Predictors of Child Eating and Parent Feeding in the ECHO Cohort

Key Takeaways: Dr. Mansolf will present findings from a study using data from over 3,500 children in the ECHO cohort to examine the complex relationships between child eating behaviors and parental feeding practices. Using machine learning approaches such as random forests and regression trees, the research identified key predictors of these behaviors, revealing both independent and interdependent patterns. Results showed that caregiver and child age were the strongest predictors, followed by caregiver stress, child BMI, and social support. These findings highlight potential intervention opportunities and provide new insights into the multifaceted nature of family food dynamics and their implications for child nutritional health.

Maxwell Mansolf, PhD

Assistant Professor in the Department of Medical Social Sciences

Northwestern University’s Feinberg School of Medicine

Co-PI, ECHO Measurement Core

Speaker Biography:

Dr. Maxwell Mansolf is an Assistant Professor in the Department of Medical Social Sciences at Northwestern University’s Feinberg School of Medicine. His research focuses on psychometric methods and child health outcomes, with expertise in classical test theory, item response theory, and computerized adaptive testing. Dr. Mansolf serves as a data scientist on large-scale longitudinal studies, including the Environmental influences on Child Health Outcomes (ECHO) Cohort, where he manages and analyzes harmonized data from 69 cohorts to advance understanding of child health and development. His recent work includes developing assessment tools such as the NIH Baby Toolbox neurodevelopmental assessment system and contributing to psychometric research published in venues including Multivariate Behavioral Research and the Handbook of Structural Equation Modeling. He is proficient in R statistical programming and recently published the AlignLV package to CRAN. Dr. Mansolf is also author of an upcoming Cambridge University Press textbook on MATLAB programming for non-technical audiences.

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