# iMatter2: An AI-driven approach to supercharge a novel digital patient-reported outcomes tool for diabetes management

> **NIH AHRQ R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $399,998

## Abstract

Project Summary: Uncontrolled type 2 diabetes (T2D) is a major health problem in the US that constitutes a
significant cause of morbidity and mortality, particularly in minoritized populations who continue to suffer
disproportionately higher rates of complications. Despite the significant role primary care practices play in the
management of T2D, they were never designed nor ideally suited to capture and address the complex
psychosocial (e.g., stress) and behavioral factors (e.g., adherence to self-care) that significantly impact
glycemic control. However, without incorporating the psychosocial and behavioral impacts of T2D into clinical
decision-making, achievement of the outcomes desired by patients and primary care providers (PCP) will be
unattainable. Application of patient-reported outcomes (PROs) to T2D care represents an ideal opportunity to
capture the psychosocial and behavioral impacts of living with T2D on patients' clinical outcomes. In direct
response to NOT-HS-16-015, this renewal application, which builds on our AHRQ-funded randomized control
trial (RCT) evaluating the efficacy of an innovative mHealth PRO tool, will address this gap in the literature.
Using a theory-driven and user-centered design approach, we will conduct the study in two phases: 1) a
formative phase to refine and user-test iMatter2 in real-world settings; and 2) a clinical trial phase. The
formative phase will use a mixed-methods approach to: a) integrate enhanced functionalities into our existing
tool and the electronic health record (EHR) in a diverse network of primary care practices serving minoritized
populations; and b) evaluate the usability of iMatter2 in a subset of patients with T2D and PCPs to optimize the
tool's performance and workflow integration. For the clinical trial phase, we will evaluate in a hybrid type 1
RCT, the effectiveness of iMatter2 vs usual care (UC) on HbA1c reduction at 12-months among 353 patients
with uncontrolled T2D (HbA1c>7%). Using the extended RE-AIM framework, we will apply an equity lens to
measure the reach, adoption, and implementation of iMatter2. We will also explore the associations between
patient's PRO responses and HbA1c reduction. PCPs will be the unit of randomization with all patients within
the PCP's panel in the same group. Patients randomized to iMatter2 will receive and respond to personalized
PROs via text message, receive personalized motivational and educational messages via an AI chatbot; and
have access to an interactive dashboard that visualizes their daily PRO and HbA1c data. PCPs randomized to
iMatter2 will have access to EHR-integrated clinical decision support tools to nudge PCPs to view the PRO
reports and HbA1c data. Patients randomized to UC will receive standard T2D treatment recommendations
from their PCPs; PCPs will not have access to the EHR reports. Primary outcome is mean reduction in HbA1c,
extracted from home A1c kits, from baseline to 12 months. Secondary outcomes are 1) equitable reach...

## Key facts

- **NIH application ID:** 10876986
- **Project number:** 5R01HS026522-07
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Devin M Mann
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2024
- **Award amount:** $399,998
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10876986

## Citation

> US National Institutes of Health, RePORTER application 10876986, iMatter2: An AI-driven approach to supercharge a novel digital patient-reported outcomes tool for diabetes management (5R01HS026522-07). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10876986. Licensed CC0.

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