldquo;addiction\u0026rdquo;, \u0026ldquo;social capital\u0026rdquo; and \u0026ldquo;online games\u0026rdquo; dominate the landscape, with terms like \u0026ldquo;online games\u0026rdquo; (32 occurrences) and \u0026ldquo;MMORPG\u0026rdquo; (29) sustaining visibility over an extended period. This era likely represents the consolidation of research on the psychological and behavioral impacts of massively multiplayer online environments, including concerns around gaming disorder (27) and addiction (23).\u003c/p\u003e\u003cp\u003eHowever, a noticeable thematic shift emerges around 2019, with the rise of health-related terms such as \u0026ldquo;well-being\u0026rdquo;, \u0026ldquo;mental health\u0026rdquo;, \u0026ldquo;depression\u0026rdquo;, \u0026ldquo;social support\u0026rdquo; and \u0026ldquo;covid-19\u0026rdquo;. These terms indicate a growing interest in the potential therapeutic or harmful effects of digital environments on psychological outcomes, possibly accelerated by the pandemic\u0026rsquo;s impact on digital socialization and health. In recent years, technologically updated concepts such as the \u0026ldquo;metaverse\u0026rdquo; and \u0026ldquo;social VR\u0026rdquo; gained prominence, particularly since 2021. These terms are among the most recent yet also the most frequent, indicating a rapidly growing subfield focused on immersive, next-generation virtual environments. Notably, \u0026ldquo;metaverse\u0026rdquo; appears with high frequency in a short span, suggesting its centrality in current discourse and potential as a future anchor for scholarly debates about virtual worlds and mental health.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Conceptualizations, variables, and methodological approaches (RQ2)\u003c/h2\u003e\u003cp\u003eOur RQ2 inquired about the conceptualizations, variables used, and methodological approaches adopted in studies on virtual worlds and mental health. The results presented in this section are based on our manual descriptive annotation of the 198 articles.\u003c/p\u003e\u003cp\u003eRegarding the conceptualization of the studies, the majority (70.6%) of the analyzed research adopts a quantitative approach. Among these, approximately three-quarters (72.5%) rely on cross-sectional surveys, while experimental, quasi-experimental, and longitudinal designs are scarce. One-fifth (21.4%) of all reviewed studies are qualitative in nature, and a small proportion (8%) uses a mixed-methods approach combining quantitative and qualitative methods (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for more details). Almost half of the qualitative studies (44.2%) are based on interviews; one fifth employs more than one qualitative method (20.9%), and smaller proportions use content analysis (11.6%) or ethnography/participant observation (7%).\u003c/p\u003e\u003cp\u003eAround half of the studies do not explicitly rely on any specific theoretical framework or conceptual model. Among those that do (48.3% of the total), the most frequently used theories are self-determination theory (Deci \u0026amp; Ryan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and theories of social capital (e.g., the theoretical approach by Putnam, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In terms of levels of analysis and following the classification proposed by Meier and Reinecke (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), a clear majority of studies (60.7%) are channel-centered, and among these, the focus tends to be on specific branded applications. The most common are, in descending order, World of Warcraft (24.4% of all studies focus on this application), Second Life (15.4%), and Pok\u0026eacute;mon Go (13.4%). Communication-centered studies represent around 27% of all reviewed studies. They focus on variables such as the specific activities that the participants do in a virtual world platform, although no particular variable clearly dominates. About 22% of the studies, in turn, combine channel-centered and communication-centered approaches. At the operational level, we find a similar percentage of studies that adopt technology-based operationalizations (e.g., measuring variables such as time spent on virtual worlds) and user-centered operationalizations (e.g., user motivations), while approximately one-third of studies (35.3%) combine both measurement approaches.\u003c/p\u003e\u003cp\u003eWhen looking at the variables used to measure user mental health, we find a relatively balanced distribution across different dimensions. The most frequent approach uses indicators of hedonic well-being, with around 40% of studies analyzing aspects such as quality of life or emotions, followed by studies examining problematic use (e.g., addiction) (36.8%), and resilience factors (35.3%) such as social capital.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDescriptive statistics of the included studies (overall and by methodological approach)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal sample\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;201)\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuantitative\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMixed (quantitative \u003cem\u003eand\u003c/em\u003e qualitative)\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStudy design\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101 (72.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed (any) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (20.