{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:01:52Z","timestamp":1775595712390,"version":"3.50.1"},"reference-count":54,"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background and aims<\/jats:title>\n                    <jats:p>The post-pandemic period has shown sustained digital intensification associated with adverse mental health outcomes. Focusing on adults, this study examined associations between screen time, sleep duration, social media use, and anxiety in a global sample from 2020 to 2024. It further tested moderation by age and sleep adequacy and identified high-risk behavioral subgroups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study applied hierarchical regression models and person-centered clustering techniques to a publicly available, repeated cross-sectional global dataset on adult mental health and lifestyle (2019\u20132024;\n                      <jats:italic>N<\/jats:italic>\n                      \u202f=\u202f3,000). Moderation by age and sleep adequacy was assessed, and behavioral profiles were identified using k-means clustering. Statistical significance was evaluated at\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Screen time was positively associated with anxiety, with each additional hour corresponding to a b of 0.30 (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). Similarly, each additional hour of social media use was associated with a\n                      <jats:italic>b<\/jats:italic>\n                      of 0.25 (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). In contrast, sleep duration showed a protective effect, with each additional hour linked to a b of \u22120.36 (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). The association between screen time and anxiety was stronger among adults younger than 40\u202fyears (\n                      <jats:italic>b<\/jats:italic>\n                      \u202f=\u202f0.323) than among those aged 40\u202fyears and older (\n                      <jats:italic>b<\/jats:italic>\n                      \u202f=\u202f0.265), and this age-based interaction was statistically significant (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.039). A high-screen and low-sleep behavioral profile, comprising 34.3 percent of the sample, exhibited the highest mean anxiety level (\n                      <jats:italic>M<\/jats:italic>\n                      \u202f=\u202f6.27).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Implications<\/jats:title>\n                    <jats:p>Findings support age-stratified interventions emphasizing sleep hygiene and nighttime device boundaries for younger adults. Screening for high-screen and low-sleep behavioral profiles in clinical and educational settings is recommended. Future longitudinal research using objective measures is needed to clarify causality and guide targeted public health strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1766808","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T13:31:35Z","timestamp":1772026295000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Digital behavior and anxiety in the post-pandemic era: a five-year analysis of screen time, sleep, and behavioral risk profiles"],"prefix":"10.3389","volume":"14","author":[{"given":"Wenjing","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Business Administration, Sejong University","place":["Seoul, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.jbusres.2021.08.011","article-title":"COVID-19 and digitalization: the great acceleration","volume":"136","author":"Amankwah-Amoah","year":"2021","journal-title":"J Bus Res"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"102447","DOI":"10.1016\/j.techsoc.2023.102447","article-title":"Irreversible effects? 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