# Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data - a Randomized Controlled Trial.

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $636,671

## Abstract

In this project, we will evaluate the efficacy of a novel approach to tailoring behavioral
interventions for self-management of type 2 diabetes to individuals' behavioral and glycemic
profiles discovered using computational learning and self-monitoring data. Growing evidence
suggests significant differences in individuals' physiology and glycemic function, and their
cultural, social, and economical circumstances that impact diabetes self-management. These
discoveries highlight the need for personally tailoring both medical treatment and behavioral
interventions. Yet tailored behavioral interventions proposed thus far typically focus on
motivation for behavior change and individuals' psycho-social characteristics, rather than
personalizing self-management strategies, such as changes in diet and physical activity.
Moreover, tailoring typically relies on expert identification of tailoring variables and decision
rules, and on standard surveys for assessment these variables. Data collected with self-
monitoring can more accurately reflect an individual's behaviors and glycemic patterns, thus
highlighting their “behavioral phenotypes”, yet such data are rarely utilized in tailoring. The
ongoing focus of this research is on developing informatics interventions for diabetes self-
management, with a specific focus on personal discovery with self-monitoring data and on
problem-solving for improving glycemic control. In the proposed research we will introduce
GlucoType that relies on computational pattern analysis of data collected with self-monitoring
technologies to identify behavioral patterns associated with poor glycemic control and formulate
personalized behavioral goals for changing problematic behaviors. In our preliminary studies we
have established that 1) computational phenotyping methods can accurately identify systematic
associations between individuals' activities and changes in BG levels; 2) these patterns can be
automatically translated into behavioral goals formulated in a natural language in a way
consistent with goals formulated by diabetes experts, and 3) individuals with T2DM can
understand and follow these behavioral goals and engage with GlucoType for personal self-
management of diabetes. In the proposed research we will evaluate GlucoType's efficacy in a
randomized controlled trial conducted with a practice-based research network (PBRN) of
Federally Qualified Community Health Centers (FQHCs) in the metropolitan New York area.

## Key facts

- **NIH application ID:** 10380910
- **Project number:** 5R01DK113189-04
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Olena Mamykina
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $636,671
- **Award type:** 5
- **Project period:** 2019-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10380910

## Citation

> US National Institutes of Health, RePORTER application 10380910, Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data - a Randomized Controlled Trial. (5R01DK113189-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10380910. Licensed CC0.

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