{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T22:16:56Z","timestamp":1793053016863,"version":"4.5.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"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. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Digital health applications and AI-supported wearables may benefit people with Multiple Sclerosis (MS), yet fluctuating cognitive and physical symptoms could shape adoption in ways not fully captured by traditional acceptance models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify determinants of digital health acceptance in MS, focusing on emotional factors and disease-related moderators, and to compare these patterns with individuals living with other chronic conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      An online survey (Winter 2024\/2025) assessed Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) constructs in MS patients (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200964) and a comparison group (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200914). Measures included Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioral Intention (BI), Social Influence (SI), Trust in Technology (TT), Technological Anxiety (TA), and self-reported wearable\/app use.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Groups did not differ significantly in PU, PEOU, BI, or SI (all\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;gt;\u2009.05), though between-group comparisons should be interpreted cautiously given the small comparison group size (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200914). However, MS participants reported substantially lower regular wearable use [\n                      <jats:italic>\u03c7<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      (2)\u2009=\u20097.83,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.020]. TT (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u2009=\u2009.52,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u2009.001) and TA (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u2009=\u2009\u2013.38,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u2009.001) were the strongest predictors of BI, whereas PU and PEOU contributed minimally. Symptom severity moderated acceptance pathways, weakening PEOU effects and amplifying TA effects.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Findings reveal an intention\u2013behavior gap in MS and show that emotional and capability-related factors outweigh cognitive predictors. We outline foundational elements of an Extended Disease-Specific Technology Acceptance Model for MS integrating trust, anxiety, and symptom burden. Digital health tools for MS should prioritize trust-building and anxiety-reducing design features.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1763329","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:47:05Z","timestamp":1773384425000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Trust and anxiety as primary drivers of digital health acceptance in multiple sclerosis: toward an extended disease-specific technology acceptance model"],"prefix":"10.3389","volume":"8","author":[{"given":"Felix","family":"H\u00f6pfl","sequence":"first","affiliation":[{"name":"Faculty of Applied Health and Social Sciences, Rosenheim Technical University of Applied Sciences","place":["Rosenheim, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mira","family":"Brundiers","sequence":"additional","affiliation":[{"name":"Faculty of Applied Health and 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