Universal Digital Mental Health Interventions for Children and Youth 

Universal Digital Mental Health Interventions for Children and Youth | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Universal Digital Mental Health Interventions for Children and Youth Dr. Kaitlin Di Pierdomenico, Oana Bucsea, Arianna Leguia, Haleh Hashemi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6473660/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This scoping review examines universal digital mental health interventions (DMHIs) for children and youth. A total of 52 studies were identified, primarily conducted in high-income countries, using a hybrid format, involving adult facilitators, and assessing multiple psychological outcomes in children aged 5–18. Emotional outcomes were the most commonly studied. Findings highlight important directions for the future by highlighting gaps in the literature such that those in greatest need of accessible mental health and that would most likely benefit have been severely understudied (i.e., traditionally disadvantaged populations and early childhood). Biological sciences/Psychology Health sciences/Health care Figures Figure 1 Introduction Prioritizing children and youth in mental health care is crucial. Two-thirds to three-quarters of mental health conditions develop before the age of 24 [ 1 ], with a median age of onset at 18 years and a peak at 14.5 years [ 2 ]. Early-onset conditions often persist into adulthood, emphasizing the importance of early intervention and preventative care to equip young people with effective coping strategies and reduce the risk of long-term mental health challenges [ 3 ]. The global decline in youth mental health post-pandemic further highlights the urgent need for targeted support [ 3 ]. Mental health care delivery is undergoing significant transformation with the growing integration of digital mental health interventions (DMHIs), which utilize technologies such as online meeting programs, mobile applications, web-based tools, video games, and virtual reality. While this shift has been driven by increased internet accessibility and the expanding range of digital technologies [ https://mentalhealthcommission.ca/wp-content/uploads/2024/09/An-E-Mental-Health-Strategy-for-Canada-FINAL.pdf ], it is critical to acknowledge the catalyst that the COVID-19 pandemic provided. The pandemic resulted in unprecedented increases in psychological challenges particularly in children [ 5 , 6 ] and exposed the limitations of traditional mental health care systems, both of which accelerated the adoption of DMHIs [ 7 ]. These digital interventions have emerged as scalable, accessible solutions [ 8 , 9 ] to longstanding challenges such as insufficient capacity, long wait times, and geographic disparities in mental health care. DMHIs bridge gaps in care by reaching remote and marginalized populations with higher rates of unmet mental health needs [10, https://emhicglobal.com/equity-and-access/hope-away-from-home-advancing-digital-mental-health-interventions-for-refugees-and-marginalized-groups/ , https://health-infobase.canada.ca/mental-health/inequalities/report.html# ]. In addition to being cost-effective via internet-delivery [ 13 ], DMHIs expand choice by offering families more power in who, when, and how they receive service. This moves away from traditional service models solely dependent on visiting a professional in a brick and mortar building, which may require travelling great distances at great cost. This shift aligns with person-centered care, which prioritizes individual preferences, self-empowerment, and holistic well-being [ https://www.grand viewresearch.com/industry-analysis/mental-health-apps-market-report]. As youth become increasingly immersed in technology earlier and earlier, DMHIs align with their behaviours, preferences, and digital fluency [ https://www.digitalcenter.org/wp-content/uploads/2013/02/ 2003_digital_future_report-year3.pdf, https://mentalhealthcommission.ca/wp-content/uploads /2024/09/An-E-Mental-Health-Strategy-for-Canada-FINAL.pdf]. The proliferation of DMHIs, including more than 20,000 apps available as of 2021 [ https://www.apa.org/monitor/2021/01/trends-mental-health-apps ], presents unparalleled opportunities to expand access to care. When effectively integrated into healthcare systems, DMHIs have been shown to be as effective as in-person services, offering promising alternatives or complements to traditional care [ https://mentalhealthcommission.ca /wp-content/uploads/2021/05/MHCC_E-Mental_Health-Briefing_Document_ENG_0.pdf] while continually improving in usability and functionality. However, significant gaps remain between research and practice, with many evidence-based tools inaccessible to sectors of the public [ 18 , 19 , 20 ]. Addressing this evidence-to-practice gap is essential to maximize the effectiveness of DMHIs in real-world contexts. DMHIs targeting children and youth can serve diverse functions, from promotion and prevention to interventions for more severe symptomology. These interventions vary in design, intensity, target populations, and target outcomes, as well as the incorporation of adults, such as parents and teachers. Technology can be integrated either as a format (e.g., online therapy session or modality to train facilitators), a tool to assist trained facilitators or self-management (e.g., an electronic mood tracker application for youth), or a stand-alone therapeutic agent (e.g., chatbots, AI-powered therapists). This scoping review focuses specifically on Tier 1 or universal DMHIs for children and youth aged 0–18. Tier 1 interventions are low-intensity but high-reach interventions designed for non-clinical, low-risk populations and aimed at promoting mental health and/or preventing clinical challenges [ 21 ]. These interventions focus on reinforcing positive social, emotional, cognitive, and behavioural skills to support overall well-being. This scoping review aims to map study characteristics, intervention designs, target outcomes, and implementation factors to identify key features and research gaps. This review is registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols ( INPLASY Protocol 6765 ). This scoping review sets out to define the current evidence base of universal DMHIs in light of the recent surge in literature and to guide future synthesis via a meta-analysis. The scoping was anchored around four topic areas and research questions: Target Populations : What samples have been studied? Target Objectives and Outcomes : What do the interventions focus on and how are they being measured? Intervention Parameters : How were the interventions implemented? Intervention Delivery : Who facilitated the interventions? Results I. Description of Included Studies A total of 21,729 studies were identified in the search process. Ultimately, 52 peer-reviewed research studies on DMHIs were included in the scoping review. See Fig. 1 and Methods below for more detail regarding selection process. The majority of studies were conducted in upper-middle-income countries (n = 50, 96%), primarily in North America (n = 13, 25%) and Australia (n = 12, 23%). See Supplementary Table 1 for a summary of Study Characteristics of Universal DMHIs For Children and Youth. Only two studies were conducted in a low-income country [22, 23]. All identified studies were published since 2002, with the highest number published in 2024 (n = 10, 19%). The majority were published between 2021 and 2024 (n = 27, 52%). Study sample sizes ranged from 24 to 1,767 participants. Most studies employed between-group designs while a notable minority used within group (i.e., repeated measures) designs to track changes within participants over time (n = 13, 25%). Randomized controlled trials (RCTs) were the most common study design (n = 35, 67%), with the rest of the studies utilizing non-randomized interventional studies (n = 17, 33%). Many studies also included follow-up assessments after the initial intervention period to examine long-term effects (n = 23, 44%), but the number and duration of follow-ups varied notably. Follow-up time periods included: Immediate (within 1 month post-intervention, n = 2), 1–3 months post-intervention (n = 14), 4–6 months post-intervention (n = 7), and 12-months or beyond (n = 5). II. Results according to key domains and research questions. 1. Target Populations: What samples have been studied? The features of target populations are summarized in Table 1. Across studies, the reported mean or median age of participants ranged from 0 to 18 years. The majority of studies focused on children and youth aged 5 to 18 years (n = 47, 90%). A smaller subset of studies (n = 4, 8%) targeted infants and preschool-aged children (0 to 4 years), where the interventions were typically delivered to parents or caregivers to administer to the child. Only one study (n = 1, 2%) focused on children aged 4 to 7 years, which did not fall neatly into either the 0–4 or 5–18 age categories [25]. To assess diversity within study samples, participants were categorized based on the proportion of racialized individuals reported from a local perspective (i.e., populations considered racialized within the country where the study was conducted). The majority of studies did not report racial demographic data (n = 33, 63%). Among those that did, most reported a sample with 0–24% racialized participants (n = 10, 19%), followed by studies with 75–100% racialized participants (n = 6, 12%) and those with 25–49% racialized participants (n = 3, 6%). Of the 19 studies (37%) that reported race and cultural background, only 14 identified the most prevalent racialized population. The most frequently reported racialized populations were Latin American (n = 7, 50%), Indigenous (n = 3, 22%), Black (n = 2, 14%), and East Asian (n = 2, 14%). Representation from historically marginalized communities was also assessed based on whether studies explicitly targeted these populations (e.g., interventions designed for individuals with low socioeconomic status or specific racial or cultural backgrounds). The majority of studies did not explicitly focus on populations that traditionally have faced marginalization (n = 46, 88%), while a small proportion (n = 6, 12%) specifically stated that their sample included participants experiencing systemic barriers. Among these, on the areas of marginalization studied were racialized and ethnically diverse youth (n = 3), low-income populations (n = 3), caregivers in low-resource settings (n = 2), and Indigenous communities (n = 1). Several studies addressed overlapping forms of marginalization, such as racial and economic disadvantage. Examples include interventions for Latinx sexual minority youth [26], Black and biracial adolescent girls from low-income urban school districts [27], and low-income Latino and African American adolescents [28]. Additional studies addressed economically disadvantaged adolescents [29], caregivers of young children in Zambia and Tanzania [23], and Inuit youth in remote Nunavut communities [30]. Table 1 Key features of Target Populations. Population features No. of studies (%) Age 5–18 47 (90) 0–4 4 (8) Mixed (4–7) 1 (2) Proportion of