Creating a machine learning tool to identify opioid withdrawal and cravings.
Official title: Development of a Machine Learning Algorithm to Detect Opioid Withdrawal and Cravings to Increase Treatment Retention
Funded at Spark Biomedical INC · NIH-10783563 · Data updated 2026-11-12
This study is testing a new technology that uses wearable devices to help people in treatment for opioid use disorder by tracking their withdrawal symptoms and cravings, so they can get support when they need it and stay on track with their recovery.
Quick facts
| Grant type | Sbir 1 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Spark Biomedical INC NIH-funded |
| Lab location | 1 site (Friendswood, United States) |
| Project start | June 15, 2024 |
| Project end | May 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $318,875 |
| NIH project number | 1R43DA059448-01 |
| Project ID | NIH-10783563 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adults aged 21 and older who are undergoing treatment for opioid use disorder and experiencing withdrawal symptoms.
✕Less likely to help
Patients who are not currently undergoing treatment for opioid use disorder or those who do not experience withdrawal symptoms may not benefit from this research.
What this research studies
This research focuses on developing a machine learning algorithm that can detect symptoms of opioid withdrawal and cravings in patients undergoing treatment for opioid use disorder. By utilizing wearable technology, the algorithm aims to monitor physiological signals and provide real-time feedback to both patients and healthcare providers. The goal is to enhance treatment retention by addressing withdrawal symptoms more effectively, ultimately helping patients stay in treatment and reduce the risk of relapse.
Why it matters
Potential benefit: If successful, this research could lead to improved treatment retention for patients struggling with opioid use disorder by providing timely interventions for withdrawal symptoms.
How similar studies have gone: While there have been successful applications of wearable technology in monitoring health conditions, the specific approach of using machine learning for opioid withdrawal detection is relatively novel and untested.
Where this research is happening
-
Spark Biomedical INC
Friendswood, United States Active
Researchers
- Principal investigator: Khodaparast, Navid — Spark Biomedical INC
- Study coordinator: Khodaparast, Navid
Related research
Other NIH-funded projects on the same topics.
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Using non-invasive nerve stimulation to help infants with opioid withdrawal
This study is looking at a gentle treatment called transcutaneous auricular neurostimulation to help babies who are going through withdrawal after being exposed to opioids before birth, with the hope of making them feel better without relying on more medications.
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Using ear stimulation to help people stay in treatment for opioid use disorder
This study is looking at a gentle ear stimulation technique to help people with opioid use disorder stick with their treatment and feel better, offering a safer and easier option than some traditional methods.
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.