{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T06:00:16Z","timestamp":1781244016686,"version":"3.54.1"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"\n The widespread adoption of continuous glucose monitoring (CGM) and wearable sensing technologies has enabled large-scale collection of high-resolution physiological and behavioral data in real-world settings. However, the analytical frameworks needed to translate these data into actionable, individualized insights remain limited. In particular, many existing approaches rely on population-level analysis or controlled experimental designs, which often fail to capture intra-individual variability in everyday life. To address this sensing\u2013analysis gap, this study investigates within-person, meal-level predictors of postprandial glucose dynamics using multimodal data collected under free-living conditions. We analyzed outcomes including peak glucose, time to peak, and area under the glucose curve above 140\u2009mg\/dL. Predictors encompassed macronutrient composition, baseline glucose, meal timing, and short-term wrist movement variability derived from wearable sensors. Linear mixed-effects models were constructed with continuous predictors centered within individuals to explicitly capture within-person effects. Net carbohydrate intake showed the strongest association with postprandial glucose magnitude (\n \n \n p<\/mml:mi>\n <<\/mml:mo>\n 0.001<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ), whereas total fat intake was associated with delayed glucose responses (\n \n \n p<\/mml:mi>\n =<\/mml:mo>\n 0.024<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ), suggesting that glucose magnitude and response timing may reflect distinct physiological patterns. Baseline glucose was consistently associated with all outcomes (\n \n \n p<\/mml:mi>\n <<\/mml:mo>\n 0.001<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ). In addition, greater short-term wrist movement variability was linked to reduced glucose excursions, suggesting the potential relevance of behavioral context in postprandial glucose variability. These preliminary findings demonstrate the potential utility of integrating multimodal wearable and behavioral data to characterize individualized postprandial glucose responses in real-world settings.\n <\/jats:p>","DOI":"10.3389\/fdgth.2026.1847884","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T05:48:44Z","timestamp":1781243324000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Within-person modeling of postprandial glucose using multimodal wearable data"],"prefix":"10.3389","volume":"8","author":[{"given":"Zilu","family":"Liang","sequence":"first","affiliation":[{"name":"Ubiquitous and Personal Computing Laboratory, Kyoto University of Advanced Science (KUAS)","place":["Kyoto, Japan"]},{"name":"Institute of Industrial Science (IIS), The University of Tokyo","place":["Tokyo, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e65272","DOI":"10.2196\/65272","article-title":"Wearable artificial intelligence for sleep disorders: scoping review","volume":"27","author":"Aziz","year":"2025","journal-title":"J Med Internet Res"},{"key":"B2","doi-asserted-by":"publisher","first-page":"12146165","DOI":"10.3389\/fendo.2025.1571362","article-title":"Continuous glucose monitoring combined with artificial intelligence: redefining the pathway for prediabetes management","volume":"16","author":"Ji","year":"2025","journal-title":"Front Endocrinol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1432876","DOI":"10.3389\/fcvm.2024.1432876","article-title":"Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives","volume":"11","author":"Papalamprakopoulou","year":"2024","journal-title":"Front Cardiovasc Med"},{"key":"B4","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1038\/s41578-022-00460-x","article-title":"End-to-end design of wearable sensors","volume":"7","author":"Ates","year":"2022","journal-title":"Nat Rev Mater"},{"key":"B5","doi-asserted-by":"publisher","first-page":"100727","DOI":"10.1016\/j.biosx.2025.100727","article-title":"Wearable sensors for health monitoring: current applications, trends, and future directions","volume":"28","author":"Kurul","year":"2026","journal-title":"Biosens Bioelectron X"},{"key":"B6","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1038\/s41928-024-01247-4","article-title":"Hybrid multimodal wearable sensors for comprehensive health monitoring","volume":"7","author":"Mahato","year":"2024","journal-title":"Nat Electron"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1038\/s41597-025-05851-7","article-title":"CGMacros: a pilot scientific dataset for personalized nutrition and diet monitoring","volume":"12","author":"Das","year":"2025","journal-title":"Sci Data"},{"key":"B8","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.nutres.2020.06.001","article-title":"Diet-induced differences in estimated plasma glucose concentrations in healthy, non-diabetic adults are detected by continuous glucose monitoring - a randomized crossover trial","volume":"80","author":"Fechner","year":"2020","journal-title":"Nutr Res"},{"key":"B9","doi-asserted-by":"publisher","first-page":"19322968251361555","DOI":"10.1177\/19322968251361555","article-title":"Continuous glucose measurements for diet monitoring in healthy adults","author":"Ong","year":"2025","journal-title":"J Diabetes Sci Technol"},{"key":"B10","doi-asserted-by":"publisher","first-page":"11795514251370507","DOI":"10.1177\/11795514251370507","article-title":"Postprandial glucose: a variable in continuum","volume":"18","author":"Joshi","year":"2025","journal-title":"Clin Med Insights Endocrinol Diabetes"},{"key":"B11","doi-asserted-by":"publisher","first-page":"3111","DOI":"10.2147\/DMSO.S458894","article-title":"Manipulation of post-prandial hyperglycaemia in type 2 diabetes: an update for practitioners","volume":"17","author":"Shibib","year":"2024","journal-title":"Diabetes Metab Syndr Obes Targets Ther"},{"key":"B12","doi-asserted-by":"publisher","first-page":"161","DOI":"10.2337\/cd24-0099","article-title":"Postprandial plasma glucose","volume":"43","author":"Aung","year":"2024","journal-title":"Clin Diabetes"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1210\/clinem\/dgae632","article-title":"1-hour postload glucose: early screening for high risk of type 2 diabetes in Koreans with normal fasting