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterviews\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuasi-experimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContent analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnography/participant observation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTheoretical framework and/or conceptual model\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97 (48.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-determination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial capital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeed satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMotivations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConceptualization\u003c/em\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel-centered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (60.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112 (78.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; device\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; type of application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (12.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; branded application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (61.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (62.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; features\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunication-centered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (33.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (37.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel- \u003cem\u003eand\u003c/em\u003e communication-centered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (18.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMost studied platforms\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorld of Warcraft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (43.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond Life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePok\u0026eacute;mon Go\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMORPGs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial VR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOperationalization\u003c/em\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnology-centered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (56.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (70.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (68.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUser-centered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 (63.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (68.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnology- \u003cem\u003eand\u003c/em\u003e user-centered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (43.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePsychopathology\u003c/em\u003e \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (33.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (12.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial anxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHedonic well-being\u003c/em\u003e \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffect/emotions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (37.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (12.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuality of life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWell-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEudaimonic well-being\u003c/em\u003e \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (18.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthenticity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeaningfulness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMastery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-determination needs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRisk factors\u003c/em\u003e \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (31.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoneliness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor sleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (31.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eResilience factors\u003c/em\u003e \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (46.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (37.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-esteem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGood sleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (31.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial capital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProblematic factors\u003c/em\u003e \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (62.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAddiction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (30.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (43.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHarassment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (18.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eToxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (12.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample size \u003cem\u003emedian (range: min. \u0026ndash; max.)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e218.5 (1\u0026ndash;1,520,025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e312 (6\u0026ndash;1,520,025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (1\u0026ndash;402)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e129.5 (10\u0026ndash;438)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge \u003cem\u003emedian (range: min. \u0026ndash; max.)