Using Machine Learning to Find Factors Linked to Disordered Eating Risk and Treatment in Rural Teens
Official title: Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents
Funded at Central Michigan University · NIH-11360562 · Data updated 2026-11-12
A machine learning study with rural teens aims to identify the most important factors that influence disordered eating risk and how treatments work, with the goal of informing better care for this age group.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Central Michigan University NIH-funded |
| Lab location | 1 site (Mount Pleasant, United States) |
| Project start | August 15, 2026 |
| Project end | August 14, 2029 |
| Funding year | 2026 |
| Award (this year) | $558,960 |
| NIH project number | 1R15MH144696-01 |
| Project ID | NIH-11360562 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal participants are adolescents aged 12–20 living in rural areas who experience or are at risk for disordered eating and who can provide survey data.
✕Less likely to help
Teens outside rural settings or those without disordered eating risk factors may not directly benefit from the findings.
What this research studies
This project uses data from rural adolescents aged 12–20 to identify which social, environmental, and personal factors most affect disordered eating risk and response to treatment. The team plans to apply machine learning techniques to cross-sectional survey data to uncover patterns that could guide tailored prevention and intervention efforts. By focusing on rural communities, the study seeks practical insights that could improve access to care and treatment outcomes for teens in these settings.
Why it matters
Potential benefit: If successful, the findings could help clinicians and communities prioritize the most impactful factors to address, potentially improving prevention and treatment for rural adolescents with disordered eating.
How similar studies have gone: Previous research has shown promise in using machine learning to identify risk factors for eating disorders, but focusing on rural adolescents is a novel aspect of this work.
Where this research is happening
-
Central Michigan University
Mount Pleasant, United States Active
Researchers
- Principal investigator: Hahn, Samantha Lynn — Central Michigan University
- Study coordinator: Hahn, Samantha Lynn
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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.