Uncovering the structure of media multitasking and attention problems using network analytic techniques

Media multitasking has become nearly ubiquitous in the developed world. Higher self-reported media multitasking has consistently been shown to relate to self-reported attention problems, including symptoms of attention deficit/hyperactivity disorder (ADHD), but the magnitude of this relationship is small and heterogeneous across studies. These findings have motivated calls for increased specificity in media multitasking research, moving beyond aggregated summaries of multitasking behavior in favor of an approach that considers how specific combinations of media behaviors relate to cognitive outcomes of interest. Herein, we take a data-driven (Jack et al., 2018), computational approach to uncover the network structure of media multitasking behaviors in a sample of 2542 young adults in the United States. Results indicate that those with greater severity of ADHD symptoms tend to have more densely connected multitasking networks overall, as well as differing patterns of node centrality within the network. These results provide increased understanding of how individual differences in media multitasking habits relate to attention and cognition, and point to the promise of network-based analyses developing a fuller understanding within this topic domain.