{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T13:45:15Z","timestamp":1791639915090,"version":"4.3.4"},"reference-count":22,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T00:00:00Z","timestamp":1638489600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000041","name":"American Diabetes Association","doi-asserted-by":"publisher","award":["1-19-PMF-014"],"award-info":[{"award-number":["1-19-PMF-014"]}],"id":[{"id":"10.13039\/100000041","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1UC4 DK108483-01"],"award-info":[{"award-number":["1UC4 DK108483-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008457","name":"University of Virginia","doi-asserted-by":"publisher","award":["Strategic Investment Fund"],"award-info":[{"award-number":["Strategic Investment Fund"]}],"id":[{"id":"10.13039\/100008457","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction:<\/jats:title>\n                    <jats:p>Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and\/or anticipate eating behaviors to improve postprandial full closed-loop control.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and\/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient\u2019s total daily insulin (TDI) modulated by the disturbance\u2019s likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Postprandial time in range was highest for MS-MPC + BPS: 60.73 \u00b1 25.39%, but improved with each module: MPC + BPS: 56.95\u00b125.83 and MS-MPC: 54.83 \u00b1 26.00%, compared with MPC: 51.79 \u00b1 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg\/dL &lt;0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 \u00b1 25.32%, MPC + BPS: 42.99 \u00b1 25.81%, MS-MPC: 45.09 \u00b1 25.96%, MPC: 48.18 \u00b1 26.09%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19322968211059159","type":"journal-article","created":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T23:13:20Z","timestamp":1638573200000},"page":"52-60","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":18,"title":["Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System"],"prefix":"10.1177","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5674-3696","authenticated-orcid":false,"given":"John P.","family":"Corbett","sequence":"first","affiliation":[{"name":"Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9970-2162","authenticated-orcid":false,"given":"Jose","family":"Garcia-Tirado","sequence":"additional","affiliation":[{"name":"Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9074-8634","authenticated-orcid":false,"given":"Patricio","family":"Colmegna","sequence":"additional","affiliation":[{"name":"Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jenny L.","family":"Diaz Castaneda","sequence":"additional","affiliation":[{"name":"Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7645-2693","authenticated-orcid":false,"given":"Marc D.","family":"Breton","sequence":"additional","affiliation":[{"name":"Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,12,3]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/dom.13052."},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2020.12.652."},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2013.0231."},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2017.0424."},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc19-0895."},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc20-2106."},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1111\/dom.14335."},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcjd.2021.02.002."},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2019.05.017."},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.23919\/acc.2019.8814728."},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1177\/1932296819879084."},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2020.0516."},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1177\/1932296821997917."},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1177\/193229680900300319."},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1950.tb00463.x."},{"key":"e_1_3_3_17_2","doi-asserted-by":"crossref","unstructured":"Lloyd SP. 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