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.7 (9\u0026ndash;67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.8 (9\u0026ndash;67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.7 (12\u0026ndash;58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.8 (9.8\u0026ndash;56)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation known\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104 (51.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; North America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (48.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (29.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Australia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; South America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Mixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBalanced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales (60% or more)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (49.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (56.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales (60% or more)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (18.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (12.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModeration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e. \u003csup\u003ea\u003c/sup\u003e: the total does not correspond to \u003cem\u003eN\u003c/em\u003e as the rest of the studies adopted distinct study designs. \u003csup\u003eb\u003c/sup\u003e: regardless of approaches used (only qualitative or quantitative, or both). \u003csup\u003ec\u003c/sup\u003e: coded only for studies using measures to analyze a specific level (i.e., information not coded for most qualitative studies). \u003csup\u003ed\u003c/sup\u003e: the total \u003cem\u003en\u003c/em\u003e of subdomains is not equal to the total \u003cem\u003en\u003c/em\u003e of mental health domains as a study could have explored different subdomains.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe median sample size is 312 for quantitative studies and 19 for qualitative ones. Among the studies that report participants\u0026rsquo; age the median is 26.7. Only 17 studies (8.5% of the sample) focus specifically on children and teenagers, and only five studies (2.5%) address older adults. Many studies recruit participants online, and for around half of the analyzed studies, the participants\u0026rsquo; geographic origin is unknown. Among those that do report this information, participants from the United States clearly dominate (48.5%), followed by European participants (26.2%), while other regions are underrepresented.\u003c/p\u003e\u003cp\u003eAround 30% of the studies use participant samples with balanced gender representation (no more than 60% of participants identifying as one gender). However, in more than half of the studies (51.2%), over 60% of the sample consists of men, indicating a predominance of male participants in this research field. The representation of minorities is relatively low: three studies (1.4% of the sample) focus on racial or ethnic minorities; two studies (0.9%) on LGBTQ\u0026thinsp;+\u0026thinsp;users; and ten studies (4.7%) focus on participants with physical or mental disabilities or neurodivergent individuals. Some of the studies analyzed (31 studies; 15.4% of the sample) focus on a specific area of application, in particular, mental healthcare (21; 10.4%) and, to lesser extent, education (4, 2%).\u003c/p\u003e\u003cp\u003eIn line with the proportion of quantitative and qualitative studies in our sample, the most common type of analysis is some form of inferential statistics (141 studies, 70.1% of the sample), followed by content or thematic analysis (56, 27.9%) and ethnographic analysis or narrative description (5, 2.5%). Nine studies (4.5% of the sample) do not conduct any form of in-depth analysis (e.g., they only reported descriptive statistics).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Evidence of effects and mechanisms (RQ3)\u003c/h2\u003e\u003cp\u003eOur RQ3 addressed the existing evidence regarding the impact of virtual worlds on users\u0026rsquo; mental health and the mechanisms that may explain these effects. To examine this question, we first examined the availability of causal evidence in the studies analyzed. Only 35 out of 201 studies (i.e., 17.4%) employed methods that can provide causal evidence or approximate it, such as experimental, quasi-experimental, or longitudinal designs. Of these, roughly half investigate the use of virtual worlds as a tool in a therapeutic or psychological support context (e.g., for psychological therapy, support groups, etc.) or for training and educational purposes (e.g., in a behavioral skills training program), yet mental health effects of virtual worlds are rarely addressed in their primary context of use \u0026ndash;namely, entertainment or socialization.\u003c/p\u003e\u003cp\u003eAmong the studies using experimental or longitudinal methods that do examine the impact of virtual worlds in this primary context, the examined applications and mental health outcomes are highly heterogeneous, making it difficult to synthesize evidence across studies (see Supplementary Material for additional details). Furthermore, although a full risk-of-bias assessment was beyond the scope of our review, a preliminary examination suggests that many of these studies have significant limitations, such as failing to control for potential confounders or relying on suboptimal methodological and analytical choices, which may undermine the quality of the evidence provided.