Racialized Participants No racial data provided 33 (63) 0–24% 10 (19) 75–100% 6 (12) 25–49% 3 (6) Most Prevalent Racialized Group Latin American 7 (50) Indigenous 3 (22) Black 2 (14) East Asian 2 (14) Lived Experience with Marginalization Historically Marginalized Communities 46 (88) Non-Marginalized Communities 6 (12) 2. Target Objectives and Outcomes: What do the interventions focus on and how are they being measured? Table 2 summarizes the target objectives and outcomes of the included studies. Promotion interventions had the aim to increase skills that strengthen mental health (e.g., increase help-seeking behaviours or improving social skills). Prevention interventions had the aim of teaching skills that decrease the occurrence of symptomology associated with mental health diagnoses (e.g., teaching thought challenging to reduce worry). Interventions that addressed both promotion and prevention often aimed to strengthen a psychosocial skill while reducing a maladaptive behaviour – such as increasing skills to make a friend while teaching distortion identification to prevent fear of negative evaluation [31]. The objective of the interventions were primarily designed to either promote mental health (n = 19, 36.5%), prevent psychopathology (n = 19, 36.5%), or address both objectives (n = 14, 27%). Interventions targeted a range of psychological outcomes, including emotional (e.g., measuring how participants feel), behavioural (e.g., measuring how participants act), social (e.g., measuring how participants connect with others), and cognitive (e.g., measuring how participants think or solve problems). Most studies assessed multiple psychological outcomes (n = 30, 57%), followed by studies that focused solely on emotional outcomes (n = 14, 27%). Only one study exclusively examined cognitive outcomes [22]. All studies used validated measures to assess intervention outcomes, with only two studies also employing study-specific unvalidated measures [28, 32]. Self-reported outcomes (n = 36, 69%) were the most used, compared to caregiver- or teacher-reported measures (n = 9, 17%), or a combination of both (n = 7, 14%). Table 2 Key features of Target Outcomes. Outcome features No. of studies (%) Objective Promotion 19 (36.5) Prevention 19 (36.5) Both (Promotion & Prevention) 14 (27) Outcomes Multiple 30 (57) Emotional 14 (27) Behavioural 4 (8) Social 3 (6) Cognitive 1 (2) Reporting Self-report 36 (69) Caregiver-report or teacher-report 9 (17) Combination of self- and caregiver-/school- reports 7 (14) 3. Intervention Parameters: How were the interventions implemented? The characteristics of intervention parameters, including format, design, and structure, are summarized in Table 3. The majority of studies utilized hybrid formats (n = 28, 54%), where a digital intervention was supplemented by an in-person component (e.g., students accessing an online program with teacher facilitation at school). This was followed by fully virtual interventions (n = 20, 38%) and a small number of studies that included both hybrid and virtual intervention arms (n = 4, 8%). Among DMHIs with a hybrid format, the in-person component most frequently took place in school settings (n = 28, 88%). Studies varied in how intervention were implemented. Most interventions were online programs (n = 24, 46%), delivered through platforms or portals that typically included multiple modules or lessons (e.g., psychoeducational programs). This was followed by apps (n = 9, 17%), standalone software applications designed for mobile devices or computers (e.g., meditation apps). Some interventions used virtual communication tools (n = 6, 11.5%), such as video conferencing or chat platforms for real-time interaction, while others combined virtual communication with websites (n = 6, 11.5%) to provide additional resources, modules, or educational content. Fewer interventions relied solely on websites (n = 2, 4%) or incorporated video game elements (n = 4, 8%). Only one study implemented virtual reality as the primary intervention format [33]. Intervention structure also varied across studies. The most common format was independent work (n = 24, 46%), where participants engaged in the intervention on their own, though some studies included asynchronous facilitator support (e.g., periodic check-ins or feedback on independent work). This was followed by group-based and independent work (n = 15, 29%), where participants completed self-guided activities alongside scheduled group sessions, utilizing an online program. Some interventions adopted a multi-modal approach (n = 6, 11%), integrating group-based, independent work, and one-to-one sessions, allowing participants to engage in self-directed activities, group discussions, and personalized support from a facilitator. A smaller number of studies used independent work combined with one-to-one support (n = 5, 10%), where participants completed self-guided tasks but also received individualized facilitator guidance. Strictly group-based interventions were the least common (n = 2, 4%), involving structured discussions, activities, or therapeutic exercises conducted in a group setting. Table 3 Key features of Intervention Parameters. Parameter features No. of studies (%) Format Hybrid 28 (54) Virtual 20 (38) Mixed (Hybrid + Virtual Arms) 4 (8) Design Online Program 24 (46) App 9 (17) Virtual Communication 6 (11.5) Virtual Communication + Website 6 (11.5) Video Game 4 (8) Website 2 (4) Virtual Reality 1 (2) Structure Independent Work 24 (46) Group-Based & Independent Work 15 (29) Group-Based & Independent Work & One-to-One 6 (11) Independent Work & One-to-One 5 (10) Group-Based 2 (4) 4. Intervention Delivery : Who facilitated the interventions? Table 4 summarizes the key features of who facilitated the intervention. Interventions were categorized as self-led, facilitator-led, or both-led. Self-led interventions allowed participants to independently access on-demand content, such as mobile apps or video games. Facilitator-led interventions involved real-time guidance from a trained mental health professional, such as in virtual therapy sessions or structured group programs. Both-led interventions included an initial facilitator-guided phase before transitioning to independent engagement. The majority of interventions were both-led (n = 35, 67%), while fewer were fully self-led (n = 13, 25%) or solely facilitator-led (n = 4, 8%). Of the 52 studies included in this review, 38 (73%) involved a facilitator in some fashion. Research personnel were the most frequently involved facilitators (n = 12, 31%), consisting of researchers or academic staff without clinical qualifications. Registered mental health professionals (n = 7, 18%), including licensed psychologists, psychiatrists, and clinical social workers, were also commonly involved. Teachers (n = 6, 15%) played a significant role in several studies, often integrating mental health interventions into their school curriculum. Some studies used multidisciplinary teams (n = 5, 13%), where licensed mental health professionals worked alongside child and youth workers or counselors. Other studies relied on non-registered mental health professionals (n = 4, 10%), such as peer supporters, wellness coaches, or unlicensed counselors. A small number of studies (n = 2, 5%) did not specify who facilitated the intervention, while another small number of studies (n = 2, 5%) were exclusively facilitated by child and youth workers and counselors. In addition, one study involved a mix of teachers and non-registered mental health professionals [33]. The level of facilitator involvement varied across studies. In most cases, facilitators were engaged in teaching or counseling (n = 26, 67%). This included providing direct instruction, delivering therapeutic guidance, or offering structured support through both group and one-on-one sessions. Their roles often involved leading psychoeducational sessions, conducting mental health counseling, and facilitating discussions and interactive activities. In contrast, some facilitators served as non-involved resources (n = 13, 33%). In these cases, facilitators did not actively guide the intervention but instead provided supplementary materials or background support. Their involvement included being available to answer questions, overseeing access to digital resources, or offering general guidance without directly interacting with participants. Table 4 Key features of Intervention Delivery. Delivery features No. of studies (%) Delivery Both-led 35 (67) Self-led 13 (25) Facilitator-led 4 (8) Facilitator Type Research Personnel 12 (31) Registered Mental Health Professional 7 (18) Teachers 6 (15) Registered Mental Health Professionals & Child & Youth Workers & Counselors 5 (13) Non-Registered Mental Health Professional 4 (10) Not Described 2 (5) Child & Youth Workers & Counselors 2 (5) Mixed 1 (3) Facilitator Involvement Teaching/Counseling 26 (67) Non-Involved Resource 13 (33) Discussion This scoping review describes the current state of the evidence base on universal (Tier 1) digital mental health interventions (DMHIs) for children and youth aged 0 to 18 years. While prior reviews have explored DMHIs for young people aged 10–24 years [ 34 ] and broader samples that include universal, at-risk populations, and clinically-diagnosed populations [ 18 ], most existing scoping reviews have focused primarily on adults [ 35 ] or children and youth within at-risk or clinical populations [ 36 ]. This review is unique in its exclusive focus on universal DMHIs for children and youth, identifying key trends in target populations, outcomes, intervention design, and delivery methods. By mapping these elements, the review provides valuable insights into the current state of DMHIs and highlights critical gaps in the literature. These findings contribute to a deeper understanding of how DMHIs support mental health promotion and the prevention of psychopathology among young populations. The rapid surge in DMHI research following the pandemic necessitates careful consideration of emerging trends and gaps in current approaches. Most studies (90%) focused on participants aged 5 to 18 years, with limited attention to early childhood populations (0 to 4 years). This skewed age representation highlights a critical gap in DMHIs for very young children, a key developmental period for building foundational psychological skills. Digital interventions targeting early childhood are necessarily caregiver-mediated, aiming to improve caregiver responsiveness and parenting skills to support early psychological development in children [37, 38, 23, 39]. Early brain development is a window of extraordinary opportunity but also profound vulnerability [ https://developingchild.harvard.edu /resources/working-paper/wp3/]. Without intervention, toxic stress can reshape brain architecture, increasing the risk of lifelong mental health challenges. Virtual therapy is not just an alternative, it is a lifeline for parents who would otherwise lack access. DMHIs break barriers, delivering scalable, equitable solutions to protect early development. These parents often have the greatest difficulty travelling to in-person appointments. DMHIs break barriers, delivering