{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:37:16Z","timestamp":1787319436961,"version":"build-2736575974"},"reference-count":38,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"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":["Diabetes Technology &amp; Therapeutics"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>\n                      Estimation of insulin sensitivity (S\n                      <jats:sub>I<\/jats:sub>\n                      ) and its daily variation are key for optimizing insulin therapy in patients with type 1 diabetes (T1D). We recently developed a method for S\n                      <jats:sub>I<\/jats:sub>\n                      estimation from continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) data in adults with T1D (S\n                      <jats:sub>I<\/jats:sub>\n                      <jats:sup>SP<\/jats:sup>\n                      ) and validated it under restrained experimental conditions. Herein, we validate in vivo a new version of S\n                      <jats:sub>I<\/jats:sub>\n                      <jats:sup>SP<\/jats:sup>\n                      performing well in daily life unrestrained conditions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      The new S\n                      <jats:sub>I<\/jats:sub>\n                      <jats:sup>SP<\/jats:sup>\n                      was tested in both simulated and real data. The simulated dataset consists of 100 virtual adults of the UVa\/Padova T1D Simulator monitored during an open-loop experiment, whereas the real dataset consists of 10 youths with T1D monitored during a hybrid closed-loop meal study. In both datasets, participants underwent two consecutive meals (breakfast and lunch, at 7 and 11 am) with the same carbohydrate content (70\u2009g). Plasma glucose and insulin were measured during each meal to estimate the oral glucose minimal model S\n                      <jats:sub>I<\/jats:sub>\n                      (S\n                      <jats:sub>I<\/jats:sub>\n                      <jats:sup>MM<\/jats:sup>\n                      ). CGM and CSII data were used for S\n                      <jats:sub>I<\/jats:sub>\n                      <jats:sup>SP<\/jats:sup>\n                      calculation, which was then validated against the gold standard S\n                      <jats:sub>I<\/jats:sub>\n                      <jats:sup>MM<\/jats:sup>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      S\n                      <jats:sub>I<\/jats:sub>\n                      <jats:sup>SP<\/jats:sup>\n                      was estimated with good precision (median coefficient of variation &lt;20%) in 100% of the real and 91% of the simulated meals. S\n                      <jats:sub>I<\/jats:sub>\n                      <jats:sup>SP<\/jats:sup>\n                      and S\n                      <jats:sub>I<\/jats:sub>\n                      <jats:sup>MM<\/jats:sup>\n                      were highly correlated, both in the simulated and real datasets (\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      \u2009=\u20090.82 and\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      \u2009=\u20090.83,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009&lt;\u20090.001), and exhibited a similar intraday pattern.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>\n                      S\n                      <jats:sub>I<\/jats:sub>\n                      <jats:sup>SP<\/jats:sup>\n                      is suitable for estimating S\n                      <jats:sub>I<\/jats:sub>\n                      in both closed- and open-loop settings, provided that the subject wears a CGM sensor and a subcutaneous insulin pump.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/dia.2022.0397","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T09:12:31Z","timestamp":1673946751000},"page":"270-278","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["A New Index of Insulin Sensitivity from Glucose Sensor and Insulin Pump Data: In Silico and In Vivo Validation in Youths with Type 1 Diabetes"],"prefix":"10.1177","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0590-2399","authenticated-orcid":false,"given":"Michele","family":"Schiavon","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padova, Padova, Italy."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfonso","family":"Galderisi","sequence":"additional","affiliation":[{"name":"Department of Woman and Child's Health, University of Padova, Padova, Italy."},{"name":"Department of Pediatrics, Yale University, New Haven, Connecticut, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ananda","family":"Basu","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, University of Virginia, Charlottesville, Virginia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0285-6622","authenticated-orcid":false,"given":"Yogish C.","family":"Kudva","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Internal Medicine, Mayo Clinic and Foundation, Rochester, Minnesota, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eda","family":"Cengiz","sequence":"additional","affiliation":[{"name":"Pediatric Diabetes Program, University of California San Francisco (UCSF) School of Medicine, San Francisco, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiara","family":"Dalla Man","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, Padova, Italy."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1152\/ajpendo.1979.237.3.E214"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1152\/ajpendo.1979.236.6.E667"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1152\/ajpendo.00319.2003"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.24.3.539"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.23.3.295"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.22.9.1462"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00280883"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.27.6.1487"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1210\/jcem.85.7.6661"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19245386"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc13-1120"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc19-1675"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2017.0248"},{"key":"e_1_3_5_15_2","doi-asserted-by":"crossref","unstructured":"Schiavon M Dalla Man C Cobelli C. Physiology-based run-to-run adaptation of insulin to carbohydrate ratio improves type1 diabetes therapy: Results from an in silico study. 2019 American Control Conference.","DOI":"10.23919\/ACC.2019.8814915"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2015-2081"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc17-1188"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1177\/1932296818757747"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.2337\/db11-0654"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2020.0027"},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.1177\/1932296816667749"},{"key":"e_1_3_5_22_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2014.0192"},{"key":"e_1_3_5_23_2","doi-asserted-by":"publisher","DOI":"10.1152\/ajpendo.00473.2004"},{"key":"e_1_3_5_24_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc20-0091"},{"key":"e_1_3_5_25_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2016.0333"},{"key":"e_1_3_5_26_2","doi-asserted-by":"publisher","DOI":"10.1177\/1932296813514502"},{"key":"e_1_3_5_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2192930"},{"key":"e_1_3_5_28_2","doi-asserted-by":"publisher","DOI":"10.1152\/ajpendo.90842.2008"},{"key":"e_1_3_5_29_2","doi-asserted-by":"publisher","DOI":"10.1177\/1932296814559746"},{"key":"e_1_3_5_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.883792"},{"key":"e_1_3_5_31_2","doi-asserted-by":"crossref","unstructured":"Cobelli C Carson ER. 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