Preference for digital media use, biobehavioral attention bias, and anxiety symptoms in adolescents

Sarah Myruski, Jennifer de Rutte, Abigail Findley, Amy K. Roy, Tracy A. Dennis-Tiwary

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Adolescence is a critical developmental period of biological and social change during which 1 in 3 youth experience significant anxiety symptoms. The social-emotional lives of the majority of adolescents are largely conducted via digital media use (DMU; e.g., social media, text messaging). Yet the past decade of research on DMU and anxiety has yielded mixed results (e.g., Keles et al., 2020 review), leaving the complex role that DMU might play in the emergence and maintenance of anxiety poorly understood. A key step forward is to leverage psychophysiology to identify individual differences in cognitive and emotional processes that confer vulnerability to potential negative effects of DMU. Further, given the ubiquity of DMU, a greater focus is needed on measurements that move beyond sheer frequency to capture DMU in comparison to face-to-face (FTF) social interactions. This study examined attention bias (AB), characterized by selective and exaggerated attention toward or away from threat, as a moderator of the link between DMU and anxiety in adolescents (N = 75; 42 female) aged 12–14 years (M = 13.28, SD = 0.87). AB was indexed during a dot probe task using reaction time metrics (i.e., trial-level bias) and via ERPs capturing attentional selection and discrimination (N170) and cognitive control (N2) to threat compared to neutral faces. AB moderated associations between DMU and anxiety. A greater preference to use DMU vs FTF predicted greater anxiety among those with a greater behavioral bias away from threat, blunted N170, and blunted N2 in the presence of threat. Future research should examine potential causal and bidirectional links between DMU and anxiety and explore whether preferences for technology-mediated interactions and individual differences in threat processing increase risk.

Original languageEnglish (US)
Article number100439
JournalComputers in Human Behavior Reports
StatePublished - Aug 2024

All Science Journal Classification (ASJC) codes

  • Neuroscience (miscellaneous)
  • Applied Psychology
  • Human-Computer Interaction
  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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