{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T04:19:39Z","timestamp":1791001179743,"version":"4.1.0"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Digital media use has expanded rapidly among medical students, raising concerns about its impact on cognitive function and academic achievement. However, specific links between screen time and distinct facets of cognition (such as fluid vs. crystallized intelligence), as well as their combined impact on academic performance, remain underexplored in medical education.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study explored how digital screen time and related behaviors are associated with different dimensions of intelligence (fluid and crystallized), attention, and academic performance in second-year medical students, using a network analysis approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A cross-sectional sample of 305 second year medical students from the Faculty of Medicine, UNAM, recruited during the 2023 academic year, completed standardized assessments: the Shipley-2 test for crystallized (Gc) and fluid intelligence (Gf), the Youth Screen Time Survey, and official academic records (standardized Physiology course exam scores). Network analysis was implemented through the\n                      <jats:italic>igraph<\/jats:italic>\n                      package in R to identify structural relationships among 34 variables representing cognitive, behavioral, and academic domains.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The network analysis identified six clusters of interrelated variables, revealing distinct groupings linking digital behavior, cognitive abilities, and academic outcomes. Key variables with the highest connectivity (centrality) were the number of failed courses, total screen time, and age, indicating these factors are most influential in the network. Notably, text messaging and short-form video use (e.g., TikTok) emerged as\n                      <jats:italic>bridge<\/jats:italic>\n                      nodes connecting digital media use to academic performance. Higher screen time was associated with lower academic performance (\n                      <jats:italic>r<\/jats:italic>\n                      \u202f\u2248\u202f\u22120.24) and reduced fluid intelligence, while crystallized intelligence appeared relatively unaffected.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Excessive digital exposure was associated with weaker cognitive efficiency and academic performance in this cross-sectional sample. These findings underscore the need for balanced digital habits to support attention, learning, and problem-solving capacity in medical students.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1736060","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T05:33:12Z","timestamp":1771997592000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Digital media use and cognitive networks in medical students: linking screen time with intelligence and academic performance"],"prefix":"10.3389","volume":"13","author":[{"given":"Alejandro","family":"Hern\u00e1ndez-Ch\u00e1vez","sequence":"first","affiliation":[{"name":"Laboratory of Prospective, Department of Physiology, Faculty of Medicine, National Autonomous University of Mexico","place":["M\u00e9xico City, Mexico"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian B.","family":"Uriarte-Ortiz","sequence":"additional","affiliation":[{"name":"Laboratory of Prospective, Department of Physiology, Faculty of Medicine, National Autonomous University of Mexico","place":["M\u00e9xico City, Mexico"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Barajas-Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Tecnol\u00f3gico de Monterrey, School of Medicine and Health Sciences","place":["M\u00e9xico City, Mexico"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ra\u00fal","family":"Sampieri-Cabrera","sequence":"additional","affiliation":[{"name":"Laboratory of Prospective, Department of Physiology, Faculty of Medicine, National Autonomous University of Mexico","place":["M\u00e9xico City, 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