{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T11:23:47Z","timestamp":1791890627651,"version":"4.3.4"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>To evaluate the real-world effectiveness of an artificial intelligence (AI) and big data-driven personalized chronic disease management model for type 2 diabetes mellitus (T2DM) patients, compared to conventional nurse-led management, and to identify factors associated with successful glycemic control within the personalized model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A retrospective cohort study was conducted involving 280 T2DM patients discharged from a single hospital between January 2019 and December 2024. Patients were divided into a conventional management group (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f100) and a personalized management group (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f180). The personalized group utilized a model integrating gradient boosting (XGBoost) for risk prediction and rule-based reasoning with reinforcement learning to dynamically generate individualized dietary, exercise, and blood glucose monitoring plans via a mobile application (APP). Both groups received 6\u202fmonths of follow-up. Glycemic control [fasting blood glucose (FBG), 2-h postprandial glucose (2hPG), glycated hemoglobin (HbA1c)], self-care activities [Summary of Diabetes Self-Care Activities (SDSCA) scale], and quality of life [Diabetes-Specific Quality of Life (DSQL) scale] were assessed at baseline and 6\u202fmonths. Within the personalized group, patients were further categorized into well-controlled (HbA1c\u202f\u2264\u202f6.5%,\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f98) and poorly-controlled (HbA1c\u202f&amp;gt;\u202f6.5%,\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f82) subgroups for case\u2013control analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      At 6\u202fmonths, the personalized management group demonstrated significantly better glycemic control (FBG: 6.79\u202f\u00b1\u202f0.72 vs. 7.03\u202f\u00b1\u202f0.89\u202fmmol\/L,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.022; 2hPG: 6.27\u202f\u00b1\u202f1.18 vs. 6.62\u202f\u00b1\u202f1.16\u202fmmol\/L,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.018; HbA1c: 6.48\u202f\u00b1\u202f0.53% vs. 6.63\u202f\u00b1\u202f0.46%,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.018), superior self-care scores across all SDSCA domains (all\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05, largest improvement in special diet:\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.001), and significantly higher quality of life (all DSQL dimensions\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05) compared to the conventional group. Within the personalized group, multivariate analysis identified alcohol consumption [odds ratio (OR)\u202f=\u202f3.576,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001], low baseline high-density lipoprotein cholesterol (HDL-C) (OR\u202f=\u202f0.102,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.007), and reduced blood glucose monitoring adherence (OR\u202f=\u202f0.958,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001) as independent risk factors for poor control, while higher exercise plan completion was protective (OR\u202f=\u202f0.976,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.037).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The AI and big data-driven personalized management model significantly improved glycemic control, self-care behaviors, and quality of life in T2DM patients over conventional care within 6\u202fmonths. Success within the model is influenced by behavioral and biological factors, alongside alcohol consumption. This approach demonstrates promise for enhancing diabetes care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1735295","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T06:40:02Z","timestamp":1769668802000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Application study of an artificial intelligence and big data-based personalized chronic disease management model for diabetes patients"],"prefix":"10.3389","volume":"14","author":[{"given":"Mei","family":"Xin","sequence":"first","affiliation":[{"name":"Department of Health Management, The First Affiliated Hospital of Guangxi Medical University","place":["Nanning, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanbing","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Elderly Endocrinology, The First Affiliated Hospital of Guangxi Medical University","place":["Nanning, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Health Management, The First Affiliated Hospital of Guangxi Medical University","place":["Nanning, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuxia","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Health Management, The First Affiliated Hospital of Guangxi Medical University","place":["Nanning, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"109119","DOI":"10.1016\/j.diabres.2021.109119","article-title":"IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 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