\u003c/p\u003e\u003cp\u003eFrom the studies in this group that do appear to provide initial causal evidence, the diversity of platforms and outcomes assessed prevents drawing firm conclusions regarding the direction (positive or negative) of the effects. For example, Gilbert et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) suggest that virtual worlds may help improve affective states in users with disabilities. However, the study by Zhang and colleagues (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) shows that using avatars with disability signifiers in VRChat may expose users to various forms of embodied, explicit harassment. Therefore, considering the different applications and outcomes targeted, it is difficult to assess whether, overall, the use of these applications may pose more risks or benefits for this population at this point in the field.\u003c/p\u003e\u003cp\u003eWith respect to the mechanisms underlying the observed effects, it is noteworthy that among studies employing quantitative methods, only 19.7% of them (and just 3 studies, i.e., 8.5% of those using experimental or longitudinal designs) conducted mediation analyses, which has been a commonly used approach to examine causal mechanisms (Hayes, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; although this approach should be treated with caution, see Rohrer et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yet, the available evidence on the mechanisms underlying a potential link between the use of virtual worlds and mental health is particularly limited.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith digital technology becoming more immersive and widespread, users increasingly frequent virtual worlds, raising questions about potentially beneficial and detrimental effects on mental health. To map existing knowledge, we identified and reviewed 198 articles addressing many different angles of the topic. Our work presents the first integrative scoping review of research examining virtual worlds in conjunction with user mental health.\u003c/p\u003e\u003cp\u003eOur bibliometric analysis reveals that research on virtual worlds and mental health is a field in active expansion, in terms of number of publications, thematic scope, and technological focus. Findings suggest rapidly increasing academic interest, likely catalyzed by developments in immersive technologies, societal interest in the metaverse (cf. Dolata \u0026amp; Schwabe, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jacobides et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and broader societal shifts, among which the COVID-19 pandemic seems to have been central. While early publications laid the conceptual foundations around online communities and platforms like Second Life, the field has progressively moved toward more specialized concerns, such as gaming disorder and the psychosocial impacts of MMORPGs.\u003c/p\u003e\u003cp\u003eWe identify five clusters of keywords that represent interconnected but distinguishable research trajectories. While all clusters engage with the relationship between virtual environments and mental health, they do so from diverse conceptual angles: from community building and addiction to physical well-being and online safety. Notably, the clustering of problematic gaming with MMORPGs (rather than casual mobile or augmented reality games) versus positive well-being themes studied in an augmented reality context may suggest contrasting assumptions about the affordances and risks of different technological modalities, or how these are used. Overall, the field shows a progressive expansion from early sociological perspectives on online communities to more nuanced and diverse explorations including augmented reality (with keywords like \u0026ldquo;Pok\u0026eacute;mon Go\u0026rdquo;, \u0026ldquo;augmented reality\u0026rdquo;), self-perception (\u0026ldquo;self-esteem\u0026rdquo;, \u0026ldquo;social anxiety\u0026rdquo;, \u0026ldquo;avatar\u0026rdquo;), and emotional well-being. This evolution illustrates how research on virtual worlds is increasingly addressing broader societal concerns from mental health to identity and digital inclusion.\u003c/p\u003e\u003cp\u003eTaken together, these findings suggest that research on virtual worlds and mental health is evolving toward greater complexity, increasingly embracing interdisciplinary dialogues at the intersection of psychology, human-computer interaction, and public health. Further research could explore to what extent these thematic separations reflect underlying disciplinary divides (e.g., clinical psychology vs. media studies) or methodological biases (e.g., qualitative vs. quantitative designs), and whether they are shifting with the emergence of new platforms.\u003c/p\u003e\u003cp\u003eAmong the studies analyzed, a predominant approach focuses primarily on the technologies themselves (channel-centered approach), and less on how these technologies are used for social interaction (communication-centered approach), which aligns with the general trend observed in research on digital media and mental health (Meier \u0026amp; Reinecke, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This may indicate a predominantly techno-deterministic perspective (i.e., the tendency to expect a direct and uniform impact of technology on users) which is also present in much previous research on social media and mental health, as noted by Orben (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, most of the analyzed studies focus on specific, branded applications (e.g., World of Warcraft, VRChat) rather than on their design characteristics, affordances, or content. This type of approach often conflates the specific causes responsible for technology effects and stands in the way of robust comparisons across technologies and platforms (Orben, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A stronger focus on the specific technological features and the subjective affordances they enable as well as key social interaction and content characteristics of virtual worlds could produce comparable and cumulative evidence regardless of which applications happen to be trendy at any given moment (Meier et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne striking finding of our review is that, despite the tendency in the literature to focus on branded applications, there is a marked lack of studies targeting some of the most widely used platforms today, such as Roblox, Fortnite, or Minecraft. In particular, these three platforms are used by tens of millions of users, many of them, children and adolescents (Clement, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kumar, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). The lack of attention to these platforms is especially notable given media reports highlighting potential risks associated with them (e.g., in terms of exposure to inadequate content, addiction, etc.; e.g., Brooks \u0026amp; Otte, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While we do not advocate for single-platform research, it appears urgent to include these more recent platforms in comparative studies of virtual worlds, ideally, as noted above, through the analysis of their technological features, affordances, and contents.\u003c/p\u003e\u003cp\u003eAnother notable aspect is the predominance of quantitative studies based on cross-sectional surveys, which offer limited capacity to provide causal tests of the effects of virtual worlds on users. There is a noticeable lack of experimental, quasi-experimental, or longitudinal studies. This trend is consistent with what has been observed in other areas of digital media and mental health research (cf. Mansfield et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Combined with the high heterogeneity of studies in this field \u0026ndash;in terms of both how technology use is conceptualized and the mental health outcomes assessed (reflecting the multiple disciplines involved in this research)\u0026ndash;, this suggests that attempts to synthesize evidence through meta-analysis may still be premature. Relatedly, the relatively high number of qualitative studies may reflect the fact that this heterogeneous research community is still in the process of defining the key variables of interest, as a necessary step prior to measuring them quantitatively. This highlights the value of joint, interdisciplinary efforts to define key constructs and the ways in which they can be measured, for example through standardization initiatives (Meier et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which could be inspired by standardization processes in the technology sector. In this context, collaboration with stakeholders who can offer perspectives beyond academic research may be a key success factor (Mansfield et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe predominant focus of available studies on Western, young adult samples also underscores the need to pay greater attention to some specific and underrepresented populations of users. Greater inclusion of (or specific focus on) children and adolescents, women, users from disadvantaged backgrounds, and other populations (e.g., ethnic and racial minorities, LGBTQIA\u0026thinsp;+\u0026thinsp;users, users with disabilities) particularly vulnerable in online environments (e.g., Schulenberg et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) will be crucial for informing the design and regulation of virtual worlds to ensure they are inclusive and beneficial for all types of users.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Limitations\u003c/h2\u003e\u003cp\u003eAs with any literature synthesis, one limitation of this scoping review is that it may have overlooked relevant work. However, our broad inclusion of search terms, the addition of new keywords after the initial search, and our thorough forward and backward searches helped minimize this risk, though we cannot rule out overlooking very recent work (especially studies published after February 2025, when our forward and backward searches were conducted). Furthermore, given the rapidly evolving nature of this field, it is possible that new relevant studies emerged during the production of this paper or shortly after its completion. Therefore, it is important to keep in mind the temporal scope of this review when interpreting the results.\u003c/p\u003e\u003cp\u003eAdditionally, our search employed commonly used general academic databases (Web of Science, Scopus, PubMed), so it is possible that some records appearing only in more specialized databases were not identified in our search. In addition, articles in languages other than English were excluded.\u003c/p\u003e\u003cp\u003eOur findings also depend on the definition of virtual worlds that we adopted. Our focus has been mainly on consumer applications. Some other possible instances of virtual worlds (e.g., digital twins of cities or factories; Grieves, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whose potential effects \u0026ndash;if any\u0026ndash; on users\u0026rsquo; mental health are likely to be much more indirect, may have been left out of our review. On the other hand, we excluded some studies due to definitional ambiguity (e.g., studies evaluating video games without specifying which ones or their key characteristics), but we believe there is no substantive reason to expect that these would systematically diverge (conceptually or methodologically) from the studies included in our review. As such, their exclusion is unlikely to compromise the validity or generalizability of our findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur literature review shows that the growing role of virtual worlds in users' lives is indeed reflected in the expanding number and increasing complexity of scientific studies examining their impact on mental health. This impact has been addressed from numerous disciplines, perspectives, and viewpoints. While such richness of approaches is likely necessary to capture the complexity of these effects, it also complicates the comparison between studies and the accumulation of transferable evidence across them.