Improving health management through personalized mobile health interventions
Official title: mDOT TR&D2 (Optimization): Dynamic Optimization of Continuously Adapting mHealth Interventions via Prudent, Statistically Efficient, and Coherent Reinforcement Learning
Funded at University of Memphis · NIH-11035078 · Data updated 2026-11-12
This study is all about creating helpful apps and wearable devices that give you personalized support to make healthier choices based on your unique needs, so you can better manage your chronic condition and stay healthy.
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 | November 30, 2025 |
| Funding year | 2025 |
| Award (this year) | $173,823 |
| NIH project number | 5P41EB028242-05 |
| Project ID | NIH-11035078 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals living with chronic diseases who are seeking innovative ways to manage their health through technology.
✕Less likely to help
Patients who do not have access to mobile technology or prefer traditional healthcare methods may not benefit from this research.
What this research studies
This research focuses on creating and optimizing mobile health (mHealth) interventions that adapt to individual patients' needs in real-time. By using advanced statistical methods and reinforcement learning, the project aims to develop tools that help patients make healthier lifestyle choices tailored to their unique circumstances. The goal is to provide personalized support that can help manage chronic diseases more effectively and potentially prevent new health issues from arising. Patients will benefit from interventions delivered through wearables and smartphone apps that respond to their specific health contexts.
Why it matters
Potential benefit: If successful, this research could lead to more effective management of chronic diseases and improved overall health outcomes for patients.
How similar studies have gone: Previous research has shown promise in using personalized mHealth interventions, indicating that this approach could be effective in improving patient outcomes.
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
Related research
Other NIH-funded projects on the same topics.
-
Next-Generation Wearable AI for Precision COPD Risk and Care
A patient-centered project uses a smart wearable and AI to predict COPD flare-ups and support doctors in making better care decisions.
-
How lifestyle factors together affect cognitive aging in adults 55+
A long-term study will look at how things like activity, smoking, drinking, body weight, and social connections influence brain aging in adults 55 and older.
-
Music-based approaches to relieve pain in adults
A research network is testing how music-based interventions and new technologies can help adults manage acute and chronic pain.
-
Data-driven QM/MM and QM-Cluster Modeling for Enzyme Reactions
A data-guided approach will use machine learning to make QM/MM and QM-cluster modeling of enzymes faster and more accurate, helping us understand how enzymes work and aiding future drug design.
-
Rotary chair and VNG system to advance vestibular care and research in children
A grant to buy a high-tech vestibular testing system for the University of Memphis to study balance and brain development from infancy through childhood.
-
Technology and Training Dissemination Core for temporally-precise interventions
This grant supports creating and sharing tools, training, and infrastructure to help researchers design and use just-in-time, wearable-enabled health interventions for chronic diseases.
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.