{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T04:41:24Z","timestamp":1792384884618,"version":"4.4.0"},"reference-count":30,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Real\u2010world glucagon\u2010like peptide\u20101 receptor agonist (GLP\u20101 RA) therapies face substantial attrition rates in commercial digital weight loss services (DWLSs). Conversational artificial intelligence (AI) has been proposed to enhance patient support at production scale, but robust evidence of its effectiveness in improving medication retention is scarce.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the effect of integrating an asynchronous AI patient support agent (Junebot) into a commercial DWLS on 6\u2010month medication adherence and to assess the independent relationship of digital engagement on retention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective analysis evaluated 16\u2009556 adults prescribed semaglutide within an Australian DWLS between May 2024 and October 2025. The primary endpoint was 6\u2010month medication adherence (\u2265\u20096 orders fulfilled within 183\u2009days). Analytical protocols featured a multivariate binary logistic regression on the full intention\u2010to\u2010treat cohort and three distinct 1:1 propensity score matching (PSM) sensitivity analyses to isolate era\u2010based and tier\u2010specific engagement effects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Six\u2010month adherence was higher post\u2010Junebot than in the pre\u2010Junebot control (53.2% vs. 47.3%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). However, the full cohort model (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.2236) revealed that the post\u2010Junebot operational era was independently associated with an increase in the odds of attrition (OR: 1.178; 95% CI [1.052\u20131.318];\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.004), a trend corroborated by the era\u2010matched PSM model (OR of attrition: 1.309;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.038). Non\u2010automation factors dominated retention trajectories: high\u2010intensity tracking during month 1 (&gt;\u200925 tracks) dramatically predicted attrition (OR: 15.753;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), alongside program pauses (OR: 2.508;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and higher program cost (OR: 2.458;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Within\u2010cohort analysis demonstrated a profound, linear curve for active interaction; structured digital engagement spanning 50%\u201374.99% of weeks significantly optimised adherence odds across both medication\u2010only (OR: 32.016) and medication plus health coaching cohorts (OR: 13.311).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Integrating an AI digital assistant did not independently improve medication adherence; programmatic costs, program pauses and initial tracking anxiety were stronger predictors of long\u2010term retention. After matching post\u2010Junebot cohorts, moderate, active digital engagement appeared to correlate with 6\u2010month retention. Digital providers should prioritise proactive behavioural risk screening and financial accessibility over baseline AI integration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.71181","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T02:01:00Z","timestamp":1786413660000},"page":"9980-9990","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>AI<\/scp>\n                    Patient Support and 6\u2010Month Medication Adherence in a Digital Obesity Program: A Retrospective Analysis"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5390-8392","authenticated-orcid":false,"given":"Louis","family":"Talay","sequence":"first","affiliation":[{"name":"Eucalyptus Health  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Connie","family":"Xu","sequence":"additional","affiliation":[{"name":"Amigo AI  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Alderete","sequence":"additional","affiliation":[{"name":"Amigo AI  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Hom","sequence":"additional","affiliation":[{"name":"Department of Medicine Stanford University Medical Centre  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6651-4691","authenticated-orcid":false,"given":"Marilyn","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Medicine Stanford University Medical Centre  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neera","family":"Ahuja","sequence":"additional","affiliation":[{"name":"Department of Medicine Stanford University Medical Centre  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"World Health Organization \u201cObesity and Overweight. 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