{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T14:07:23Z","timestamp":1787926043899,"version":"build-2784847793"},"reference-count":71,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Interact J Med Res"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The human capacity to process information is limited. During the COVID-19 pandemic, people were exposed to a large amount of uncertain and complex health information. This situation made some people experience perceived information overload, which made them unable to adopt appropriate preventive behaviors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to examine the individual characteristics, abilities, and attention to informational media that predict the perception of information overload during a pandemic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a longitudinal study with 2 time points, August 2020 and August 2021, among residents of Japan under a COVID-19 emergency declaration. The sample had the same proportions for sex, age, and prefecture as the general Japanese population. We used a web-based survey to measure sociodemographic characteristics, health literacy (HL), attention to 6 different types of information channels, and participants\u2019 perception of information overload. Hierarchical multiple regression analysis was conducted with information overload as the objective variable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 784 participants responded to the survey at both time points, with a follow-up rate of 78.4% (784\/1000). Hierarchical multiple regression analysis showed that younger age (\u03b2=\u22120.084, 95% CI \u22120.142 to \u22120.013), male sex (\u03b2=\u22120.163, 95% CI \u22120.008 to \u22120.003), lower HL (\u03b2=\u22120.084, 95% CI \u22120.114 to \u22120.011), paying less attention to television news (\u03b2=\u22120.118, 95% CI \u22120.038 to \u22120.001), and paying greater attention to social media (\u03b2=0.089, 95% CI 0.000-0.027) significantly predicted information overload 1 year after exposure to information during the pandemic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Public health communicators should aim to provide concise and understandable information in consideration of a target population that is vulnerable to information overload during a pandemic. A high level of attention to social media may increase the perception of information overload. By contrast, HL may reduce the cognitive load in information processing. Providing an environment during normal periods that allows people to develop the skills to critically interpret health information will help them to prepare for future infodemics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/67098","type":"journal-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T21:15:05Z","timestamp":1761858905000},"page":"e67098","source":"Crossref","is-referenced-by-count":4,"title":["Factors Predicting Information Overload During the COVID-19 Pandemic in the Digital Age: Longitudinal 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