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ArtículoFitoVida; Vol. 4 No. 1 (2025): Edition: January - June; 44 - 532026SPA

De las pantallas al bienestar neurodigital: depresión y ansiedad en hombres y mujeres del Perú desde la perspectiva de la OCDE, las neurociencias y la inteligencia artificial

Buendía Giribaldi, Atilio Rodolfo

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Resumen

The spread of digital technologies has transformed processes of communication, learning, socialization, and identity formation, while anxiety and depression have become key public health challenges. Consequently, research into the relationship between screen use and mental health must move beyond metrics based solely on the number of hours spent using devices. Recent evidence from the Organisation for Economic Co-operation and Development (OECD) highlights the complexity of this relationship, noting its heterogeneity and dependence on the type of use, content, individual context, and social conditions; specific patterns of digital usage can be linked to sleep disturbances, cyberbullying, social comparison, isolation, and numerous other factors affecting psychological well-being. This conceptual article proposes a framework for investigating the relationship between screen use, anxiety, and depression among men and women in Peru, integrating neuroscientific insights into neurobehavioral mechanisms and positioning artificial intelligence as a potential tool for early detection and personalized prevention. It posits that the psychological impact of digitalization may be mediated by sleep, emotional regulation, attention, physical activity, and social relationships, and moderated by sex, age, socioeconomic status, and territorial context. The NEURO-PERÚ DIGITAL model is presented-comprising five components (neuroscience, education, healthy usage, early risk assessment, and longitudinal observation)-alongside seven research hypotheses and a proposal for a national study. The model aims to inform the development of public policies and university strategies focused on digital well-being, without pathologizing technology use or assuming causality based on observational associations.

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