{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T19:33:59Z","timestamp":1789241639086,"version":"build-2803163510"},"reference-count":59,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Telemedicine and e-Health"],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Telehealth use surged during the COVID-19 pandemic and plateaued in recent years, yet factors influencing sustained use among patients remain underexplored. This study examines sociodemographic, health, digital literacy, and prior telemedicine experience related predictors of willingness to continue telehealth use in a postpandemic population of the United States United States.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Data from 2,423 participants in the 2024 Health Information National Trends Survey were analyzed, focusing on respondents with prior telehealth experience. Ordinal logistic regression was employed to identify predictors of willingness to continue telehealth use. Predictors included sociodemographic factors, health conditions, digital literacy, and prior telehealth experiences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Among respondents, 59.1% (n = 1,432) were very willing and 30.99% (n = 751) somewhat willing to continue telehealth use. Significant predictors included age (35\u201349: OR = 2.12, 95% CI: 1.14\u20133.96,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      = 0.019; 50\u201364: OR = 2.26, 95% CI: 1.06\u20134.85,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      = 0.036; 65\u201374: OR = 2.87, 95% CI: 1.08\u20137.65,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      = 0.035), digital literacy (health information search skills: OR = 1.67, 95% CI: 1.19\u20132.34,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      = 0.004), convenience (OR = 3.12, 95% CI: 2.04\u20134.77,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      &lt; 0.001), perceived telecare quality (OR = 1.79, 95% CI: 1.33\u20132.42,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      &lt; 0.001), and recommendation likelihood (OR = 3.67, 95% CI: 2.44\u20135.50,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      &lt; 0.001). Lower willingness was observed among males (OR = 0.57, 95% CI: 0.35\u20130.92,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      = 0.023), Non-Hispanic blacks (OR = 0.40, 95% CI: 0.20\u20130.80,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      = 0.010), audio-only visits (OR = 0.64, 95% CI: 0.40\u20130.89,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      = 0.05) and those used telehealth for rehabilitation\/therapy purposes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>This study uniquely highlights strong postpandemic telehealth willingness among U.S. adults, driven by age, digital literacy, and positive experiences, with novel insights into racial and gender disparities and the unsuitability of telehealth for rehabilitation. These findings have important implications for health policy makers and administrators to design targeted interventions to enhance digital access and video-based care quality to sustain telehealth use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15305627251399715","type":"journal-article","created":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T02:49:23Z","timestamp":1763779763000},"page":"305-314","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Who Stays Digital? Patient-Level Predictors of Sustained Telehealth Use"],"prefix":"10.1177","volume":"32","author":[{"given":"Ranganathan","family":"Chandrasekaran","sequence":"first","affiliation":[{"name":"Department of Information &amp; Decision Sciences and Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, Illinois, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-01845-2"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamahealthforum.2023.5142"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10597-021-00861-2"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.2196\/38600"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1279-4"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2017.0058"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2025.0089"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-025-00757-2"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1056\/nejmp2210790"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2024.0604"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2020.0570"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2021.0434"},{"key":"e_1_3_3_14_2","unstructured":"Anonymous. Monthly Telehealth Regional Tracker. n.d. Available from: https:\/\/www.fairhealth.org\/fh-trackers\/telehealth [Last accessed: July2 2025]."},{"key":"e_1_3_3_15_2","unstructured":"Percent Change. Percent of Medical Claim Lines Top Five Diagnostic Categories. n.d. Available from: https:\/\/s3.amazonaws.com\/media2.fairhealth.org\/infographic\/telehealth\/Telehealth-Trending-Report-July-Dec-2024-National.pdf [Last accessed: July3 2025]."},{"key":"e_1_3_3_16_2","unstructured":"Bestsennyy O Gilbert G Harris A et al. Telehealth: A Quarter-Trillion-Dollar Post-COVID-19 Reality? McKinsey & Company; 2020. Available from: https:\/\/www.mckinsey.com\/industries\/healthcare\/our-insights\/telehealth-a-quarter-trillion-dollar-post-covid-19-reality. 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