{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:50:34Z","timestamp":1782989434224,"version":"3.54.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686684","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,29]]},"abstract":"<jats:p>Continuous glucose monitoring (CGM) and insulin pump technologies generate extensive physiological and therapeutic data. Most commercial platforms display data streams separately, requiring users to cognitively integrate events that influence glycemic outcomes. This segmentation may obscure the relationships among glucose fluctuations, insulin administration, and meal patterns, potentially impairing decision-making. This project employed the UFuRT framework to create a unified visualization that consolidates glucose traces, bolus events, basal profiles, and carbohydrate intake into a single timeline. Supplementary 14-day heatmaps and summary widgets facilitate pattern recognition and interpretation of metrics such as Time in Range (TIR), Glucose Management Indicator (GMI), and glucose variability. Design decisions were guided by evidence from diabetes informatics, human factors research, and visualization science. A high-fidelity Figma prototype was developed for heuristic evaluation and cognitive walkthroughs. The potential benefits include reduced interpretation time, improved recognition of cause-and-effect relationships, and supporting safer insulin adjustment decisions.<\/jats:p>","DOI":"10.3233\/shti260796","type":"book-chapter","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:59:16Z","timestamp":1782986356000},"source":"Crossref","is-referenced-by-count":0,"title":["An Integrated Visualization of Continuous Glucose and Insulin Pump Data for Patients"],"prefix":"10.3233","author":[{"given":"Eunyoung","family":"Cho","sequence":"first","affiliation":[{"name":"McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Texas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"McGinnis","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Texas, USA"},{"name":"Cizik School of Nursing, The University of Texas Health Science Center at Houston, Texas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0864-8368","authenticated-orcid":false,"given":"Yang","family":"Gong","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Texas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Health Sciences Informatics Leads and Empowers the Digital Health Transformation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260796","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:59:16Z","timestamp":1782986356000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,29]]},"ISBN":["9781643686684"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260796","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,29]]}}}