scalable, equitable solutions to protect early development. The glaring absence of evidence-based early childhood interventions in DMHIs cannot be overlooked. Despite increasing attention to digital health equity, historically racialized and marginalized communities also remain underrepresented in DMHI research. Only 12% of studies included participants from marginalized populations, raising concerns about the generalizability and accessibility of DMHIs for diverse populations. Once again, this gap is in a troubling area – these are the young populations at greatest risk of developing mental health challenges in the future. To address these challenges and bridge the digital equity divide, future research must prioritize efforts to co-develop DMHIs with communities that are marginalized to develop also interventions that are accessible, effective, and culturally responsive. Additionally, researchers should work toward improving racial demographic reporting in DMHI studies to ensure that racialized and underserved populations are adequately represented. As noted in our results, the majority of studies (n = 33, 63%) did not report racial demographic data, making it difficult to assess whether DMHIs effectively serve racialized and underrepresented populations. Without detailed demographic reporting relating to racialization, it remains unclear who benefits most from these interventions. Most interventions were hybrid, meaning they combined a digital component with in-person support, such as facilitator guidance or structured activities in schools or community settings. The frequent use of hybrid interventions reflects a growing trend toward blended approaches that integrate digital tools with in-person engagement to improve effectiveness and user experience. However, the optimal level of in-person involvement in digital interventions remains unclear, as few studies put a hybrid versus virtual intervention head to head. Future research should examine whether hybrid models provide greater benefits than fully virtual formats and identify which aspects of in-person support contribute most to intervention success. Additionally, while most studies assessed multiple psychological outcomes, the majority focused on emotional outcomes, with fewer addressing behavioural, social, or cognitive outcomes. This reflects a gap in the development of interventions that target broader psychosocial and cognitive skills, which are essential for comprehensive youth mental health support. As digital interactions increasingly replace in-person experiences, the need to investigate and support social skill development is more pressing than ever. Research shows that pandemic-related isolation hindered social cognitive growth in early childhood, particularly for children from lower socioeconomic backgrounds [41]. Likewise, rising digital engagement reduces opportunities for youth to develop essential social competencies like empathy, communication, and conflict resolution [ https://effectiveschoolsolutions.com/teenage-social-skills/ ]. As digital adolescence becomes the norm, future research must prioritize integrating social skill-building into DMHIs to ensure that youth are equipped with the interpersonal skills necessary for well-being and resilience in an increasingly digital world. Despite the expansion of DMHIs, immersive digital technologies such as video games (8%) and virtual reality (2%) were rarely utilized as standalone interventions. This underutilization represents a missed opportunity to improve engagement, particularly among younger populations. Future research must move beyond simply replicating traditional therapeutic approaches in digital formats and instead harness the interactive and immersive potential of these technologies. By creatively integrating game-based and virtual experiences into DMHIs, interventions can better align with the natural interests of children and youth, ultimately improving user experience and intervention effectiveness. Conclusion This scoping review highlights key trends and gaps in DMHIs for children and youth. While hybrid interventions and online psychoeducational programs dominate the landscape, significant gaps remain in early childhood interventions, digital health equity, facilitator involvement, and the use of immersive digital tools. Addressing these gaps is essential to ensuring that DMHIs are inclusive, effective, and accessible to all young people. Recommendations for Future Research: Expand research on DMHIs for early childhood (0–4 years) to better understand how digital tools can support early psychological development. Improve digital health equity by increasing representation of historically marginalized populations, improving reporting on racial and cultural backgrounds, and identifying barriers to access and engagement. Expand research on DMHIs that target a broader range of psychological outcomes beyond emotional outcomes, particularly giving the evolving nature of how children may be learning social skills with large components of their life online. Increase the development of immersive digital tools that build on what children and youth enjoy, such as virtual reality and gamification, to assess feasibility and potential for engagement Examine the impact of hybrid versus fully virtual interventions on mental health outcomes. Limitations While this review offers a comprehensive overview of the existing literature, several limitations should be noted. The inclusion of only peer-reviewed studies means that unpublished research and grey literature were not considered, which may have led to the omission of relevant interventions. Additionally, the predefined eligibility criteria may have excluded studies with valuable insights that did not explicitly report on these criteria. Despite these limitations, this review provides a broad mapping of how DMHIs are currently being implemented in universal child and youth populations. Addressing gaps in early childhood intervention and digital health equity is crucial to ensuring that DMHIs effectively meet the diverse mental health needs of young people and can be scaled for widespread implementation. Methods We followed the PRISMA Extension for Scoping Reviews [ 43 ] (Supplementary Fig. 1). Eligibility Criteria Types of Sources. This review included full-text, peer-reviewed original research articles, as these represent the highest standard for evidence-based practice in health and social care. Studies published in non-peer-reviewed sources, such as book chapters, case studies, conference abstracts, and dissertations, were excluded. Participants. Studies were eligible if the minimum or mean age of participants was between 0 and 18 years. Studies including some participants over 18 were still considered if the average age of the sample was approximately 18 years or younger. Studies that exclusively focused on individuals older than 18 or did not specify participant age were excluded. Mental Health Status. Only studies involving non-clinical populations were included. Studies were excluded if they focused on participants who had been hospitalized for acute medical or psychiatric conditions (e.g., suicide attempt or cancer treatment) or had recently been discharged from such care. Intervention Format. Studies had to include a digital intervention. Eligible interventions were either virtual (delivered entirely online, without in-person components) or hybrid (integrating digital tools with in-person elements, such as school-based implementation). Studies that relied solely on in-person interventions without a digital component were excluded. Measured Outcomes. Studies were included if they measured at least one mental health outcome within the domains of emotional, behavioural, social, or cognitive well-being. Studies that assessed only physiological measures without a corresponding mental health outcome were excluded. For each study, only one measure per outcome was selected, prioritizing the most psychometrically valid assessment. Search Strategy A systematic search for DMHIs targeting psychological outcomes was created with a highly experienced academic librarian. The search process began with a preliminary scan of Google Scholar to identify key articles relevant to the three main components of the research question: universal scope, DMHIs, and children and youth. These initial articles informed the development of a comprehensive list of keywords, which were refined to ensure compatibility across databases. The final search strategy was implemented in three major databases: MEDLINE, PsycInfo, and Embase. An example of the search terms used across these databases is provided in Supplementary Table 2. The initial search was conducted between June 26, 2024, and July 5, 2024. To capture newly published literature, an updated search using the same strategy was performed on January 6, 2025, covering studies published between July 3, 2024, and January 6, 2025. Evidence Selection All search results were imported into Covidence, a reference management and screening tool, to facilitate de-duplication, title and abstract screening, full-text review, and data extraction. While many records were automatically categorized as database sources based on indexed journal information, a large proportion (n = 10,532) were labeled as “unspecified sources” due to missing metadata. This included manually uploaded references and records lacking identifiable source information. Title and abstract screening was conducted by a four-person review team, with all records independently screened by two reviewers. Reliability screening was performed on a subset of studies (25% of the total sample) to assess consistency in screening decisions. Full-text articles deemed potentially relevant were uploaded to Covidence for further screening by two independent reviewers. Discrepancies in inclusion or exclusion decisions were resolved through weekly consensus meetings among the authorship team. Full-text data extraction was conducted using both Covidence and Excel, and always performed by two independent reviewers. Additionally, a manual search of reference lists from excluded but flagged systematic reviews was conducted, along with targeted handsearching of key journals and sources likely to contain relevant studies. Inter-rater agreement for title and abstract screening ranged from 0.84 to 0.98, while agreement for full-text review ranged from 0.90 to 0.96, indicating an almost perfect agreement between reviewers [ 44 ]. Data Charting Process Data extraction was conducted using Microsoft Excel, with categories initially adapted from the PICO framework: Population, Intervention, Comparators, and Outcomes [ 45 ]. Variables selected for extraction (as reported above) was an iterative process as familiarity with the scope of literature deepened. Data extraction templates were piloted and revised to improve clarity and consistency between co-authors. Each article was independently extracted by two reviewers to improve accuracy and reliability. Data Synthesis Data synthesis was performed using Microsoft Excel. Inter-rater agreement was calculated during title and abstract screening and full-text review. Descriptive