glucose","volume":"110","author":"Lee","year":"2025","journal-title":"J Clin Endocrinol Metab"},{"key":"B14","doi-asserted-by":"publisher","first-page":"3571","DOI":"10.3390\/nu15163571","article-title":"Individual postprandial glycemic responses to meal types by different carbohydrate levels and their associations with glycemic variability using continuous glucose monitoring","volume":"15","author":"Song","year":"2023","journal-title":"Nutrients"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e2533193","DOI":"10.1001\/jamanetworkopen.2025.33193","article-title":"Age, sex, BMI, meal timing, and glycemic response to meal glycemic load","volume":"8","author":"Calvo-Malvar","year":"2025","journal-title":"JAMA Netw Open"},{"key":"B16","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/s12986-019-0368-1","article-title":"Postprandial glycemic response in a non-diabetic adult population: the effect of nutrients is different between men and women","volume":"16","author":"Gonz\u00e1lez-Rodr\u00edguez","year":"2019","journal-title":"Nutr Metab"},{"key":"B17","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41746-021-00465-w","article-title":"Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches","volume":"4","author":"Bent","year":"2021","journal-title":"NPJ Digit Med"},{"key":"B18","author":"Cho","year":""},{"key":"B19","doi-asserted-by":"publisher","first-page":"S132","DOI":"10.2337\/dc26-S006","article-title":"6. glycemic goals, hypoglycemia, and hyperglycemic crises: standards of care in diabetes\u20142026","volume":"49","year":"2025","journal-title":"Diabetes Care"},{"key":"B20","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/1475-2891-5-22","article-title":"Acute effect of meal glycemic index and glycemic load on blood glucose and insulin responses in humans","volume":"5","author":"Galgani","year":"2006","journal-title":"Nutr J"},{"key":"B21","doi-asserted-by":"publisher","first-page":"2798","DOI":"10.1093\/jn\/126.11.2798","article-title":"Source and amount of carbohydrate affect postprandial glucose and insulin in normal subjects","volume":"126","author":"Wolever","year":"1996","journal-title":"J Nutr"},{"key":"B22","doi-asserted-by":"publisher","first-page":"e054370","DOI":"10.1136\/bmj-2020-054370","article-title":"Fibre intake for optimal health: how can healthcare professionals support people to reach dietary recommendations?","volume":"378","author":"McKeown","year":"2022","journal-title":"BMJ"},{"key":"B23","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1159\/000499374","article-title":"Revised reference values for the intake of protein","volume":"74","author":"Richter","year":"2019","journal-title":"Ann Nutr Metab"},{"key":"B24","doi-asserted-by":"publisher","first-page":"172","DOI":"10.2337\/diab.40.2.S172","article-title":"Percentage of carbohydrate and glycemic response to breakfast, lunch, and dinner in women with gestational diabetes","volume":"40","author":"Peterson","year":"1991","journal-title":"Diabetes"},{"key":"B25","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1079\/BJN19890116","article-title":"The effect of incorporating fat into different components of a meal on gastric emptying and postprandial blood glucose and insulin responses","volume":"61","author":"Cunningham","year":"1989","journal-title":"Br J Nutr"},{"key":"B26","doi-asserted-by":"publisher","first-page":"6585","DOI":"10.1038\/s41467-024-50764-5","article-title":"Personalized insulin dosing using reinforcement learning for high-fat meals and aerobic exercises in type 1 diabetes: a proof-of-concept trial","volume":"15","author":"Jafar","year":"2024","journal-title":"Nat Commun"},{"key":"B27","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s00394-016-1313-y","article-title":"Dietary fat and carbohydrate quality have independent effects on postprandial glucose and lipid responses","volume":"57","author":"Sun","year":"2018","journal-title":"Eur J Nutr"},{"key":"B28","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1177\/02601060221112805","article-title":"Effects of different diets on glycemic control among patients with type 2 diabetes: a literature review","volume":"29","author":"Al-Adwi","year":"2023","journal-title":"Nutr Health"},{"key":"B29","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1093\/ajcn\/34.3.328","article-title":"Effect of test meals of varying dietary fiber content on plasma insulin and glucose response","volume":"34","author":"Potter","year":"1981","journal-title":"Am J Clin Nutr"},{"key":"B30","doi-asserted-by":"publisher","first-page":"2305","DOI":"10.3390\/nu11102305","article-title":"Determination of postprandial glycemic responses by continuous glucose monitoring in a real-world setting","volume":"11","author":"R\u00f6hling","year":"2019","journal-title":"Nutrients"},{"key":"B31","author":"Barth","year":""},{"key":"B32","author":"Liang","year":""},{"key":"B33","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1007\/s00779-016-0960-6","article-title":"Sleepexplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors","volume":"20","author":"Liang","year":"2016","journal-title":"Person Ubiquitous Comput"},{"key":"B34","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/S2213-8587(22)00319-9","article-title":"Continuous glucose monitoring and metrics for clinical trials: an international consensus statement","volume":"11","author":"Battelino","year":"2023","journal-title":"Lancet Diabetes Endocrinol"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.2337\/dci19-0028","article-title":"Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range","volume":"42","author":"Battelino","year":"2019","journal-title":"Diabetes Care"},{"key":"B36","doi-asserted-by":"publisher","first-page":"1026830","DOI":"10.3389\/fmedt.2022.1026830","article-title":"Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults","volume":"4","author":"Liang","year":"2022","journal-title":"Front Med Technol"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1847884\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T05:48:45Z","timestamp":1781243325000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1847884\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,12]]},"references-count":36,"alternative-id":["10.3389\/fdgth.2026.1847884"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2026.1847884","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,12]]},"article-number":"1847884"}}