Using technology to understand what influences children's eating habits
Official title: Leveraging machine learning and EMA data to identify factors that predict children's energy intake
Funded at Lsu Pennington Biomedical Research Ctr · NIH-11165882 · Data updated 2026-11-12
This study is looking at how using technology to collect information in real-time can help us understand what affects how much kids eat, so we can create personalized plans to help them develop healthier eating habits.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Lsu Pennington Biomedical Research Ctr NIH-funded |
| Lab location | 1 site (Baton Rouge, United States) |
| Project start | June 1, 2025 |
| Project end | May 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $80,020 |
| NIH project number | 1F32HD116537-01A1 |
| Project ID | NIH-11165882 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are children aged 8 to 12 years old who are part of the Mothers And Their Children's Health (MATCH) study.
✕Less likely to help
Patients outside the age range of 8 to 12 years or those not involved in the MATCH study may not benefit from this research.
What this research studies
This research investigates how real-time data collection through technology can help identify factors that influence children's energy intake. By utilizing ecological momentary assessment (EMA), the study aims to gather information on various contextual factors such as location, food preparation style, and social influences at the time of eating. Machine learning algorithms will be developed and tested to predict children's eating behaviors based on this data. The goal is to create personalized models that can help modify and improve children's dietary habits.
Why it matters
Potential benefit: If successful, this research could lead to personalized interventions that help children develop healthier eating habits and reduce obesity rates.
How similar studies have gone: Previous research has shown promise in using technology and machine learning to understand dietary behaviors, indicating that this approach could be effective.
Where this research is happening
-
Lsu Pennington Biomedical Research Ctr
Baton Rouge, United States Active
Researchers
- Principal investigator: Diktas, Hanim Ecem — Lsu Pennington Biomedical Research Ctr
- Study coordinator: Diktas, Hanim Ecem
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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.