statistics (frequencies, medians, and ranges) were used to summarize study characteristics. Data were presented graphically and in tabular format where appropriate. We generated descriptive summaries of study characteristics, including the frequency and distribution of publication year, country of origin, and study design. Additionally, we summarized participant characteristics (e.g., age distribution, lived experience with marginalization), DMHI characteristics (e.g., intervention format, setting, and facilitation type), and mental health outcome measures (e.g., domains assessed, types of scales used, and reporting methods). Declarations Reporting Summary Further information on research design is available in the Nature Research Reporting Summary linked to this article. Data Availability This study did not analyze or generate any new datasets. Aggregated data used in this review can be obtained from the corresponding author upon request. Since this scoping review relies solely on peer-reviewed articles, ethical approval was not required. Code Availability No code was generated during the current study. Acknowledgements We would like to thank the Jackman Foundation and York University (funding dedicated to DIVERT Mental Health collaboration, a CIHR funded Health Research Training Platform) for funding that supported this research project. Author Contributions KDP co-conceptualized the study, led the literature search and analysis, and drafted the initial manuscript. RPR conceptualized the study, assisted with literature analysis, and co-drafted the initial drafts of the manuscript. OB, HH, and AL assisted with literature analysis and provided editorial comments on submitted manuscript. A Lovegrove was actively involved in developing the study’s conceptual foundation. RC provided editorial comments on submitted manuscript, as the statistical consultant on the associated meta-analysis. Competing Interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 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Hope away from home: Advancing digital mental health interventions for refugees and marginalized groups. eMHIC Global . (2023). Public Health Agency of Canada. Inequalities in mental health, well-being and wellness in Canada: social determinants and changes over time . (2024). Christensen, H. & Griffiths, K. M. The prevention of depression using the Internet . Med. J. Aust . 177 , S122 (2002). Hancock, A. Mental health apps market size, share & trends analysis report by 2030. Vantage Market Res . (2023). UCLA Center for Communication Policy. The UCLA internet report: surveying the digital future year 3 . (2003). Clay, R. A. Mental health apps are gaining traction. Am. Psychol. Assoc . (2021). Mental Health Commission of Canada (MHCC). E-mental health in Canada: transforming the mental health system using technology . (2014). Bergin, A. D. et al. Preventive digital mental health interventions for children and young people: a review of the design and reporting of research. NPJ Digit. Med . 3 , 133 (2020). Liu, M. & Schueller, S. M. Moving evidence-based mental health interventions into practice: implementation of digital mental health interventions. Curr. Treat. Options Psychiatry 10 , 333–345 (2023). Grist, R., Porter, J. & Stallard, P. Mental health mobile apps for preadolescents and adolescents: a systematic review. J. Med. Internet Res . 19 , e176 (2017). Hoover, S. et al. Advancing comprehensive school mental health: Guidance from the field . https://www.schoolmentalhealth.org/AdvancingCSMHS (2019). Hassen, H. M. et al. Effectiveness and implementation outcome measures of mental health curriculum intervention using social media to improve the mental health literacy of adolescents. J. Multidiscip. Healthc . 15 , 979–997 (2022). Skeen, S. et al. Using WhatsApp support groups to promote responsive caregiving, caregiver mental health and child development in the COVID-19 era: a randomised controlled trial of a fully digital parenting intervention. Digit. Health 9 , 20552076231203893 (2023). Sterne, J. A. C. et al. RoB 2: a revised tool for assessing risk of bias in randomized trials. BMJ 366 , l4898 (2019). https://doi.org/10.1136/bmj.l4898. Liu, S. et al. Online mindfulness-based group intervention for young Chinese children: effectiveness of the OKmind program in attention and emotion regulation. Mindfulness 15 , 1–12 (2024). Estrada, Y. et al. Familias con Orgullo: Pilot study of a family intervention for Latinx sexual minority youth to prevent drug use, sexual risk behavior, and depressive symptoms. Prev. Sci. 25 , 1079–1090 (2024). Neal-Barnett, A. et al. Evaluation of the effectiveness of a musical cognitive restructuring app for Black inner-city girls: survey, usage, and focus group evaluation. JMIR Mhealth Uhealth 7 , e11310 (2019). Lang, J. M., Waterman, J. & Baker, B. L. Computeen: A randomized trial of a preventive computer and psychosocial skills curriculum for at-risk adolescents. J. Prim. Prev . 30 , 587–603 (2009). Gefter, L. et al. Assessing health behaviour change and comparing remote, hybrid and in-person implementation of a school-based health promotion and coaching program for adolescents from low-income communities. Health Educ. Res . 39 , 291–312 (2024). Bohr, Y. et al. Evaluating the utility of a psychoeducational serious game (SPARX) in protecting Inuit youth from depression: Pilot randomized controlled trial. JMIR Serious Games 11 , e38493 (2023). DeSmet, A. et al. The efficacy of the Friendly Attac serious digital game to promote prosocial bystander behavior in cyberbullying among young adolescents: a cluster-randomized controlled trial. Comput. Hum. Behav . 78 , 336–347 (2018). Chillemi, K., Abbott, J. A. M., Austin, D. W. & Knowles, A. A pilot study of an online psychoeducational program on cyberbullying that aims to increase confidence and help-seeking behaviors among adolescents. Cyberpsychol. Behav. Soc. Netw . 23 , 253–256 (2020). Hadley, W. et al. Moving beyond role-play: Evaluating the use of virtual reality to teach emotion regulation for the prevention of adolescent risk behavior within a randomized pilot trial. J. Pediatr. Psychol . 44 , 425–435 (2019). Nelson, J. R., Martella, R. M. & Marchand-Martella, N. Maximizing student learning: the effects of a comprehensive school-based program for preventing problem behaviors. J . Emot. Behav. Disord . 10 , 136–148 (2002). Baka, E. et al. Scoping review of digital interventions for the promotion of mental health and prevention of mental health conditions for young people. Oxf. Open Digit. Health 3 , oqaf005 (2025). Lipschitz, J. M. et al. Digital mental health interventions for depression: scoping review of user engagement. J. Med. Internet Res . 24 , e39204 (2022). Danese, A. et al. Scoping review: digital mental health interventions for children and adolescents affected by war. J. Am. Acad. Child Adolesc. Psychiatry (2024) McRury, J. M. & Zolotor, A. J. A randomized, controlled trial of a behavioral intervention to reduce crying among infants. J. Am. Board Fam. Med . 23 , 315–322 (2010). Morawska, A. & Sanders, M. R. Self-administered behavioural family intervention for parents of toddlers: Effectiveness and dissemination. Behav. Res. Ther . 44 , 1839–1848 (2006). Zhang, X. et al. Effectiveness of digital guided self-help mindfulness training during pregnancy on maternal psychological distress and infant neuropsychological development: randomized controlled trial . J. Med. Internet Res . 25 , e41298 (2023). National Scientific Council on the Developing Child. Excessive stress disrupts the architecture of the developing brain: working Paper 3 . Updated Edition. (2014). Scott, R. M., Nguyentran, G. & Sullivan, J. Z. The COVID-19 pandemic and social cognitive outcomes in early childhood. Sci. Rep . 14 , 28939 (2024). Effective School Solutions. The decline of teenage social skills . (2024). Tricco, A. C. et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann. Intern. Med . 169 , 467–473 (2018). McHugh, M. L. Interrater reliability: the kappa statistic. Biochem. Med . 22 , 276–282 (2012). Joanna Briggs Institute (JBI). JBI Reviewer’s Manual: methodology for JBI scoping reviews . The Joanna Briggs Institute (2014). Supplementary Figure 1 Supplementary Figure 1 is not available with this version. Additional Declarations No competing interests reported. Supplementary Files SupplementaryDocumentsPRISMAReporingSummary.pdf SupplementalOnlineContenttables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6473660","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":448906935,"identity":"f2d8cd7f-a8e3-4c7c-be27-af2a356d2ac1","order_by":0,"name":"Dr. Kaitlin Di Pierdomenico","email":"","orcid":"","institution":"York University, Canada","correspondingAuthor":false,"prefix":"Dr.","firstName":"Kaitlin","middleName":"Di","lastName":"Pierdomenico","suffix":""},{"id":448906936,"identity":"7f874f8b-d821-428d-91b5-7988d32a7b1f","order_by":1,"name":"Oana Bucsea","email":"","orcid":"","institution":"York University, 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16:47:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":64587,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalOnlineContenttables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6473660/v1/9642f6f61d29c88d12f8d776.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Universal Digital Mental Health Interventions for Children and Youth ","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrioritizing children and youth in mental health care is crucial. Two-thirds to three-quarters of mental health conditions develop before the age of 24 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with a median age of onset at 18 years and a peak at 14.5 years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Early-onset conditions often persist into adulthood, emphasizing the importance of early intervention and preventative care to equip young people with effective coping strategies and reduce the risk of long-term mental health challenges [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The global decline in youth mental health post-pandemic further highlights the urgent need for targeted support [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMental health care delivery is undergoing significant transformation with the growing integration of digital mental health interventions (DMHIs), which utilize technologies such as online meeting programs, mobile applications, web-based tools, video games, and virtual reality. While this shift has been driven by increased internet accessibility and the expanding range of digital technologies [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mentalhealthcommission.ca/wp-content/uploads/2024/09/An-E-Mental-Health-Strategy-for-Canada-FINAL.pdf\u003c/span\u003e\u003cspan address=\"https://mentalhealthcommission.ca/wp-content/uploads/2024/09/An-E-Mental-Health-Strategy-for-Canada-FINAL.