Using machine learning to track bednet use and behaviors linked to malaria risk
Official title: Training of machine learning algorithms for the classification of accelerometer-measured bednet use and related behaviors associated with malaria risk
Funded at University of California, San Francisco · NIH-10899664 · Data updated 2026-11-12
This study is looking at better ways to track how people use special bednets that help prevent malaria, using smart technology to get more accurate information than just asking people, so we can improve how we protect against the disease.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | August 4, 2023 |
| Project end | July 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $301,881 |
| NIH project number | 5R21AI178393-02 |
| Project ID | NIH-10899664 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals living in malaria-endemic regions, particularly those who use or have access to bednets.
✕Less likely to help
Patients who do not reside in malaria-affected areas or do not use bednets may not benefit from this research.
What this research studies
This research focuses on improving the measurement of how people use long-lasting insecticide-treated bednets (LLINs) to prevent malaria. By employing advanced machine learning algorithms and accelerometer-based sensors, the project aims to accurately classify various behaviors associated with bednet use. This approach seeks to overcome the limitations of traditional self-reported measures, providing a more reliable assessment of bednet usage patterns and their impact on malaria exposure. The findings could help in understanding and enhancing malaria prevention strategies.
Why it matters
Potential benefit: If successful, this research could lead to more effective malaria prevention strategies by providing accurate data on bednet usage.
How similar studies have gone: Previous research has shown success in using technology to monitor health behaviors, but this specific approach using machine learning and accelerometers is relatively novel.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Krezanoski, Paul Joseph — University of California, San Francisco
- Study coordinator: Krezanoski, Paul Joseph
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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.