Making smartphone and wearable health messages more personal for heart and chronic disease care
Leveraging ML algorithms and data integration techniques to improve efficiency of causal moderation analyses of micro-randomized trial data
This project uses data from phones and wearables plus machine learning to better time and tailor digital support for people with heart and other long-term conditions.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Michigan at Ann Arbor NIH-funded |
| Lab location | 1 site (Ann Arbor, United States) |
| Project ID | NIH-11237068 on NIH RePORTER |
What this research studies
From your perspective, researchers will use information collected by smartphones and wearables in micro-randomized trials to learn when and how digital prompts help most. They will combine different sensor and phone data and apply machine-learning methods to find patterns that predict when an intervention will be helpful. The team will develop new analysis techniques so future apps can deliver the right type and amount of support at the right moment. This work focuses on computer modeling of real trial data rather than testing a new device on people directly.
Who could benefit from this research
Good fit: People living with cardiovascular disease or other chronic illnesses who use smartphones or wearable devices are the main groups who could benefit from tools developed from this work.
Not a fit: People without access to smartphones or wearables, or those needing immediate medical procedures rather than digital self-management help, are unlikely to benefit directly.
Why it matters
Potential benefit: If successful, this could help digital health apps give more useful, timely support that improves self-management and outcomes for people with cardiovascular and other chronic diseases.
How similar studies have performed: Just-in-time adaptive interventions and micro-randomized trials have shown promise for digital health, but applying advanced machine-learning to causal moderation in these trials is a relatively new approach.
Where this research is happening
Ann Arbor, United States
- University of Michigan at Ann Arbor — Ann Arbor, United States (Active)
Researchers
- Principal investigator: Dempsey, Walter — University of Michigan at Ann Arbor
- Study coordinator: Dempsey, Walter
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 below.