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e], it is critical to acknowledge the catalyst that the COVID-19 pandemic provided. The pandemic resulted in unprecedented increases in psychological challenges particularly in children [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and exposed the limitations of traditional mental health care systems, both of which accelerated the adoption of DMHIs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These digital interventions have emerged as scalable, accessible solutions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] to longstanding challenges such as insufficient capacity, long wait times, and geographic disparities in mental health care.\u003c/p\u003e \u003cp\u003eDMHIs bridge gaps in care by reaching remote and marginalized populations with higher rates of unmet mental health needs [10, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://emhicglobal.com/equity-and-access/hope-away-from-home-advancing-digital-mental-health-interventions-for-refugees-and-marginalized-groups/\u003c/span\u003e\u003cspan address=\"https://emhicglobal.com/equity-and-access/hope-away-from-home-advancing-digital-mental-health-interventions-for-refugees-and-marginalized-groups/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://health-infobase.canada.ca/mental-health/inequalities/report.html#\u003c/span\u003e\u003cspan address=\"https://health-infobase.canada.ca/mental-health/inequalities/report.html#\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. In addition to being cost-effective via internet-delivery [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], DMHIs expand choice by offering families more power in who, when, and how they receive service. This moves away from traditional service models solely dependent on visiting a professional in a brick and mortar building, which may require travelling great distances at great cost. This shift aligns with person-centered care, which prioritizes individual preferences, self-empowerment, and holistic well-being [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.grand\u003c/span\u003e\u003cspan address=\"https://www.grand\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eviewresearch.com/industry-analysis/mental-health-apps-market-report]. As youth become increasingly immersed in technology earlier and earlier, DMHIs align with their behaviours, preferences, and digital fluency [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.digitalcenter.org/wp-content/uploads/2013/02/\u003c/span\u003e\u003cspan address=\"https://www.digitalcenter.org/wp-content/uploads/2013/02/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e2003_digital_future_report-year3.pdf, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mentalhealthcommission.ca/wp-content/uploads\u003c/span\u003e\u003cspan address=\"https://mentalhealthcommission.ca/wp-content/uploads\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e/2024/09/An-E-Mental-Health-Strategy-for-Canada-FINAL.pdf].\u003c/p\u003e \u003cp\u003eThe proliferation of DMHIs, including more than 20,000 apps available as of 2021 [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.apa.org/monitor/2021/01/trends-mental-health-apps\u003c/span\u003e\u003cspan address=\"https://www.apa.org/monitor/2021/01/trends-mental-health-apps\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e], presents unparalleled opportunities to expand access to care. When effectively integrated into healthcare systems, DMHIs have been shown to be as effective as in-person services, offering promising alternatives or complements to traditional care [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mentalhealthcommission.ca\u003c/span\u003e\u003cspan address=\"https://mentalhealthcommission.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e/wp-content/uploads/2021/05/MHCC_E-Mental_Health-Briefing_Document_ENG_0.pdf] while continually improving in usability and functionality. However, significant gaps remain between research and practice, with many evidence-based tools inaccessible to sectors of the public [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Addressing this evidence-to-practice gap is essential to maximize the effectiveness of DMHIs in real-world contexts.\u003c/p\u003e \u003cp\u003eDMHIs targeting children and youth can serve diverse functions, from promotion and prevention to interventions for more severe symptomology. These interventions vary in design, intensity, target populations, and target outcomes, as well as the incorporation of adults, such as parents and teachers. Technology can be integrated either as a format (e.g., online therapy session or modality to train facilitators), a tool to assist trained facilitators or self-management (e.g., an electronic mood tracker application for youth), or a stand-alone therapeutic agent (e.g., chatbots, AI-powered therapists). This scoping review focuses specifically on Tier 1 or universal DMHIs for children and youth aged 0\u0026ndash;18. Tier 1 interventions are low-intensity but high-reach interventions designed for non-clinical, low-risk populations and aimed at promoting mental health and/or preventing clinical challenges [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These interventions focus on reinforcing positive social, emotional, cognitive, and behavioural skills to support overall well-being.\u003c/p\u003e \u003cp\u003e This scoping review aims to map study characteristics, intervention designs, target outcomes, and implementation factors to identify key features and research gaps. This review is registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (\u003cb\u003eINPLASY Protocol 6765\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e This scoping review sets out to define the current evidence base of universal DMHIs in light of the recent surge in literature and to guide future synthesis via a meta-analysis. The scoping was anchored around four topic areas and research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTarget Populations\u003c/b\u003e: What samples have been studied?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTarget Objectives and Outcomes\u003c/b\u003e: What do the interventions focus on and how are they being measured?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntervention Parameters\u003c/b\u003e: How were the interventions implemented?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntervention Delivery\u003c/b\u003e: Who facilitated the interventions?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eI. Description of Included Studies\u003c/h2\u003e\n \u003cp\u003eA total of 21,729 studies were identified in the search process. Ultimately, 52 peer-reviewed research studies on DMHIs were included in the scoping review. See Fig.\u0026nbsp;1 and Methods below for more detail regarding selection process.\u003c/p\u003e\n \u003cp\u003eThe majority of studies were conducted in upper-middle-income countries (n\u0026thinsp;=\u0026thinsp;50, 96%), primarily in North America (n\u0026thinsp;=\u0026thinsp;13, 25%) and Australia (n\u0026thinsp;=\u0026thinsp;12, 23%). See Supplementary Table\u0026nbsp;1 for a summary of Study Characteristics of Universal DMHIs For Children and Youth. Only two studies were conducted in a low-income country [22, 23].\u003c/p\u003e\n \u003cp\u003eAll identified studies were published since 2002, with the highest number published in 2024 (n\u0026thinsp;=\u0026thinsp;10, 19%). The majority were published between 2021 and 2024 (n\u0026thinsp;=\u0026thinsp;27, 52%). Study sample sizes ranged from 24 to 1,767 participants.\u003c/p\u003e\n \u003cp\u003eMost studies employed between-group designs while a notable minority used within group (i.e., repeated measures) designs to track changes within participants over time (n\u0026thinsp;=\u0026thinsp;13, 25%). Randomized controlled trials (RCTs) were the most common study design (n\u0026thinsp;=\u0026thinsp;35, 67%), with the rest of the studies utilizing non-randomized interventional studies (n\u0026thinsp;=\u0026thinsp;17, 33%). Many studies also included follow-up assessments after the initial intervention period to examine long-term effects (n\u0026thinsp;=\u0026thinsp;23, 44%), but the number and duration of follow-ups varied notably. Follow-up time periods included: Immediate (within 1 month post-intervention, n\u0026thinsp;=\u0026thinsp;2), 1\u0026ndash;3 months post-intervention (n\u0026thinsp;=\u0026thinsp;14), 4\u0026ndash;6 months post-intervention (n\u0026thinsp;=\u0026thinsp;7), and 12-months or beyond (n\u0026thinsp;=\u0026thinsp;5).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eII. Results according to key domains and research questions.\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1. Target Populations: What samples have been studied?\u003c/h3\u003e\n\u003cp\u003eThe features of target populations are summarized in Table\u0026nbsp;1. Across studies, the reported mean or median age of participants ranged from 0 to 18 years. The majority of studies focused on children and youth aged 5 to 18 years (n\u0026thinsp;=\u0026thinsp;47, 90%). A smaller subset of studies (n\u0026thinsp;=\u0026thinsp;4, 8%) targeted infants and preschool-aged children (0 to 4 years), where the interventions were typically delivered to parents or caregivers to administer to the child. Only one study (n\u0026thinsp;=\u0026thinsp;1, 2%) focused on children aged 4 to 7 years, which did not fall neatly into either the 0\u0026ndash;4 or 5\u0026ndash;18 age categories [25].\u003c/p\u003e\n\u003cp\u003eTo assess diversity within study samples, participants were categorized based on the proportion of racialized individuals reported from a local perspective (i.e., populations considered racialized within the country where the study was conducted). The majority of studies did not report racial demographic data (n\u0026thinsp;=\u0026thinsp;33, 63%). Among those that did, most reported a sample with 0\u0026ndash;24% racialized participants (n\u0026thinsp;=\u0026thinsp;10, 19%), followed by studies with 75\u0026ndash;100% racialized participants (n\u0026thinsp;=\u0026thinsp;6, 12%) and those with 25\u0026ndash;49% racialized participants (n\u0026thinsp;=\u0026thinsp;3, 6%). Of the 19 studies (37%) that reported race and cultural background, only 14 identified the most prevalent racialized population. The most frequently reported racialized populations were Latin American (n\u0026thinsp;=\u0026thinsp;7, 50%), Indigenous (n\u0026thinsp;=\u0026thinsp;3, 22%), Black (n\u0026thinsp;=\u0026thinsp;2, 14%), and East Asian (n\u0026thinsp;=\u0026thinsp;2, 14%).\u003c/p\u003e\n\u003cp\u003eRepresentation from historically marginalized communities was also assessed based on whether studies explicitly targeted these populations (e.g., interventions designed for individuals with low socioeconomic status or specific racial or cultural backgrounds). The majority of studies did not explicitly focus on populations that traditionally have faced marginalization (n\u0026thinsp;=\u0026thinsp;46, 88%), while a small proportion (n\u0026thinsp;=\u0026thinsp;6, 12%) specifically stated that their sample included participants experiencing systemic barriers. Among these, on the areas of marginalization studied were racialized and ethnically diverse youth (n\u0026thinsp;=\u0026thinsp;3), low-income populations (n\u0026thinsp;=\u0026thinsp;3), caregivers in low-resource settings (n\u0026thinsp;=\u0026thinsp;2), and Indigenous communities (n\u0026thinsp;=\u0026thinsp;1). Several studies addressed overlapping forms of marginalization, such as racial and economic disadvantage. Examples include interventions for Latinx sexual minority youth [26], Black and biracial adolescent girls from low-income urban school districts [27], and low-income Latino and African American adolescents [28]. Additional studies addressed economically disadvantaged adolescents [29], caregivers of young children in Zambia and Tanzania [23], and Inuit youth in remote Nunavut communities [30].