# Design and Evaluation of a Decision Support Engine for Advanced Treatment of Type 1 Diabetes

> **NIH NIH F31** · OREGON HEALTH & SCIENCE UNIVERSITY · 2020 · $50,520

## Abstract

Project Summary
Type 1 diabetes (T1D) is a disease characterized by pancreatic beta destruction with subsequent insulin depletion. The
alterations in glucose dynamics are incredibly difficult to manage and are confounded by meals, exercise, menstruation,
and stress. Although automated insulin delivery systems are becoming commercially available, the large majority of
people with T1D are treated with multiple daily injections of insulin (MDI). Dangerous complications of hypoglycemia
and diabetic ketoacidosis can occur from failure to dose insulin correctly, however vigilant adherence to tedious insulin
dosing strategies are difficult for MDI users maintain. This difficulty is magnified during exercise, which is critical to
ameliorating long-term complications of diabetes; even when guidelines for insulin dosage adjustments are followed,
acute hypoglycemia during exercise and night-time hypoglycemia can occur. In our recent survey of 1400 subjects living
with T1D, the majority of subjects on MDI therapy were not confident in managing their glucose during exercise and felt
they lacked tools to do so. In aggregate, difficult treatment schedules and bolus calculations, associated acute
complications from daily activities, and the emotional and psychological toll of this chronic disease can result in treatment
non-adherence and poor glycemic outcomes. Therefore, there is a critical need for decision support tools designed for
MDI users to improve glycemic control surrounding meals, daily activities and exercise. The goal of this proposal to
develop a decision support tool for patients with type 1 diabetes who utilize continuous glucose monitoring systems and
multiple daily injection therapy. This tool will be called miTREAT, the multiple injection treatment recommender system
for exercise-aware therapies. We hypothesize that use of a novel decision support tool equipped with content-based
collaborative filtering methods and dynamic exercise hypoglycemia prediction algorithms will improve overall
euglycemia and reduce time spent in hypoglycemia for patients on MDI therapy. In our first aim, we will leverage
decades of research in computer science recommender systems and machine learning optimization strategies to develop a
novel decision support system that identifies issues in glycemic control and recommends appropriate insulin dose and
behavioral modifications. In our second aim, we will develop a new exercise model that reflects both the dynamics of
rapid-uptake of glucose through GLUT-4 channels and the longitudinal biphasic insulin sensitivity profile. This new
model structure will be used to predict hypoglycemia during and after the exercise period. In our third aim, we will
explore the performance of our decision support engine in an in-vivo clinical trial. This clinical trial will assess the
usability of a new smart-phone app designed to assist MDI users that we have developed at OHSU. In achieving these
goals, we will develop the first decision...

## Key facts

- **NIH application ID:** 9898150
- **Project number:** 5F31DK121436-02
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Nichole Sahar Tyler
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $50,520
- **Award type:** 5
- **Project period:** 2019-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9898150

## Citation

> US National Institutes of Health, RePORTER application 9898150, Design and Evaluation of a Decision Support Engine for Advanced Treatment of Type 1 Diabetes (5F31DK121436-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9898150. Licensed CC0.

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