# Building and Validating Lifelong Self-Management Capacity with Advanced AI: MyMSMentor

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2024 · $329,346

## Abstract

PROJECT SUMMARY
In 2018, 51.8% (129 million) of American adults had at least one chronic condition (e.g., diabetes, arthritis). The
total cost of chronic conditions is $3.7 trillion; about 84% of healthcare costs are attributed to chronic condition
treatment. Self-management is critical to reducing clinical and economic burdens. Multiple sclerosis (MS)
is one of the most demanding cases, costing approximately $28 billion for about one million Americans with MS.
However, people with chronic conditions, including people with MS (PwMS), often face challenges in applying
self-care information and partnering with their clinicians over their illness progression to reflect their ever-
changing needs. Unfortunately, the existing self-management interventions and mHealth solutions for PwMS
and other patients do not support self-sustained personalization of self-management through lifelong user
modeling. To address these challenges, we propose MyMSMentor, a Health Action Process Approach (HAPA)-
driven artificial intelligence (AI) agent, to estimate users’ states and provide just-in-time guidance. Overarching
goals are: [i] developing personalized, lifelong self-management support that is contextualized in an individual’s
daily life and available resources; [ii] making MyMSMentor generalizable for the millions of people who need
lifelong self-management of other chronic conditions. The innovation of the study focuses on bridging HAPA,
social-cognitive science, and AI algorithms to build a theory-based intelligent healthcare advising agent that can
support the adoption and maintenance of health behavior. Specific Aims are: (1a) Build MyMSMentor for
proactive patient-centered self-management support; (1b) adopt participatory design to refine and evaluate
MyMSMentor iteratively; (2) validate the feasibility and acceptability of MyMSMentor with PwMS. For Aim-1a, we
will build [i] lifelong comprehensive user modeling by combining the Information Need Model, Known Information
Model, and HAPA status variables and [ii] five function modules (i.e., symptom recording and updating, case
summary generation, note-taking, user model inference module, HAPA-based interactive intervention module).
For Aim-1b, we will collaborate with patient and clinician advisory boards to co-design and evaluate MyMSMentor
iteratively. For Aim-2, 50 PwMS will use MyMSMentor for 30 days. They will use feasibility and acceptance
measures to evaluate MyMSMentor and provide user feedback. The study will make a critical and timely
contribution to establishing a generalizable novel model to bridge big data, artificial intelligence, and social-
cognitive science for precision self-management among people with chronic conditions and support the
National Neurological Conditions Surveillance System to advance data-powered research and healthcare
services.

## Key facts

- **NIH application ID:** 10803204
- **Project number:** 1R01LM014250-01A1
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Jessie Chin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $329,346
- **Award type:** 1
- **Project period:** 2024-09-18 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10803204

## Citation

> US National Institutes of Health, RePORTER application 10803204, Building and Validating Lifelong Self-Management Capacity with Advanced AI: MyMSMentor (1R01LM014250-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10803204. Licensed CC0.

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