\u003c/p\u003e\n\u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eKey features of Target Populations.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation features\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of studies (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed (4\u0026ndash;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of Racialized Participants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo racial data provided\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u0026ndash;100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMost Prevalent Racialized Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLatin American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndigenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLived Experience with Marginalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistorically Marginalized Communities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Marginalized Communities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e2. Target Objectives and Outcomes: What do the interventions focus on and how are they being measured?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2 summarizes the target objectives and outcomes of the included studies. Promotion interventions had the aim to increase skills that strengthen mental health (e.g., increase help-seeking behaviours or improving social skills). Prevention interventions had the aim of teaching skills that decrease the occurrence of symptomology associated with mental health diagnoses (e.g., teaching thought challenging to reduce worry). Interventions that addressed both promotion and prevention often aimed to strengthen a psychosocial skill while reducing a maladaptive behaviour \u0026ndash; such as increasing skills to make a friend while teaching distortion identification to prevent fear of negative evaluation [31]. The objective of the interventions were primarily designed to either promote mental health (n\u0026thinsp;=\u0026thinsp;19, 36.5%), prevent psychopathology (n\u0026thinsp;=\u0026thinsp;19, 36.5%), or address both objectives (n\u0026thinsp;=\u0026thinsp;14, 27%).\u003c/p\u003e\n\u003cp\u003eInterventions targeted a range of psychological outcomes, including emotional (e.g., measuring how participants feel), behavioural (e.g., measuring how participants act), social (e.g., measuring how participants connect with others), and cognitive (e.g., measuring how participants think or solve problems). Most studies assessed multiple psychological outcomes (n\u0026thinsp;=\u0026thinsp;30, 57%), followed by studies that focused solely on emotional outcomes (n\u0026thinsp;=\u0026thinsp;14, 27%). Only one study exclusively examined cognitive outcomes [22]. All studies used validated measures to assess intervention outcomes, with only two studies also employing study-specific unvalidated measures [28, 32]. Self-reported outcomes (n\u0026thinsp;=\u0026thinsp;36, 69%) were the most used, compared to caregiver- or teacher-reported measures (n\u0026thinsp;=\u0026thinsp;9, 17%), or a combination of both (n\u0026thinsp;=\u0026thinsp;7, 14%).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eKey features of Target Outcomes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome features\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of studies (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eObjective\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePromotion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoth (Promotion \u0026amp; Prevention)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBehavioural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eReporting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaregiver-report or teacher-report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCombination of self- and caregiver-/school- reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e3. Intervention Parameters: How were the interventions implemented?\u003c/h3\u003e\n\u003cp\u003eThe characteristics of intervention parameters, including format, design, and structure, are summarized in Table\u0026nbsp;3. The majority of studies utilized hybrid formats (n\u0026thinsp;=\u0026thinsp;28, 54%), where a digital intervention was supplemented by an in-person component (e.g., students accessing an online program with teacher facilitation at school). This was followed by fully virtual interventions (n\u0026thinsp;=\u0026thinsp;20, 38%) and a small number of studies that included both hybrid and virtual intervention arms (n\u0026thinsp;=\u0026thinsp;4, 8%). Among DMHIs with a hybrid format, the in-person component most frequently took place in school settings (n\u0026thinsp;=\u0026thinsp;28, 88%).\u003c/p\u003e\n\u003cp\u003eStudies varied in how intervention were implemented. Most interventions were online programs (n\u0026thinsp;=\u0026thinsp;24, 46%), delivered through platforms or portals that typically included multiple modules or lessons (e.g., psychoeducational programs). This was followed by apps (n\u0026thinsp;=\u0026thinsp;9, 17%), standalone software applications designed for mobile devices or computers (e.g., meditation apps). Some interventions used virtual communication tools (n\u0026thinsp;=\u0026thinsp;6, 11.5%), such as video conferencing or chat platforms for real-time interaction, while others combined virtual communication with websites (n\u0026thinsp;=\u0026thinsp;6, 11.5%) to provide additional resources, modules, or educational content. Fewer interventions relied solely on websites (n\u0026thinsp;=\u0026thinsp;2, 4%) or incorporated video game elements (n\u0026thinsp;=\u0026thinsp;4, 8%). Only one study implemented virtual reality as the primary intervention format [33].\u003c/p\u003e\n\u003cp\u003eIntervention structure also varied across studies. The most common format was independent work (n\u0026thinsp;=\u0026thinsp;24, 46%), where participants engaged in the intervention on their own, though some studies included asynchronous facilitator support (e.g., periodic check-ins or feedback on independent work). This was followed by group-based and independent work (n\u0026thinsp;=\u0026thinsp;15, 29%), where participants completed self-guided activities alongside scheduled group sessions, utilizing an online program. Some interventions adopted a multi-modal approach (n\u0026thinsp;=\u0026thinsp;6, 11%), integrating group-based, independent work, and one-to-one sessions, allowing participants to engage in self-directed activities, group discussions, and personalized support from a facilitator. A smaller number of studies used independent work combined with one-to-one support (n\u0026thinsp;=\u0026thinsp;5, 10%), where participants completed self-guided tasks but also received individualized facilitator guidance. Strictly group-based interventions were the least common (n\u0026thinsp;=\u0026thinsp;2, 4%), involving structured discussions, activities, or therapeutic exercises conducted in a group setting.\u003c/p\u003e\n\u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eKey features of Intervention Parameters.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter features\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of studies (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFormat\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVirtual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed (Hybrid\u0026thinsp;+\u0026thinsp;Virtual Arms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnline Program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVirtual Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVirtual Communication\u0026thinsp;+\u0026thinsp;Website\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVideo Game\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWebsite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVirtual Reality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndependent Work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup-Based \u0026amp; Independent Work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup-Based \u0026amp; Independent Work \u0026amp; One-to-One\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndependent Work \u0026amp; One-to-One\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup-Based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e4. Intervention Delivery\u003c/strong\u003e: \u003cstrong\u003eWho facilitated the interventions?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;4 summarizes the key features of who facilitated the intervention. Interventions were categorized as self-led, facilitator-led, or both-led. Self-led interventions allowed participants to independently access on-demand content, such as mobile apps or video games. Facilitator-led interventions involved real-time guidance from a trained mental health professional, such as in virtual therapy sessions or structured group programs. Both-led interventions included an initial facilitator-guided phase before transitioning to independent engagement. The majority of interventions were both-led (n\u0026thinsp;=\u0026thinsp;35, 67%), while fewer were fully self-led (n\u0026thinsp;=\u0026thinsp;13, 25%) or solely facilitator-led (n\u0026thinsp;=\u0026thinsp;4, 8%).\u003c/p\u003e\n\u003cp\u003eOf the 52 studies included in this review, 38 (73%) involved a facilitator in some fashion. Research personnel were the most frequently involved facilitators (n\u0026thinsp;=\u0026thinsp;12, 31%), consisting of researchers or academic staff without clinical qualifications. Registered mental health professionals (n\u0026thinsp;=\u0026thinsp;7, 18%), including licensed psychologists, psychiatrists, and clinical social workers, were also commonly involved. Teachers (n\u0026thinsp;=\u0026thinsp;6, 15%) played a significant role in several studies, often integrating mental health interventions into their school curriculum. Some studies used multidisciplinary teams (n\u0026thinsp;=\u0026thinsp;5, 13%), where licensed mental health professionals worked alongside child and youth workers or counselors. Other studies relied on non-registered mental health professionals (n\u0026thinsp;=\u0026thinsp;4, 10%), such as peer supporters, wellness coaches, or unlicensed counselors. A small number of studies (n\u0026thinsp;=\u0026thinsp;2, 5%) did not specify who facilitated the intervention, while another small number of studies (n\u0026thinsp;=\u0026thinsp;2, 5%) were exclusively facilitated by child and youth workers and counselors. In addition, one study involved a mix of teachers and non-registered mental health professionals [33].