# Improving glycemic control among underserved patients with insulin-treated type 2 diabetes through nurse-led, app-based behavioral intervention

> **NIH NIH K99** · INDIANA UNIVERSITY INDIANAPOLIS · 2024 · $162,349

## Abstract

“Therapeutic inertia,” defined as a lack of timely adjustment to therapy when a patient’s treatment goals are not
met, is a major cause of poor outcomes in Type 2 Diabetes (T2D). This is especially true for low
socioeconomic status (low-SES) populations, who often face barriers to effective disease self-management at
three levels: (1) patient (e.g., limited resources, low health literacy, and limited access to care); (2) clinician
(e.g., lack of time and/or poor cultural sensitivity); and (3) health system (e.g., poor or absent decision support
and effective patient panel management). One way to address the complex challenges of chronic disease
management at these three levels is with multi-level health information technology (HIT)-supported behavioral
interventions. Such interventions combine changes to clinical workflows and self-management support to help
patients track diabetes electronically, transmit data to clinicians, and receive feedback for adjusting treatment.
Currently, minimal data exist to inform optimal design, implementation, and use of such multi-level behavioral
interventions, particularly for low-SES populations. My career goal is to establish an independently funded
research program that helps decrease health disparities using technologies that support effective long-term
self-management and improve outcomes. In this project, I propose a 5-year training and research plan for a
multi-level app-based intervention to improve outcomes for T2D in low-SES populations. I will develop and
pilot-test a nurse-led, app-based behavioral intervention consisting of three evidence-based interventions: (1)
education on A1C results and goal setting via MyChart, the patient portal in the Epic electronic health record;
(2) a problem-solving action plan developed by clinicians in collaboration with their patients; and (3) remote
monitoring via OnTrack, a top-rated diabetes app, to analyze blood glucose and identify the need to adjust
treatment. Training in population health informatics, HIT implementation science, health disparities, and
pragmatic trials will not only allow me to complete the proposed project, but will set the stage for expanding the
concept to other diseases and clinical use cases. Indiana University and the Regenstrief Institute, an
informatics powerhouse, plus a strong team of mentors in biomedical informatics, HIT implementation, health
disparity, and qualitative methods, provide an exceptional scientific environment. The proposed work includes:
(1) time-to-event analysis to understand population characteristics associated with persistent therapeutic
inertia, used to guide intervention development and tailoring [Aim 1]; (2) input from diabetes care team to adapt
the intervention and implementation strategies to the clinical operations level [Aim 2]; (3) a focus group study
with low-SES patients to help tailor the intervention [Aim 3]; and (4) a pilot study at two clinics (n=60, 30
patients per clinic) to assess the feasibilit...

## Key facts

- **NIH application ID:** 10891383
- **Project number:** 5K99NR020377-02
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Helen Nai-Chi Chen
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $162,349
- **Award type:** 5
- **Project period:** 2023-07-20 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10891383

## Citation

> US National Institutes of Health, RePORTER application 10891383, Improving glycemic control among underserved patients with insulin-treated type 2 diabetes through nurse-led, app-based behavioral intervention (5K99NR020377-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10891383. Licensed CC0.

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