{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:59:38Z","timestamp":1776844778716,"version":"3.51.2"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA239246"],"award-info":[{"award-number":["R01CA239246"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>\n                    Many breast cancer survivors are prescribed daily oral medications called endocrine therapy that prevent cancer recurrence. Despite its clinical importance, maintaining consistent daily adherence remains challenging due to the dynamic and interrelated influences of behavioral, physiological, and psychological factors. While prior studies have explored adherence prediction using mobile sensing, they often rely on single-modality data, limited temporal granularity, or aggregate-level modeling\u2014limiting their ability to capture short and long-term behavioral variability and to facilitate deeper understanding of non-adherence and tailored interventions. To address these gaps, we propose a multimodal sensing framework that explicitly models daily adherence dynamics using temporally adaptive inputs. We recruited a sample of breast cancer survivors (\n                    <jats:italic toggle=\"yes\">N =<\/jats:italic>\n                    20) and collected longitudinal data streams including wearable-derived physiological features (Fitbit), medication event monitoring system (MEMS) data, and ecological momentary assessments (EMAs). Using multimodal data across varying time windows, we examined whether recent patterns in behavioral, physiological, psychological, and environmental factors improve the prediction of next-day endocrine therapy adherence. Our results demonstrate the feasibility of using multimodal sensing data to predict daily adherence with moderate accuracy. Moreover, models integrating multimodal data consistently outperformed those relying on a single modality. Importantly, we observed that the predictive value of each modality varied depending on the temporal proximity of the input signals, underscoring the importance of modeling immediate and longer-term behavioral patterns. The findings offer valuable insights for advancing adherence monitoring systems, suggesting that incorporating personalized and temporally adaptive data fusion strategies may significantly enhance the effectiveness of intervention design and delivery.\n                  <\/jats:p>","DOI":"10.1145\/3770864","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T19:42:32Z","timestamp":1764704552000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Multimodal Sensing and Modeling of Endocrine Therapy Adherence in Breast Cancer Survivors"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1203-1850","authenticated-orcid":false,"given":"Fangxu","family":"Yuan","sequence":"first","affiliation":[{"name":"Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5782-0655","authenticated-orcid":false,"given":"Navreet","family":"Kaur","sequence":"additional","affiliation":[{"name":"Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1611-2053","authenticated-orcid":false,"given":"Zhiyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4650-5157","authenticated-orcid":false,"given":"Manuel","family":"Gonzales IV","sequence":"additional","affiliation":[{"name":"SDSU\/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1531-9028","authenticated-orcid":false,"given":"Cristian","family":"Garcia Alcaraz","sequence":"additional","affiliation":[{"name":"SDSU\/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8404-6733","authenticated-orcid":false,"given":"Gabriel","family":"Estrella","sequence":"additional","affiliation":[{"name":"Department of Educational Psychology, San Diego State University, San Diego, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7359-8678","authenticated-orcid":false,"given":"Kristen","family":"Wells","sequence":"additional","affiliation":[{"name":"Department of Psychology, San Diego State University, San Diego, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8224-5164","authenticated-orcid":false,"given":"Laura","family":"Barnes","sequence":"additional","affiliation":[{"name":"Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCW.2018.8403547"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2020.2993993"},{"key":"e_1_2_1_3_1","volume-title":"Social cognitive theory: An agentic perspective. 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