{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T18:07:20Z","timestamp":1788890840573,"version":"build-2803163510"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":148,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Lirio, Inc."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In the United States, underserved women face persistent healthcare disparities, making mammography screening and equity public health priorities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This retrospective observational study assessed how tailored messaging influenced mammogram scheduling and attendance, determined which message types were most effective, and explored differences across key demographic subgroups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We employed Precision Nudging using reinforcement learning and behavioral science to tailor email messages informed by the Capability, Opportunity\u2014Behavior (COM-B) model. Messages were sent to 156\u00a0954 women overdue for screening. Mammogram scheduling and attendance were documented via electronic health records. Analyses focused on the engaged patient population who opened at least one email (N\u2009=\u2009106\u00a0616; 67.9%). Logistic regressions examined whether COM-B content in the first and last message predicted scheduling and attendance, testing interactions for age, race, income, insurance, and education.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among engaged patients, 23.7% scheduled and 20.3% attended a mammogram. Capability-focused messaging was generally associated with higher overall uptake, while opportunity messaging consistently predicted lower scheduling and attendance rates. Equity analyses revealed nuanced engagement patterns among underserved groups. Underrepresented racial\/ethnic minority women had higher odds of completing screening than White women. Older age predicted lower attendance, with early messages addressing motivation predicting lower attendance than capability messages. Final intervention messages addressing motivation were more effective for low- and high-income women than middle-income women and more effective for women without private insurance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings suggest that tailoring messages to patients\u2019 needs and context encourages mammogram uptake and may help reduce disparities in women\u2019s health.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/abm\/kaag024","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T11:26:17Z","timestamp":1777980377000},"source":"Crossref","is-referenced-by-count":0,"title":["Retrospective observational study applying reinforcement learning and behavioral science to mammography: insights for equitable preventive care"],"prefix":"10.1093","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5205-943X","authenticated-orcid":false,"given":"Emily","family":"Frith","sequence":"first","affiliation":[{"name":"Lirio, Inc. Behavioral Reinforcement Learning Lab (BReLL), , 320 Corporate Drive, NW , Knoxville, TN 37923,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4145-7279","authenticated-orcid":false,"given":"Sarah","family":"Deedat","sequence":"additional","affiliation":[{"name":"Lirio, Inc. 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