\u003c/p\u003e\n\u003cp\u003eThe level of facilitator involvement varied across studies. In most cases, facilitators were engaged in teaching or counseling (n\u0026thinsp;=\u0026thinsp;26, 67%). This included providing direct instruction, delivering therapeutic guidance, or offering structured support through both group and one-on-one sessions. Their roles often involved leading psychoeducational sessions, conducting mental health counseling, and facilitating discussions and interactive activities. In contrast, some facilitators served as non-involved resources (n\u0026thinsp;=\u0026thinsp;13, 33%). In these cases, facilitators did not actively guide the intervention but instead provided supplementary materials or background support. Their involvement included being available to answer questions, overseeing access to digital resources, or offering general guidance without directly interacting with participants.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eKey features of Intervention Delivery.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDelivery features\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of studies (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDelivery\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoth-led\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-led\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFacilitator-led\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacilitator Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResearch Personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegistered Mental Health Professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegistered Mental Health Professionals \u0026amp; Child \u0026amp; Youth Workers \u0026amp; Counselors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Registered Mental Health Professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Described\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChild \u0026amp; Youth Workers \u0026amp; Counselors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacilitator Involvement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTeaching/Counseling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Involved Resource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e This scoping review describes the current state of the evidence base on universal (Tier 1) digital mental health interventions (DMHIs) for children and youth aged 0 to 18 years. While prior reviews have explored DMHIs for young people aged 10\u0026ndash;24 years [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and broader samples that include universal, at-risk populations, and clinically-diagnosed populations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], most existing scoping reviews have focused primarily on adults [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e35\u003c/span\u003e] or children and youth within at-risk or clinical populations [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This review is unique in its exclusive focus on universal DMHIs for children and youth, identifying key trends in target populations, outcomes, intervention design, and delivery methods. By mapping these elements, the review provides valuable insights into the current state of DMHIs and highlights critical gaps in the literature. These findings contribute to a deeper understanding of how DMHIs support mental health promotion and the prevention of psychopathology among young populations. The rapid surge in DMHI research following the pandemic necessitates careful consideration of emerging trends and gaps in current approaches.\u003c/p\u003e \u003cp\u003eMost studies (90%) focused on participants aged 5 to 18 years, with limited attention to early childhood populations (0 to 4 years). This skewed age representation highlights a critical gap in DMHIs for very young children, a key developmental period for building foundational psychological skills. Digital interventions targeting early childhood are necessarily caregiver-mediated, aiming to improve caregiver responsiveness and parenting skills to support early psychological development in children [37, 38, 23, 39]. Early brain development is a window of extraordinary opportunity but also profound vulnerability [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://developingchild.harvard.edu\u003c/span\u003e\u003cspan address=\"https://developingchild.harvard.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e/resources/working-paper/wp3/]. Without intervention, toxic stress can reshape brain architecture, increasing the risk of lifelong mental health challenges. Virtual therapy is not just an alternative, it is a lifeline for parents who would otherwise lack access. DMHIs break barriers, delivering scalable, equitable solutions to protect early development. These parents often have the greatest difficulty travelling to in-person appointments. DMHIs break barriers, delivering scalable, equitable solutions to protect early development. The glaring absence of evidence-based early childhood interventions in DMHIs cannot be overlooked.\u003c/p\u003e \u003cp\u003eDespite increasing attention to digital health equity, historically racialized and marginalized communities also remain underrepresented in DMHI research. Only 12% of studies included participants from marginalized populations, raising concerns about the generalizability and accessibility of DMHIs for diverse populations. Once again, this gap is in a troubling area \u0026ndash; these are the young populations at greatest risk of developing mental health challenges in the future. To address these challenges and bridge the digital equity divide, future research must prioritize efforts to co-develop DMHIs with communities that are marginalized to develop also interventions that are accessible, effective, and culturally responsive. Additionally, researchers should work toward improving racial demographic reporting in DMHI studies to ensure that racialized and underserved populations are adequately represented. As noted in our results, the majority of studies (n\u0026thinsp;=\u0026thinsp;33, 63%) did not report racial demographic data, making it difficult to assess whether DMHIs effectively serve racialized and underrepresented populations. Without detailed demographic reporting relating to racialization, it remains unclear who benefits most from these interventions.\u003c/p\u003e \u003cp\u003eMost interventions were hybrid, meaning they combined a digital component with in-person support, such as facilitator guidance or structured activities in schools or community settings. The frequent use of hybrid interventions reflects a growing trend toward blended approaches that integrate digital tools with in-person engagement to improve effectiveness and user experience. However, the optimal level of in-person involvement in digital interventions remains unclear, as few studies put a hybrid versus virtual intervention head to head. Future research should examine whether hybrid models provide greater benefits than fully virtual formats and identify which aspects of in-person support contribute most to intervention success.\u003c/p\u003e \u003cp\u003eAdditionally, while most studies assessed multiple psychological outcomes, the majority focused on emotional outcomes, with fewer addressing behavioural, social, or cognitive outcomes. This reflects a gap in the development of interventions that target broader psychosocial and cognitive skills, which are essential for comprehensive youth mental health support. As digital interactions increasingly replace in-person experiences, the need to investigate and support social skill development is more pressing than ever. Research shows that pandemic-related isolation hindered social cognitive growth in early childhood, particularly for children from lower socioeconomic backgrounds [41]. Likewise, rising digital engagement reduces opportunities for youth to develop essential social competencies like empathy, communication, and conflict resolution [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://effectiveschoolsolutions.com/teenage-social-skills/\u003c/span\u003e\u003cspan address=\"https://effectiveschoolsolutions.com/teenage-social-skills/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. As digital adolescence becomes the norm, future research must prioritize integrating social skill-building into DMHIs to ensure that youth are equipped with the interpersonal skills necessary for well-being and resilience in an increasingly digital world.\u003c/p\u003e \u003cp\u003eDespite the expansion of DMHIs, immersive digital technologies such as video games (8%) and virtual reality (2%) were rarely utilized as standalone interventions. This underutilization represents a missed opportunity to improve engagement, particularly among younger populations. Future research must move beyond simply replicating traditional therapeutic approaches in digital formats and instead harness the interactive and immersive potential of these technologies. By creatively integrating game-based and virtual experiences into DMHIs, interventions can better align with the natural interests of children and youth, ultimately improving user experience and intervention effectiveness.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis scoping review highlights key trends and gaps in DMHIs for children and youth. While hybrid interventions and online psychoeducational programs dominate the landscape, significant gaps remain in early childhood interventions, digital health equity, facilitator involvement, and the use of immersive digital tools. Addressing these gaps is essential to ensuring that DMHIs are inclusive, effective, and accessible to all young people.\u003c/p\u003e \u003cp\u003eRecommendations for Future Research:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExpand research on DMHIs for early childhood (0\u0026ndash;4 years) to better understand how digital tools can support early psychological development.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eImprove digital health equity by increasing representation of historically marginalized populations, improving reporting on racial and cultural backgrounds, and identifying barriers to access and engagement.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExpand research on DMHIs that target a broader range of psychological outcomes beyond emotional outcomes, particularly giving the evolving nature of how children may be learning social skills with large components of their life online.