Understanding daily behaviors that affect cancer risk
Novel Statistical Models for Intensive Longitudinal Analyses of Cancer Control Behaviors
This project creates new tools that use frequent real-time mood and activity data to better predict behaviors linked to cancer risk for people who smoke, are inactive, or have excess body weight.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Chicago NIH-funded |
| Lab location | 1 site (Chicago, United States) |
| Project ID | NIH-11194389 on NIH RePORTER |
What this research studies
They will collect intensive short-term data like momentary mood reports (EMA) and activity from wearable accelerometers, often many times per day. The team will build and test multilevel statistical models that summarize each person's average levels, variability, and short-term trends to see how those patterns relate to smoking, physical activity, body weight, alcohol use, and nutrition. They will apply these models to existing and new datasets, package the methods into user-friendly software (MixWILD), and share the tools with other researchers. This work aims to uncover day-to-day patterns that could be targeted to help people change risky behaviors.
Who could benefit from this research
Good fit: Ideal participants would be adults willing to carry a smartphone and wear an accelerometer, answer multiple brief surveys each day, and who are at risk for behavior-related cancers (for example, smokers, physically inactive individuals, or people with excess body weight).
Not a fit: People who are not willing or able to use a smartphone or wear an activity monitor, or whose cancer risk is unrelated to behavior (for example, inherited genetic cancers), may not benefit directly from this work.
Why it matters
Potential benefit: Could enable more personalized, timely support to help people reduce smoking, increase activity, and manage weight by identifying the daily patterns that lead to risky behaviors.
How similar studies have performed: Other studies using EMA and accelerometers have successfully linked momentary mood and activity patterns to health behaviors, but these specific multilevel modeling tools are a novel advance to better quantify intraindividual patterns.
Where this research is happening
Chicago, United States
- University of Chicago — Chicago, United States (Active)
Researchers
- Principal investigator: Hedeker, Donald — University of Chicago
- Study coordinator: Hedeker, Donald
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.