Dynamic, adaptive mobile health interventions powered by smart learning
Official title: TR&D2 - Dynamic Optimization of Continuously Adapting Collaborative mHealth Interventions via Prudent, Statistically Efficient, and Coherent Reinforcement Learning
Funded at University of Memphis · NIH-11336104 · Data updated 2026-11-12
A center-led project uses advanced learning systems to tailor mobile health support to individuals over time, helping people manage health with personalized digital interventions.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Memphis NIH-funded |
| Lab location | 1 site (Memphis, United States) |
| Project start | July 15, 2020 |
| Project end | April 30, 2031 |
| Funding year | 2026 |
| Award (this year) | $280,716 |
| NIH project number | 2P41EB028242-06 |
| Project ID | NIH-11336104 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are individuals using mobile health tools who have chronic health management needs and caregiver support.
✕Less likely to help
Patients who do not use mobile health apps or do not engage with digital health interventions may see limited or no benefit.
What this research studies
This grant supports the development of adaptive, just-in-time health interventions delivered through mobile devices. It uses reinforcement learning and domain knowledge to personalize choices in real time, aiming to boost long-term health outcomes. The approach involves collaborating with users and their caregivers to sustain engagement and effectiveness across diverse health scenarios. The work builds on existing mHealth infrastructure to learn efficiently from limited data and to respect the user’s context and needs.
Why it matters
Potential benefit: If successful, patients could receive more relevant and timely digital health guidance that improves adherence and health management.
How similar studies have gone: Prior work in personalized digital health and JITAI strategies has shown promise, but this project advances RL-based personalization at scale.
Where this research is happening
-
University of Memphis
Memphis, United States Active
Researchers
- Principal investigator: Murphy, Susan a — University of Memphis
- Study coordinator: Murphy, Susan a
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.