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Adolescents’ rising depressive and anxiety symptoms (Mojtabai & Olfson, 2020) paired with near constant use of mobile technologies and social media has triggered concerns around this relation and synchronous trend (Twenge, 2020). Yet, evidence has been limited to warrant causal effects (Odgers & Jensen, 2020) and most prior research has examined key indicators in isolation. Given that multiple factors are likely to determine both adolescents’ mental health and how they interface with technology ecosystems, there is a need for a broader understanding of how the interactions influence outcomes during this developmental period. This study utilizes data from a recent national sample (N = 11,875) of early adolescents (Volkow et al., 2018; abcdstudy.org) to examine multilevel correlates of early adolescent internalizing behaviors through the lens of the digital age. Analyses and interpretations are framed around a Social Ecological Framework (SEF) to evaluate and compare associations of factors across the tiered domains of the framework. These include individual factors (e.g. screen time, cognitive functioning), family factors (e.g. family mental health history, income, family conflict, and parental monitoring), social factors (e.g. peer relationships), and community-level factors (e.g. neighborhood safety). Correlations (Table 1) revealed factors related to family history of mental illness (r=-0.19), family conflict (r=0.09), parental monitoring (r=-0.09) and neighborhood safety (r=-0.11) have a stronger association to internalizing behaviors compared to that of weekday (r=0.00) and weekend (r=0.02) social media use and cumulative screen time (weekday r=0.05; weekend r=0.06). When examining the multilevel correlations separately based on adolescent sex, there are small yet significant positive associations across overall screen time and internalizing behaviors among males (weekday r=0.08; weekend r=0.06), while female associations are non-significant. Sex differences are also significant between overall levels of internalizing behaviors (t=-9.87, p<0.001) with adolescent males reporting higher levels (M=49.4, SD=10.7) compared to adolescent females (M=47.4, SD=10.5), which contradicts prior research. Multiple regression models further addressed overall key associations between screen time and theoretically based risk factors for early adolescent internalizing behaviors. In a fully controlled model including covariates of adolescent sex, age, race/ethnicity and parent education, results suggest family (family history of mental illness β = 0.17, 95% CI = [0.15, 0.19], p <0.001; family conflict (β = 0.06, 95% CI = [0.04, 0.08], p <0.001), peer (β = -0.03 95% CI = -0.05, 0.00], p = 0.02), and neighborhood environment (β = -0.11, 95% CI = [ -0.13, -0.08 ], p <0.001) factors maintain significant relations to internalizing behaviors with greater effect sizes compared to screen time variables (Table 2). Exploratory analyses stratified by adolescent race/ethnicity were also conducted to explore potential differences by sociocultural influences. The results of this analysis revealed differing patterns of associations between groups, suggesting unique associations of screen time and internalizing behaviors potentially influenced by various cultural backgrounds. Limitations of this study includes the cross-sectional design, a lack of a nationally representative sample, and limited metrics of screen time. Overall, the findings suggest that multiple social ecological factors beyond technology use may influence internalizing behaviors to a greater extent during early adolescence.