Using sleep and daily rhythms to better predict blood sugar after meals
Integrating Sleep and Circadian Factors into Machine Learning Models for Personalized Glycemic Response Prediction
It uses sleep, activity, and meal-timing data with continuous glucose monitors to better predict adults' blood sugar spikes after meals, including night shift workers.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Colorado State University NIH-funded |
| Lab location | 1 site (Fort Collins, United States) |
| Project ID | NIH-11322380 on NIH RePORTER |
What this research studies
You would wear a continuous glucose monitor (CGM) while researchers tightly track when you eat, sleep, and move over an 8-week period. The team will collect thousands of post-meal glucose readings linked to detailed sleep and activity timing. They will train machine-learning models that include circadian (daily rhythm) information so predictions reflect how timing affects your glucose. The goal is to make meal-time glucose predictions that work in real life and across different daily schedules.
Who could benefit from this research
Good fit: Ideal candidates are adults (21+)—especially night shift workers or people at risk for type 2 diabetes—who can wear a CGM and follow scheduled meal and sleep monitoring for several weeks.
Not a fit: People with type 1 diabetes, children, or anyone unable to comply with strict monitoring and controlled feeding are less likely to benefit directly from this work.
Why it matters
Potential benefit: If successful, this could lead to more personalized meal timing and dietary advice that reduces harmful post-meal blood sugar spikes and lowers risk for type 2 diabetes and heart disease.
How similar studies have performed: Prior research shows that circadian timing affects glucose control, but integrating sleep and circadian data into machine-learning models for post-meal glucose prediction is relatively new.
Where this research is happening
Fort Collins, United States
- Colorado State University — Fort Collins, United States (Active)
Researchers
- Principal investigator: Broussard, Josiane — Colorado State University
- Study coordinator: Broussard, Josiane
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.