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June 19, 2019

App may make diabetes self-management and personalized health care decisions easier

By Elizabeth Zimmermann
young woman on city street looking at her phone

Bithika Thompson, M.D., an endocrinologist at Mayo Clinic in Phoenix, and M. Adela Grando, PhD., a biomedical informaticist at Arizona State University, recently teamed up on a health care research project that could transform the way individuals manage their diabetes.

Type 1 diabetes is a chronic disease that affects more than 1.2 million people in the U.S. There is no cure for Type 1 diabetes. Patients must consistently engage with ongoing self-management behaviors to manage the disease effectively and prevent the numerous complications that can result from poorly controlled diabetes. These self-management behaviors include frequent monitoring of blood glucose levels, adherence to diet, and administration of insulin based on blood sugar levels and carbohydrates consumed.

screen shot of app

However, the complexity of managing these behaviors in daily life makes diabetes management difficult. When the researchers started the project, they wanted to better understand patient behaviors with respect to diabetes self-management. Armed with that knowledge, they and their team hoped to develop custom interventions that would help each person better control their blood glucose levels, thereby lessening the likelihood of diabetes-related adverse health events.

Specifically, they developed and tested a new way to design individualized data-driven educational interventions to improve Type 1 diabetes adherence. To do this, the researchers collected:

  • Real-time self-reported data collected daily by a smartphone app they had previously developed,
  • Objective data collected daily from the patients' insulin pump and continuous glucose monitoring systems, and
  • Patient disease and device comprehension and self-management routines, as captured through surveys and interviews.

The app tracks real-time information such as exercise plans, food and beverage intake, and diabetes compensation techniques (eating a snack prior to exercise to prevent a low blood sugar).

Additional computer algorithms were developed to automatically collect and analyze all the information brought in, and provide actionable information for clinicians.

"Patients have unique self-care preferences and behaviors that should be taken into consideration by clinicians," says Dr. Thompson, "particularly as patients are more likely to achieve adherence when incorporating lifestyle and self-management preferences."

Based on what the team found out about patient behavior, they were able to help develop more specific educational materials for patients. They also developed talking points for clinicians to lead discussions on the patients' individual self-management behaviors that could lead to positive or negative health outcomes.

"We hope that targeted patient education, and facilitating individualized conversations, will lead to better health outcomes," she says.

This research was funded by Arizona State University and the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery.

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Tags: Arizona State University, artificial intelligence, Bithika Thompson, Center for the Science of Health Care Delivery, diabetes, Findings, Innovations, News

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