\u003c/p\u003e\u003cp\u003eOur results suggest that research streams on topics related to virtual worlds and mental health seem to emerge (and fade) in parallel with the popularity of specific platforms (e.g., Second Life, World of Warcraft, Pok\u0026eacute;mon Go). As a result, the existing literature appears to cluster around platforms and outcome types, with no integrated conceptualization of virtual worlds shared across different strands of research. Our work represents an important first step in this direction. In future studies, approaches centered on the technological characteristics and user affordances of various platforms may be key to generating valid and transferable evidence across contexts.\u003c/p\u003e\u003cp\u003eDespite the diversity of theoretical and methodological perspectives used in the literature, a technocentric view currently prevails; a tendency also common in other areas of research on digital media and mental health. This view often downplays the role of specific patterns of use, individual characteristics, contexts, and other non-technological factors that may shape the impact of virtual worlds. Future research will need to pay closer attention to these dimensions, as well as to the experiences of minority groups and vulnerable users, in order to fully capture the complexity of potential effects and produce evidence that reflects users\u0026rsquo; diversity.\u003c/p\u003e\u003cp\u003eWhile virtual worlds are used by more and more people around the globe, our findings highlight the current lack of causal evidence regarding their potential mental health impacts. They also underscore the urgent need for multidisciplinary approaches and greater coordination among researchers to navigate the complexity of this issue. Within the context of the increasing public debate on the impact of digital media on mental health (Di Iorio et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Stokel-Walker, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we hope that our mapping of existing research can contribute to develop future studies more effectively, and facilitate the urgent scientific scrutiny of these platforms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthical statement\u003c/h2\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eM.B-Á. and I.H. carried out the initial conceptualization. All authors contributed to the development of the methodology and the formal analysis. M.B-Á., S.A., and I.H. were responsible for data curation. M.B-Á. and S.A. wrote the original draft. I.H. prepared the figures. All authors reviewed and edited the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis work received funding from the Centre for Advanced Studies of the Joint Research Centre (JRC) of the European Commission, thought the project Virtual Worlds and Society (VirtueS)\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe dataset containing the bibliographic references of the articles included in the review is publicly available at [BLINDED FOR ANONYMOUS REVIEW].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmeida F (2017) Concept and dimensions of web 4.0. Int J Comput Technol 16(7) https://doi.org/10.24297/ijct.v16i7.6446 \u003c/li\u003e\n\u003cli\u003eApple (2023). 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In: CHI Play\u0026rsquo;20 Proceedings of the Annual Symposium on Computer-Human Interaction in Play, Virtual event Canada, 2-4 November 2020. https://doi.org/10.1145/3410404.3414266 \u003c/li\u003e\n\u003cli\u003eMandryk RL, Frommel J, Armstrong A, Johnson D (2020) How passion for playing World of Warcraft predicts in-game social capital, loneliness, and wellbeing. Front Psychol 11:2165 https://doi.org/10.3389/fpsyg.2020.02165 \u003c/li\u003e\n\u003cli\u003eMansfield KL, Ghai S, Hakman T, Ballou N, Vuorre M, Przybylski AK (2025) From social media to artificial intelligence: Improving research on digital harms in youth. Lancet Child Adolesc Health 9(3):194-204. https://www.thelancet.com/journals/lanchi/article/PIIS2352-4642(24)00332-8/abstract\u003c/li\u003e\n\u003cli\u003eMeier A, Ellison N, Reinecke L, Valkenburg PM (2024). 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Int J Hum Comput Stud 69(12):787\u0026ndash;800. https://doi.org/10.1016/j.ijhcs.2011.07.004 \u003c/li\u003e\n\u003cli\u003ePeckmann C, Kannen K, Pensel MC, Lux S, Philipsen A, Braun N (2022) Virtual reality induces symptoms of depersonalization and derealization: A longitudinal randomised control trial.\u003cem\u003e \u003c/em\u003eComput Hum Behav 131:107233. https://doi.org/10.1016/j.chb.2022.107233 \u003c/li\u003e\n\u003cli\u003ePeters MDJ, Marnie C, Colquhoun H, Garritty CM, Hempel S, Horsley T, Langlois EV, Lillie E, O\u0026rsquo;Brien KK, Tun\u0026ccedil;alp Ӧ, Wilson MG, Zarin W, Tricco AC (2021) Scoping reviews: Reinforcing and advancing the methodology and application. Syst Rev 10(1):263. https://doi.org/10.1186/s13643-021-01821-3 \u003c/li\u003e\n\u003cli\u003ePeters MD, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, McInerney P, Godfrey CM, Khalil H (2020) Updated methodological guidance for the conduct of scoping reviews. 