Pixels and perception: Mapping the association between digital media and psychotic-like experiences in adolescents.

INTRODUCTION: Psychotic-like experiences (PLEs) during adolescence can lead to psychotic disorders. Digital media usage has been suggested to link to PLEs, but research is limited on how different types of screen exposure may differentially relate to PLEs over time. This study aimed to examine longitudinal associations between screen usage patterns and PLEs in adolescents. METHODS: Participants comprised 11,876 adolescents assessed annually from ages 9-12 years as part of the Adolescent Brain Cognitive Development study (ABCD). Screen usage (TV, video games, online video, social media, texting, video chat) and PLEs were assessed via self-report. Longitudinal network analysis models were estimated to examine connections between screen usage types and PLEs across three time points. RESULTS: Two clusters were formed, including digital media for socializing (e.g., social media/texting/video chat) and digital media for entertainment (e.g., online video/video games/TV). Texting and online video(s) had the highest centrality at each time point, suggesting importance in the network. PLE symptoms of hallucinations and concentration difficulties exhibited higher centrality than other symptoms. Online video and TV were influential bridges between screen usage and PLEs. Network structure significantly differed between ages 9-10 and 10-12 years, but global strength was unchanged over time. DISCUSSION: Results highlight the importance of understanding the associations between specific screen usage types and PLE symptoms. Texting and online video usage appear most influential in the development of adolescent PLEs over time. Findings can inform targeted interventions to promote healthy screen habits and reduce PLEs in at-risk youth.

Examining how gamers connect with their avatars to assess their anxiety: A novel artificial intelligence approach.

Research has supported that a gamer's attachment to their avatar can offer significant insights about their mental health, including anxiety. To assess this hypothesis, longitudinal data from 565 adult and adolescent participants (Mage = 29.3 years, SD = 10.6) was analyzed at two points, six months apart. Respondents were assessed using the User-Avatar Bond (UAB) scale and the Depression Anxiety Stress Scale (DASS) to measure their connection with their avatar and their risk for anxiety. The records were processed using both untuned and tuned artificial intelligence [AI] classifiers to evaluate present and future anxiety. The findings indicated that AI models are capable of accurately and autonomously discerning cases of anxiety risk based on the gamers' self-reported UAB, age, and duration of gaming, both at present and after six months. Notably, random forest algorithms surpassed other AI models in effectiveness, with avatar compensation emerging as the most significant factor in model training for prospective anxiety. The implications for assessment, prevention, and clinical practice are discussed.

Cross-Addiction Risk Profile Associations with COVID-19 Anxiety: a Preliminary Exploratory Study.

"Cross-addiction" involves a person substituting one form of addictive behaviour for another. Indeed, cross-additive presentations have been frequently described (e.g. from drugs to alcohol, gambling to sex), and risk profiles have been assumed. Nevertheless, there has been a dearth of evidence considering the occurrence of cross-addiction risk profiles in the community. This research is imperative for informing effective prevention/intervention policies, especially under anxiety-provoking conditions, such as the current coronavirus pandemic. To address this need, a cross-sectional exploratory research design was utilized, with quantitative survey data obtained from 968 respondents (18-64; M age = 29.5 years, SD = 9.36), who completed an online survey regarding a range of addictive behaviours (i.e. abuse of alcohol, drug, smoking, online gaming, shopping, internet, exercise, online gambling, sex, and social media) and their anxiety about the coronavirus. Latent class/profiling analyses were implemented to (a) explore profiles of cross-addiction risk, (b) describe the characteristics and the proportions of these profiles, and (c) identify their differential associations with the pandemic precipitated anxiety. Findings revealed two distinct profiles/types, the "cross-addiction low risk" (57.4%) and the "cross-addiction high risk" (42.6%). Those in the latter scored consistently higher across all behaviours assessed, were more likely to suffer from concurrent addictive problems, and reported significantly higher levels of pandemic-related anxiety. Implications for prevention, assessment, and treatment and future research are discussed.