From HIIT to Hormones: Evolution, Gaps, and the GLUT of Machine Learning in T1DM Glycemic Prediction: A 2010–2025 Scoping Review

From HIIT to Hormones: Evolution, Gaps, and the GLUT of Machine Learning in T1DM Glycemic Prediction: A 2010–2025 Scoping Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review From HIIT to Hormones: Evolution, Gaps, and the GLUT of Machine Learning in T1DM Glycemic Prediction: A 2010–2025 Scoping Review Shoaib Z. Khan, Michael S. Ramirez Campos, Irena A. Rebalka, Michael D. Noseworthy, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9488956/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract With a focus on physical activity and physiological variables this scoping review synthesizes recent trends in machine learning for glycemic prediction in individuals with Type 1 diabetes. A structured PRISMA-ScR search (2010–2025) identified 41 studies which resulted in three dominant application areas: (1) Multi-horizon prediction of glycemia and physical activity detection, driven mainly by recurrent neural networks (RNN)-most commonly long short-term memory (LSTM)-with evidence that incorporating energy expenditure improves model performance; (2) prediction of exercise-induced dysglycemia and nocturnal hypoglycemia, which share overlapping temporal horizons, indicating potential for unified forecasting models; and (3) translation of prediction models into bolus-optimization strategies, though real-world validation is limited. The review identifies two critical gaps: (1) The handling of physiological drift and model decay as a result of physiological training or detraining; (2) Menstrual cycle integration and its use as a feature remains unexplored, while multiple studies have demonstrated the decrease of insulin sensitivity in the late luteal phase of the cycle. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Endocrinology Biological sciences/Physiology Machine learning Artificial intelligence Type 1 Diabetes Mellitus Physical Activity Scoping Review Blood Glucose Prediction Full Text Additional Declarations No competing interests reported. Supplementary Files SMsearch.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 May, 2026 Reviewers agreed at journal 15 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 27 Apr, 2026 Submission checks completed at journal 27 Apr, 2026 First submitted to journal 21 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9488956","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":638567752,"identity":"4a7a98ce-3f9c-4689-ba18-5336531f9009","order_by":0,"name":"Shoaib Z. 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