Modeling Dynamic Food Choice Processes to Understand Dietary Intervention Effects

• Type of Material: Article
• Authors: Christopher Steven Marcum, National Human Genome Research Institute; Megan R. Goldring, Columbia University; Colleen McBride, Emory University; Susan Persky, National Human Genome Research Institute
• Language: English
• Date: 2018-03-01
• Publisher: Oxford University Press Inc.
• Publication Version: Final Published Version
• Copyright Statement: © The Society of Behavioral Medicine 2018.
• License: Creative Commons Universal : Public Domain Dedication
• Final Published Version (URL): http://dx.doi.org/10.1093/abm/kax041
• Title of Journal or Parent Work: Annals of Behavioral Medicine
• Volume: 52
• Issue: 3
• Start Page: 252
• End Page: 261
• Grant/Funding Information: Dr. C. S. Marcum’s work was supported by ZIA HG200397 (Dr. Laura Koehly, PI).; This research was supported by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health.
• Keywords: Relational event model; FRUIT; Nutrition intervention; Psychology, Multidisciplinary; Micro-behavior; HEALTH; NETWORKS; OBESITY; Psychology; Social Sciences; VEGETABLE INTAKE; BEHAVIOR; Food choice
• Research Categories: Psychology, General

Abstract

Background Meal construction is largely governed by nonconscious and habit-based processes that can be represented as a collection of in dividual, micro-level food choices that eventually give rise to a final plate. Despite this, dietary behavior intervention research rarely captures these micro-level food choice processes, instead measuring outcomes at aggregated levels. This is due in part to a dearth of analytic techniques to model these dynamic time-series events. Purpose The current article addresses this limitation by applying a generalization of the relational event framework to model micro-level food choice behavior following an educational intervention. Method Relational event modeling was used to model the food choices that 221 mothers made for their child following receipt of an information-based intervention. Participants were randomized to receive either (a) control information; (b) childhood obesity risk information; (c) childhood obesity risk information plus a personalized family history-based risk estimate for their child. Participants then made food choices for their child in a virtual reality-based food buffet simulation. Results Micro-level aspects of the built environment, such as the ordering of each food in the buffet, were influential. Other dynamic processes such as choice inertia also influenced food selection. Among participants receiving the strongest intervention condition, choice inertia decreased and the overall rate of food selection increased. Conclusions Modeling food selection processes can elucidate the points at which interventions exert their influence. Researchers can leverage these findings to gain insight into nonconscious and uncontrollable aspects of food selection that influence dietary outcomes, which can ultimately improve the design of dietary interventions.