Leveraging machine learning and EMA data to identify factors that predict children's energy intake
Description: PROJECT NARRATIVE A better understanding of children’s eating behavior will lead to the development of innovative intervention strategies for preventing childhood obesity. The current F32 application is designed to employ machine learning techniques to identify factors that predict children’s eating behaviors based on ecological momentary assessment (EMA) data. The findings of the proposed research have the potential to inform personalized interventions that integrate EMA data and provide real-time feedback to promote healthy eating and prevent childhood obesity.
Funding type: fellowship
Funder scheme: F32
Start date: 2025-06-01
End date: 2028-05-31
Investigator: HANIM ECEM DIKTAS
Institution: LSU PENNINGTON BIOMEDICAL RESEARCH CTR
Funded outputs count: 7
Funder award ID: F32HD116537
OpenAlex ID: G9000902890