Improving how we measure physical activity, sleep, and sedentary behavior using advanced data analysis techniques
Official title: Novel machine learning and missing data methods for improving estimates of physical activity, sedentary behavior and sleep using accelerometer data
Funded at Stanford University · NIH-10765650 · Data updated 2026-11-12
This study is working on new ways to better understand how much you move, how often you sit, and how well you sleep using movement-tracking devices, so we can learn more about how these habits affect heart health.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | May 3, 2021 |
| Project end | January 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $334,475 |
| NIH project number | 5R01LM013355-04 |
| Project ID | NIH-10765650 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include adults who are interested in monitoring their physical activity, sleep, and sedentary behavior, particularly those at risk for cardiovascular issues.
✕Less likely to help
Patients who do not use accelerometers or are unable to provide data on their physical activity and sleep patterns may not benefit from this research.
What this research studies
This research focuses on developing new statistical and machine learning methods to analyze data from accelerometers, which are devices that track movement. By accurately estimating physical activity, sedentary behavior, and sleep patterns, the study aims to understand their impact on health outcomes, particularly cardiovascular health. The researchers will create and validate methods to differentiate between periods of inactivity and actual sleep, addressing current limitations in data interpretation. This approach is crucial for providing more reliable insights into how lifestyle behaviors affect health.
Why it matters
Potential benefit: If successful, this research could lead to better understanding and management of physical activity and sleep, ultimately improving cardiovascular health for patients.
How similar studies have gone: Other research has shown success in using machine learning techniques to analyze health-related data, indicating that this approach has potential for meaningful advancements.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Desai, Manisha — Stanford University
- Study coordinator: Desai, Manisha
Related research
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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.