How emotions and body signals predict return to substance use after leaving treatment
Official title: Interplay of Affect and Physiology in the Real-Time Prediction of Return to Use During Community Reintegration of Substance Users
Funded at University of Rhode Island · NIH-11310172 · Data updated 2026-11-12
This project tracks emotions and wearable body signals in people leaving residential substance treatment to spot moments when they might be likely to use again.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Rhode Island NIH-funded |
| Lab location | 1 site (Kingston, United States) |
| Project start | May 1, 2024 |
| Project end | February 28, 2029 |
| Funding year | 2026 |
| Award (this year) | $679,078 |
| NIH project number | 5R01DA058636-03 |
| Project ID | NIH-11310172 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults (21+) who are leaving or have recently left residential substance use treatment and are willing to wear a sensor and answer brief mobile surveys.
✕Less likely to help
People under 21, those not coming from residential SUD treatment, or anyone unwilling to use wearables or mobile surveys may not benefit from joining this project.
What this research studies
You would wear a small biosensor and get brief phone prompts throughout the day to report how you feel while the study records physiological signals like heart rate and skin responses. The team focuses on the high-risk period soon after discharge—especially the first 30 days—to see which emotional ups and downs and body changes come right before return to use. Researchers combine the momentary self-reports and sensor data to build models that predict risk in real time. The aim is to learn when people need extra support during community reintegration so help can be offered at the right moments.
Why it matters
Potential benefit: If successful, this work could help deliver timely supports to prevent or delay return to substance use after treatment.
How similar studies have gone: Previous studies using mobile surveys and wearables have shown promise for spotting short-term risk signals, but real-time relapse prediction during community reintegration remains an emerging and still-developing approach.
Where this research is happening
-
University of Rhode Island
Kingston, United States Active
Researchers
- Principal investigator: Weiss, Nicole Holland — University of Rhode Island
- Study coordinator: Weiss, Nicole Holland
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.