{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T09:07:47Z","timestamp":1790845667354,"version":"4.1.0"},"reference-count":20,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Automated insulin delivery (AID) algorithms can benefit from tuning of their aggressiveness to meet individual needs, as insulin requirements vary among and within users. We introduce the Performance-Based Adaptation Index (PAI), a tool designed to enable automatic adjustment of an AID system aggressiveness based on continuous glucose monitoring (CGM) metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      PAI integrates two CGM-based metrics\u2014one for hypoglycemia and another for hyperglycemia exposure\u2014over a previous time window into a single index (\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                          <mml:mrow>\n                            <mml:msub>\n                              <mml:mi>\u03b1<\/mml:mi>\n                              <mml:mi>\u03b8<\/mml:mi>\n                            <\/mml:msub>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      ). We propose two methods to compute\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                          <mml:mrow>\n                            <mml:msub>\n                              <mml:mi>\u03b1<\/mml:mi>\n                              <mml:mi>\u03b8<\/mml:mi>\n                            <\/mml:msub>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      : one based on time in range (TIR, 70-180 mg\/dL), and the other on glycemic risk indices. Using\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                          <mml:mrow>\n                            <mml:msub>\n                              <mml:mi>\u03b1<\/mml:mi>\n                              <mml:mi>\u03b8<\/mml:mi>\n                            <\/mml:msub>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      , we developed a multiplicative strategy to adjust the AID system\u2019s aggressiveness, accounting for situations where\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                          <mml:mrow>\n                            <mml:msub>\n                              <mml:mi>\u03b1<\/mml:mi>\n                              <mml:mi>\u03b8<\/mml:mi>\n                            <\/mml:msub>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      cannot be reliably calculated. The feasibility of this method was assessed in-silico using the UVA\/Padova Type 1 Diabetes Simulator and our full closed-loop algorithm (UVA-model predictive control (MPC)) across five scenarios: optimal tuning (baseline), conservative and aggressive tunings, and temporary and permanent changes in insulin needs. Glycemic outcomes were evaluated from the simulated glucose traces.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Negligible performance variations were observed in the baseline scenario. For the conservative scenario, adjusting\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                          <mml:mrow>\n                            <mml:msub>\n                              <mml:mi>\u03b1<\/mml:mi>\n                              <mml:mi>\u03b8<\/mml:mi>\n                            <\/mml:msub>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      improved TIR (35.1% vs 71.8%) and increased total daily insulin (32.1 U vs 41.2 U). Conversely, for the aggressive scenario, it reduced hypoglycemia exposure (TBR: 2.6% vs 1.4%) and overall insulin usage (45.6 U vs 43.0 U).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>In-silico results demonstrated the safety and efficacy of using PAI to automatically tune the UVA-MPC controller, achieving TIR values above 70% under fully closed-loop conditions and across various physiological states. Clinical validation of these results is warranted.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19322968251315499","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T02:31:36Z","timestamp":1738809096000},"page":"942-953","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["A Performance-Based Adaptation Index for Automated Insulin Delivery Systems"],"prefix":"10.1177","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7863-4914","authenticated-orcid":false,"given":"Jenny L.","family":"Diaz C.","sequence":"first","affiliation":[{"name":"Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA, USA"},{"name":"Insulet Co., Acton, MA, 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, School of Medicine, University of Virginia, Charlottesville, VA, USA"},{"name":"Dexcom Inc, San Diego, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2946-0732","authenticated-orcid":false,"given":"Elliot","family":"Pryor","sequence":"additional","affiliation":[{"name":"Center for Diabetes Technology, School of Medicine, 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, School of Medicine, University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.2337\/DCI22-0018"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41745-022-00348-3"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.2337\/DC21-0172"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eprac.2022.10.001"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1260\/2040-2295.5.1.1"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2017.2652062"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1177\/1932296819881467"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2009.04.004"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2019.05.003"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2018.11.061"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.9b05979"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2866392"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.2337\/CD21-0083"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1177\/1932296819826111"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.CMPB.2021.106401"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.2337\/DC21-0932"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1177\/19322968211059159"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CDC51059.2022.9993157"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc14-0147"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.IFACOL.2023.10.274"}],"container-title":["Journal of Diabetes Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968251315499","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/19322968251315499","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968251315499","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T09:51:26Z","timestamp":1782294686000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/19322968251315499"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,5]]},"references-count":20,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1177\/19322968251315499"],"URL":"https:\/\/doi.org\/10.1177\/19322968251315499","relation":{},"ISSN":["1932-2968","1932-2968"],"issn-type":[{"value":"1932-2968","type":"print"},{"value":"1932-2968","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,5]]}}}