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Comput Hum Behav Rep 14:100404. https://doi.org/10.1016/j.chbr.2024.100404 \u003c/li\u003e\n\u003cli\u003eSchroeder R (2008) Defining virtual worlds and virtual environments. J Virtual Worlds Res 1(1). https://jvwr-ojs-utexas.tdl.org/jvwr/article/view/294\u003c/li\u003e\n\u003cli\u003eSchulenberg K, Freeman G, Li L, Barwulor C (2023) Creepy towards my avatar body, creepy towards my body\u0026quot;: How women experience and manage harassment risks in social virtual reality. Proc ACM Hum Comput Interact 7(CSCW2):236 https://doi.org/10.1145/3610027 \u003c/li\u003e\n\u003cli\u003eSchulenberg K, Freeman G, Li L, Knijnenburg BP (2024) Does who you are or appear to be matter?: Understanding identity-based harassment in social VR through the lens of (mis)perceived identity revelation. 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Who knows. \u003cem\u003eN Sci, 266\u003c/em\u003e(3545):18. https://doi.org/10.1016/S0262-4079(25)00864 \u003c/li\u003e\n\u003cli\u003eTricco AC, Lillie E, Zarin W, O\u0026rsquo;Brien, KK, Colquhoun H, Levac D, Moher D, Peters MDJ, Horsley T, Weeks L, Hempel S, Akl EA, Chang C, McGowan J, Stewart L, Hartling L, Aldcroft A, Wilson MG, Garritty C, \u0026hellip; Straus SE (2018) PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann Intern Med 169(7):467\u0026ndash;473. https://doi.org/10.7326/M18-0850 \u003c/li\u003e\n\u003cli\u003eValkenburg PM, Meier A, Beyens I (2022) Social media use and its impact on adolescent mental health: An umbrella review of the evidence. Curr Opin Psychol 44:58\u0026ndash;68. https://doi.org/10.1016/j.copsyc.2021.08.017 \u003c/li\u003e\n\u003cli\u003eVan Brakel V, Barreda-\u0026Aacute;ngeles M, Hartmann T (2023) Feelings of presence and perceived social support in social virtual reality platforms. 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Oxford University Press, New York. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization (2005) Promoting mental health: Concepts, emerging evidence, practice. Available at: https://iris.who.int/bitstream/handle/10665/43286/9241562943_eng.pdf?sequence=1 Accessed 7 Sep 2025\u003c/li\u003e\n\u003cli\u003eZhang K, Deldari E, Yao Y, Zhao Y (2023) A diary study in social virtual reality: Impact of avatars with disability signifiers on the social experiences of people with disabilities. In: Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility, Association for Computing Machinery, New York 23-25 October 2023. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"virtual worlds, mental health, well-being, metaverse, Web 4.0, video games","lastPublishedDoi":"10.21203/rs.3.rs-7561416/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7561416/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe constant increase in the use of virtual worlds in recent decades has raised concerns about their potential effects on users\u0026rsquo; mental health. However, the great heterogeneity of the scientific literature on this topic makes it difficult to identify existing evidence, knowledge gaps, and the most relevant future research directions. To address this issue, the present scoping review maps the current research landscape by analyzing 198 selected articles. The results reveal a growing interest in this topic across various disciplines, as well as the multiple assumptions and approaches adopted by different research communities. Existing research predominantly focuses on specific branded applications such as Second Life or World of Warcraft. However, there is very little research on some of the virtual world platforms that currently have the largest user bases (e.g., Roblox, Fortnite). Importantly, little attention has been paid to the shared features across virtual world platforms (e.g., technological affordances), which could help build more generalizable theories. The availability of causal evidence on the impact of virtual world use on users\u0026rsquo; mental health is very limited, as is the diversity of participant samples used in studies. Addressing these issues will be key to advancing research in this field and to understanding the multiple ways in which virtual world applications may affect users\u0026rsquo; psychological well-being, particularly regarding vulnerable users like children and adolescents.\u003c/p\u003e","manuscriptTitle":"Virtual worlds and mental health: A scoping review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 11:47:20","doi":"10.21203/rs.3.rs-7561416/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-28T11:29:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T02:54:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74147267246533717027729588607459372662","date":"2025-10-11T05:46:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-10T15:47:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T05:41:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T04:51:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-08T07:41:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fdad4d6a-09b3-4a07-a7da-55611185c6bb","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55425106,"name":"Biological sciences/Psychology"},{"id":55425107,"name":"Social science/Psychology"},{"id":55425108,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-04-14T09:26:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 11:47:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7561416","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7561416","identity":"rs-7561416","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}