{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:15:43Z","timestamp":1783577743550,"version":"3.55.0"},"reference-count":60,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"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<\/jats:title>\n                    <jats:p>Hypertension requires sustained self-management beyond routine clinical encounters, yet evidence on how patients engage with digital health information and what shapes their trust in AI-based physician chatbots remains limited, particularly in Saudi Arabia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This study examined digital health information-seeking behaviors, information verification practices, and perceptions of AI-based physician chatbots among adults diagnosed with hypertension in Saudi Arabia and tested a theoretically grounded model linking six constructs to chatbot safety and trust.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A cross-sectional online survey was conducted among 322 adults with hypertension recruited through WhatsApp, Telegram, and Facebook patient groups. A structured questionnaire measured six constructs Information Validation Sources (IVS), Digital Self-Diagnosis Tools (DSDT), Online Information-Seeking Behavior (OISB), Familiarity with MOH Digital Services (FMOHDS), Perceived Reliability of Self-Assessment (PRISA), and Chatbot Safety and Trust (CST) adapted from past studies. Internal consistency, descriptive statistics, confirmatory factor analysis, bootstrapping and Kruskal-Wallis group comparisons were performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      All constructs scored above the scale midpoint (\n                      <jats:italic>M<\/jats:italic>\n                      \u202f=\u202f3.32\u20133.78). WhatsApp was the dominant platform for health information seeking (43.5%), verification (51.9%), and communication with health services (41.9%). Measurement model and CFA results shows that scales are reliable and valid, factor loadings, alpha values, AVE and composite reliability and VIF values met threshold. Discriminant validity using Fornell-Larcker is also reported. Bootstrapping results have revealed that OISB has positive significant effect on CST, while PRISA has negative but significant effect on CST, remaining IVS, DSDT and FMOHDS effects on CST were not significant. Moreover, direct effects of predictors on PRISA were also investigated, findings have revealed that FMOHDS is most dominant predictor of PRISA, while OISB negatively but significantly predicted PRISA, however, IVS, DSDT effects on CST were not significant. Regarding indirect mediating effects only PRISA significantly mediates between FMOHDS and CST, while PRISA mediating effects were insignificant between IVS, DSDT OISB and CST.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Trust in AI-based physician chatbots among hypertensive patients is positively shaped by institutional familiarity with MOH digital services and perceived reliability of self-assessment tools and negatively influenced by active independent digital health engagement. The mediating effect of PRISA on FMOHDS and CST suggests that building institutional trust in existing digital infrastructure is a prerequisite for effective AI chatbot adoption. These findings have direct implications for the design of AI-enabled hypertension management strategies within Saudi Arabia\u2019s Vision 2030 digital health agenda.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1837642","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:34:45Z","timestamp":1783575285000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Digital health information-seeking behaviors and trust in AI-based physician chatbots among patients with hypertension: a cross-sectional survey in Saudi Arabia"],"prefix":"10.3389","volume":"14","author":[{"given":"Haitham","family":"Alzghaibi","sequence":"first","affiliation":[{"name":"Department of Health Informatics, College of Applied Medical Sciences, Qassim University","place":["Buraydah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasir Hayat","family":"Mughal","sequence":"additional","affiliation":[{"name":"Department of Health Informatics, College of Applied Medical Sciences, Qassim University","place":["Buraydah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khurshid","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Health Informatics, College of Applied Medical Sciences, Qassim University","place":["Buraydah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qazi Mohammad Sajid","family":"Jamal","sequence":"additional","affiliation":[{"name":"Department of Health Informatics, College of Applied Medical Sciences, Qassim University","place":["Buraydah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"ref1","volume-title":"Global Report on Hypertension: The Race Against a Silent 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