{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T13:21:41Z","timestamp":1787923301877,"version":"build-2784847793"},"reference-count":23,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Aut\u00f3noma del Per\u00fa","award":["PI 146-26"],"award-info":[{"award-number":["PI 146-26"]}]},{"award":["PI 146-26"],"award-info":[{"award-number":["PI 146-26"]}],"id":[{"id":"https:\/\/ror.org\/05dxn4584","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Non-invasive continuous glucose monitoring remains an important technological challenge in diabetes management. This study was revised as a literature-grounded simulation and methodological proof-of-concept rather than an experimental clinical validation. The analysis used 320 independent synthetic reference\u2013NIR glucose pairs generated from physiological ranges and performance distributions reported in the cited literature, together with an illustrative 576-point, 48-h synthetic time series. Three sensor archetypes were informed by published optical systems. Descriptive agreement was evaluated using mean absolute relative difference (MARD), Pearson correlation, Bland\u2013Altman analysis, and the supplied Clarke Error Grid (CEG) categories. Candidate physiological and environmental factors were examined using Pearson correlations with 95% confidence intervals, raw p-values, and Holm correction. The synthetic dataset produced an overall MARD of 10.38% (SD = 7.67%), r = 0.958 (R2 = 0.918, p &lt; 0.001), a Bland\u2013Altman bias of 4.32 mg\/dL, and 89.4%\/10.3%\/0.3% of records in CEG zones A\/B\/D, respectively. All candidate-factor correlations were small and non-significant after multiplicity correction; the largest was hematocrit (r = 0.090, 95% CI \u22120.019 to 0.198; Holm-adjusted p = 0.852). Differences in MARD among sensor archetypes were also non-significant (ANOVA p = 0.369; Kruskal\u2013Wallis p = 0.543). Adaptive calibration is therefore presented as a literature-informed framework for future prospective validation, not as a performance improvement demonstrated by the present synthetic dataset. No FDA or ISO compliance or clinical applicability is claimed.<\/jats:p>","DOI":"10.3390\/s26175443","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T12:27:46Z","timestamp":1787920066000},"page":"5443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Literature-Grounded Simulation of Non-Invasive Glucose Monitoring Using NIR Wearable Sensor Archetypes: Accuracy Metrics, Candidate Confounders, and an Adaptive Calibration Framework"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0031-4275","authenticated-orcid":false,"given":"David Alberto","family":"Garc\u00eda-Arango","sequence":"first","affiliation":[{"name":"Research Directorate, Universidad Aut\u00f3noma del Per\u00fa, Villa EL Salvador, Lima 15042, Peru"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Jos\u00e9 Alexander Vel\u00e1squez","family":"Ochoa","sequence":"additional","affiliation":[{"name":"Research Departament, Tecnol\u00f3gico de Antioquia, Medell\u00edn 050036, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Luis Fernando Garc\u00e9s","family":"Giraldo","sequence":"additional","affiliation":[{"name":"Posgraduate School, Universidad Continental, Av. Alfredo Mendiola 5210, Lima 15306, Peru"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4390-5646","authenticated-orcid":false,"given":"Natalia Isabel Jaramillo","family":"G\u00f3mez","sequence":"additional","affiliation":[{"name":"Research Departament, Tecnol\u00f3gico de Antioquia, Medell\u00edn 050036, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,28]]},"reference":[{"key":"ref_1","first-page":"2098","article-title":"Non-invasive sensing techniques for glucose detection: A review","volume":"11","author":"Matar","year":"2022","journal-title":"Bull. 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