Youth Screen Media Activity Patterns and Associations With Behavioral Developmental Measures and Resting-state Brain Functional Connectivity.

objectiveScreen media activity (SMA) consumes considerable time in youth's lives, raising concerns about the effects it may have on youth development. Disentangling mixed associations between SMA of youth and developmental measures should move beyond overall screen time and consider types and patterns of SMA. This study aimed to identify reliable and generalizable SMA patterns among youth and examine their associations with behavioral developmental measures and developing brain functional connectivity.

methodThree waves of Adolescent Brain and Cognitive Development (ABCD) data were examined. The Lifespan Human Connectome Project in Development (HCP-D) was interrogated as an independent sample. ABCD participants included 11,876 children at baseline. HCP-D participants included 652 children and adolescents. Youth-reported SMA and behavioral developmental measures (neurocognitive performance, behavioral problems, psychotic-like experiences, impulsivity, and sensitivities to punishment/reward) were assessed with validated instruments. We identified SMA patterns in the ABCD baseline data using K-means clustering and sensitivity analyses. Generalizability and stability of the identified SMA patterns were examined in HCP-D data and ABCD follow-up waves, respectively. Relations between SMA patterns and behavioral and brain (resting-state brain functional connectivity) measures were examined using linear mixed effects modeling with false discovery rate (FDR) correction.

resultsSMA data from 11,815 children (mean [SD] age = 119.0 [7.5] months; 6,159 [52.1%] boys) were examined; 3,151 (26.7%) demonstrated a video-centric higher-frequency SMA pattern, and 8,664 (73.3%) demonstrated a lower-frequency pattern. SMA patterns were validated in similarly aged HCP-D youth. Compared with the lower-frequency SMA pattern group, the video-centric higher-frequency SMA pattern group showed poorer neurocognitive performance (β = -.12, 95% CI [-0.08, -0.16], FDR-corrected p < .001), more total behavioral problems (β = .13, 95% CI [0.09, 0.18], FDR-corrected p < .001), and more psychotic-like experiences (β = .31, 95% CI [0.27, 0.36], FDR-corrected p < .001). The video-centric higher-frequency SMA pattern group demonstrated higher impulsivity, more sensitivity to punishment/reward, and altered resting-state brain functional connectivity among brain areas implicated previously in cognitive processes. Most of the associations persisted with age in the ABCD data, with more participants (n = 3,378, 30.4%) in the video-centric higher-frequency SMA group at 1-year follow-up. A social communication-centric SMA pattern was observed in HCP-D adolescents.

conclusionVideo-centric SMA patterns are reliable and generalizable during late childhood. A higher-frequency video entertainment SMA pattern group showed altered resting-state brain functional connectivity and poorer developmental measures that persisted longitudinally. The findings suggest that public health strategies to decrease excessive time spent by children on video entertainment-related SMA are needed. Further studies are needed to examine potential video-centric/social communication-centric SMA bifurcation to understand dynamic changes and trajectories of SMA patterns and related outcomes developmentally. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.