# Leveraging Big Data and Deep Learning to Develop Next Generation Decision Support Tools to Improve Glycemic Outcomes in Type 1 Diabetes > **NIH NIH F30** · OREGON HEALTH & SCIENCE UNIVERSITY · 2024 · $53,974 ## Abstract Project Summary The hallmark of type 1 diabetes (T1D) is insufficient insulin production caused by pancreatic beta cell dysfunction. Most people treat their T1D through multiple daily injections (MDI) of insulin or use of a transcutaneous insulin pump. Several decision support smartphone apps exist to help people estimate insulin doses based on continuous glucose monitor (CGM) data and food intake. More sophisticated decision support tools employ mathematical models of human physiology to predict future glucose levels and provide generalized insulin therapy recommendations. Exercise is a crucial component of the long-term management of T1D, however many people avoid physical activity for fear of hypoglycemia (< 70 mg/dL). While consensus guidelines exist to help people manage glucose during physical activity, people still experience acute complications. Mathematical models of aerobic exercise yield promise in predicting hypoglycemia during controlled in- clinic experiments but do not perform well in the real-world or during other types of exercise. There is a critical need for a decision support system that helps people with T1D maintain safe glucose levels around exercise of varying types. The goal of this proposal is to develop a decision support tool to help people with T1D who utilize CGM better manage their glucose surrounding exercise. This tool will be called AIDES, the Artificially Intelligent Diabetic Exercise Support system. We hypothesize that use of a novel exercise- specific decision support tool, powered by predictive physiological modelling, artificial intelligence (AI), and deep learning, can provide treatment recommendations to reduce the number of hypoglycemic events experienced by people with T1D around regular physical exercise. In our first aim, we will develop a new model of resistance exercise that describes both insulin- and non-insulin mediated effects on glucose dynamics. We will then create a novel hybrid computational framework that harnesses AI to augment physiology models of aerobic and resistance exercise. This hybrid framework, called physAI, will harness real- world, free-living exercise data from the T1Dexi project (Big Data). In our second aim, we will leverage decades of research into deep learning with the Big Data provided by the T1Dexi project to train an AI-based decision support system that gives treatment recommendations to help users maintain target glucose during exercise. In our third aim, we will assess the safety and usability of our decision support engine in a small proof-of-concept study with human participants, supported by the Sponsor. This will be the first decision support system specifically designed to provide treatment recommendations that help users maintain safe glucose levels while performing aerobic and resistance exercise. ## Key facts - **NIH application ID:** 10838459 - **Project number:** 5F30DK128914-04 - **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY - **Principal Investigator:** Gavin Young - **Activity code:** F30 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $53,974 - **Award type:** 5 - **Project period:** 2021-05-01 → 2025-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10838459 ## Citation > US National Institutes of Health, RePORTER application 10838459, Leveraging Big Data and Deep Learning to Develop Next Generation Decision Support Tools to Improve Glycemic Outcomes in Type 1 Diabetes (5F30DK128914-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10838459. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*