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIncrease the development of immersive digital tools that build on what children and youth enjoy, such as virtual reality and gamification, to assess feasibility and potential for engagement\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExamine the impact of hybrid versus fully virtual interventions on mental health outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile this review offers a comprehensive overview of the existing literature, several limitations should be noted. The inclusion of only peer-reviewed studies means that unpublished research and grey literature were not considered, which may have led to the omission of relevant interventions. Additionally, the predefined eligibility criteria may have excluded studies with valuable insights that did not explicitly report on these criteria. Despite these limitations, this review provides a broad mapping of how DMHIs are currently being implemented in universal child and youth populations. Addressing gaps in early childhood intervention and digital health equity is crucial to ensuring that DMHIs effectively meet the diverse mental health needs of young people and can be scaled for widespread implementation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cp\u003eWe followed the PRISMA Extension for Scoping Reviews [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e43\u003c/span\u003e] (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003ch3\u003eEligibility Criteria\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eTypes of Sources.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e This review included full-text, peer-reviewed original research articles, as these represent the highest standard for evidence-based practice in health and social care. Studies published in non-peer-reviewed sources, such as book chapters, case studies, conference abstracts, and dissertations, were excluded.\u003c/p\u003e \u003cp\u003e \u003cb\u003eParticipants.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStudies were eligible if the minimum or mean age of participants was between 0 and 18 years. Studies including some participants over 18 were still considered if the average age of the sample was approximately 18 years or younger. Studies that exclusively focused on individuals older than 18 or did not specify participant age were excluded.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMental Health Status.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOnly studies involving non-clinical populations were included. Studies were excluded if they focused on participants who had been hospitalized for acute medical or psychiatric conditions (e.g., suicide attempt or cancer treatment) or had recently been discharged from such care.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIntervention Format.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStudies had to include a digital intervention. Eligible interventions were either virtual (delivered entirely online, without in-person components) or hybrid (integrating digital tools with in-person elements, such as school-based implementation). Studies that relied solely on in-person interventions without a digital component were excluded.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMeasured Outcomes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStudies were included if they measured at least one mental health outcome within the domains of emotional, behavioural, social, or cognitive well-being. Studies that assessed only physiological measures without a corresponding mental health outcome were excluded. For each study, only one measure per outcome was selected, prioritizing the most psychometrically valid assessment.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSearch Strategy\u003c/h2\u003e \u003cp\u003eA systematic search for DMHIs targeting psychological outcomes was created with a highly experienced academic librarian. The search process began with a preliminary scan of Google Scholar to identify key articles relevant to the three main components of the research question: universal scope, DMHIs, and children and youth. These initial articles informed the development of a comprehensive list of keywords, which were refined to ensure compatibility across databases.\u003c/p\u003e \u003cp\u003eThe final search strategy was implemented in three major databases: MEDLINE, PsycInfo, and Embase. An example of the search terms used across these databases is provided in Supplementary Table\u0026nbsp;2. The initial search was conducted between June 26, 2024, and July 5, 2024. To capture newly published literature, an updated search using the same strategy was performed on January 6, 2025, covering studies published between July 3, 2024, and January 6, 2025.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEvidence Selection\u003c/h2\u003e \u003cp\u003eAll search results were imported into Covidence, a reference management and screening tool, to facilitate de-duplication, title and abstract screening, full-text review, and data extraction. While many records were automatically categorized as database sources based on indexed journal information, a large proportion (n = 10,532) were labeled as “unspecified sources” due to missing metadata. This included manually uploaded references and records lacking identifiable source information.\u003c/p\u003e \u003cp\u003e Title and abstract screening was conducted by a four-person review team, with all records independently screened by two reviewers. Reliability screening was performed on a subset of studies (25% of the total sample) to assess consistency in screening decisions. Full-text articles deemed potentially relevant were uploaded to Covidence for further screening by two independent reviewers. Discrepancies in inclusion or exclusion decisions were resolved through weekly consensus meetings among the authorship team. Full-text data extraction was conducted using both Covidence and Excel, and always performed by two independent reviewers.\u003c/p\u003e \u003cp\u003eAdditionally, a manual search of reference lists from excluded but flagged systematic reviews was conducted, along with targeted handsearching of key journals and sources likely to contain relevant studies. Inter-rater agreement for title and abstract screening ranged from 0.84 to 0.98, while agreement for full-text review ranged from 0.90 to 0.96, indicating an almost perfect agreement between reviewers [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData Charting Process\u003c/h2\u003e \u003cp\u003eData extraction was conducted using Microsoft Excel, with categories initially adapted from the PICO framework: Population, Intervention, Comparators, and Outcomes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Variables selected for extraction (as reported above) was an iterative process as familiarity with the scope of literature deepened. Data extraction templates were piloted and revised to improve clarity and consistency between co-authors. Each article was independently extracted by two reviewers to improve accuracy and reliability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData Synthesis\u003c/h2\u003e \u003cp\u003eData synthesis was performed using Microsoft Excel. Inter-rater agreement was calculated during title and abstract screening and full-text review. Descriptive statistics (frequencies, medians, and ranges) were used to summarize study characteristics. Data were presented graphically and in tabular format where appropriate. We generated descriptive summaries of study characteristics, including the frequency and distribution of publication year, country of origin, and study design. Additionally, we summarized participant characteristics (e.g., age distribution, lived experience with marginalization), DMHI characteristics (e.g., intervention format, setting, and facilitation type), and mental health outcome measures (e.g., domains assessed, types of scales used, and reporting methods).\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eReporting Summary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther information on research design is available in the Nature Research Reporting Summary linked to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not analyze or generate any new datasets. Aggregated data used in this review can be obtained from the corresponding author upon request. Since this scoping review relies solely on peer-reviewed articles, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo code was generated during the current study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Jackman Foundation and York University (funding dedicated to DIVERT Mental Health collaboration, a CIHR funded Health Research Training Platform) for funding that supported this research project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKDP co-conceptualized the study, led the literature search and analysis, and drafted the initial manuscript. RPR conceptualized the study, assisted with literature analysis, and co-drafted the initial drafts of the manuscript. OB, HH, and AL assisted with literature analysis and provided editorial comments on submitted manuscript. A Lovegrove was actively involved in developing the study\u0026rsquo;s conceptual foundation. RC provided editorial comments on submitted manuscript, as the statistical consultant on the associated meta-analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKessler, R. C. et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. \u003cem\u003eArch. Gen. 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PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. \u003cem\u003eAnn. Intern. Med\u003c/em\u003e.\u003cstrong\u003e\u0026nbsp;169\u003c/strong\u003e, 467\u0026ndash;473 (2018).\u003c/li\u003e\n \u003cli\u003eMcHugh, M. L. Interrater reliability: the kappa statistic. \u003cem\u003eBiochem. Med\u003c/em\u003e. \u003cstrong\u003e22\u003c/strong\u003e, 276\u0026ndash;282 (2012).\u003c/li\u003e\n \u003cli\u003eJoanna Briggs Institute (JBI). \u003cem\u003eJBI Reviewer\u0026rsquo;s Manual: methodology for JBI scoping \u003cem\u003ereviews\u003c/em\u003e. The Joanna Briggs Institute (2014).\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Figure 1","content":"\u003cp\u003eSupplementary Figure 1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6473660/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6473660/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis scoping review examines universal digital mental health interventions (DMHIs) for children and youth. A total of 52 studies were identified, primarily conducted in high-income countries, using a hybrid format, involving adult facilitators, and assessing multiple psychological outcomes in children aged 5\u0026ndash;18. Emotional outcomes were the most commonly studied. Findings highlight important directions for the future by highlighting gaps in the literature such that those in greatest need of accessible mental health and that would most likely benefit have been severely understudied (i.e., traditionally disadvantaged populations and early childhood).\u003c/p\u003e","manuscriptTitle":"Universal Digital Mental Health Interventions for Children and Youth ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 16:47:54","doi":"10.21203/rs.3.rs-6473660/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2606a267-38d9-47de-b60e-02ef97a217d4","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47766440,"name":"Biological sciences/Psychology"},{"id":47766441,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-06-27T08:38:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 16:47:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6473660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6473660","identity":"rs-6473660","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}