More than 10 million U.S. adults with type 2 diabetes (T2DM) do not meet American Diabetes Association (ADA) glycemic targets for glucose control. Healthcare costs related to diabetes were estimated at $327 billion in 2017, including $237 billion in direct medical costs. Poorly regulated dietary intake, which often results in chronic hyperglycemia and elevated HbA1c levels, contributes substantially to this cost. As such, medical nutrition therapy is an integral part of diabetes management. According to the ADA, the primary goals of nutrition therapy for adults with T2DM is to promote and support healthful eating patterns to maintain glycemic control. However, determining what to eat and following a food plan is the most challenging part of treatment for patients, particularly when access to diabetes educators and trained dietitians is limited. Patients are most often left to self-monitor food intake and post-meal glucose levels to inform future dietary decisions. However, the vast majority of people are unable to sustain a level of self-monitoring to experience such meaningful insights. While multiple diet and glucose tracking technologies have been developed for self-monitoring, none of these tools merge dietary data with glucose data to optimize personalized diabetes self-management. Mobile technology that complements traditional medical nutrition therapy and is personalized to an individualâs biological response to foods is long overdue. The time has come for a patient-friendly, mobile dietary guidance system (mDGS) for diabetes management that lessens the burden of self-monitoring while producing meaningful dietary feedback about what to eat and what to avoid with the goal of improving oneâs glycemic control. In response, we are proposing a new mobile dietary assessment approach that integrates continuous glucose monitoring (CGM) data with a mobile diet tracker to predict diet-related hyperglycemic events and provide dietary guidance to prevent these events in the future. We will accomplish this by automating the self- monitoring of post-meal glucose levels in a manner consistent with ADA glucose targets, thereby reducing the burden of self-monitoring by more than 80% and improving a userâs glycemic control. To achieve these goals, we will build off of our preliminary data by: (Aim 1) developing algorithms that use CGM data to predict post- meal hyperglycemia and (Aim 2) designing, prototyping, and testing the usability of CGM-integrated mobile dietary guidance system interfaces. Phase II will complete mDGS as a fully functional CGM-integrated dietary guidance system for the management of T2DM in partnership with CGM device companies. Post Phase II, we plan for a clinical trial to test the effectiveness of mDGS under free-living conditions. In the commercialization phase, Viocare will seek FDA approval for mDGS, target primary care practices, certified diabetes educators, Accountable Care Organizations, and payers treating people with T2DM. Viocare will start by offering mDGS licenses to its current customer base of over 100 healthcare organizations, which include Mayo Clinic, the Baylor Diabetes Prevention Program group, Geisinger, and the Ohio State University.
Public Health Relevance Statement: Dietary intake self-monitoring is an instrumental but burdensome aspect of diabetes self-management despite recent advances in mobile technology. Here, we propose a new dietary assessment and intervention approach that, for the first time, integrates continuous glucose monitoring (CGM) data with a mobile diet tracker to automate personalized dietary guidance. This CGM-integrated mobile dietary guidance system will be the first to use biological data to reduce the burden of dietary self-monitoring with the aim of improving glucose control in people with type 2 diabetes.
Project Terms: Adult; Algorithms; American; base; Biological; blood glucose regulation; Caring; Chronic; Clinic; Clinical Trials; Collection; commercialization; Complement; cost; Data; design; Detection; Development; diabetes educator; diabetes management; Diabetes Mellitus; diabetes prevention program; Diagnosis; Diet; Diet Monitoring; Dietary Assessment; Dietary intake; Dietary Intervention; Dietary Practices; Dietitian; Disease Management; Drops; Eating; Effectiveness; Event; experience; Feedback; Food; food surveillance; Future; Glucose; glucose monitor; glycemic control; Glycosylated Hemoglobin; Glycosylated hemoglobin A; Goals; Gold; Health behavior; Health Care Costs; health care service organization; Health Personnel; Hemoglobin concentration result; Hyperglycemia; Hypoglycemia; improved; Individual; Information Management; insight; Insulin; Journals; Left; Licensing; Medical Care Costs; Medical Nutrition Therapy; Methods; mobile computing; Modeling; Monitor; monitoring device; Non-Insulin-Dependent Diabetes Mellitus; novel; Nutrition Therapy; Ohio; Patients; Pattern; Persons; Phase; prediction algorithm; prevent; Primary Health Care; prototype; Recommendation; response; Sampling; Self Efficacy; Self Management; Specificity; System; Technology; Testing; Time; tool; Training; Translating; Universities; usability; user centered d
