Modeling Dynamic Food Choice Processes to Understand Dietary Intervention Effects.

Area of Science:

• Behavioral Science
• Nutritional Science
• Data Science

Background:

• Dietary behavior is driven by subconscious, habit-based micro-choices, not just overall intake.
• Current dietary interventions often overlook these micro-level food choices, focusing on aggregated outcomes.
• A lack of analytical methods for dynamic time-series events hinders the study of micro-level food choices.

Purpose of the Study:

• To address the limitation in studying micro-level food choices by applying a generalized relational event framework.
• To model dynamic food selection processes following an educational intervention.
• To understand the influence of nonconscious factors on dietary behavior.

Main Methods:

• Relational event modeling was employed to analyze food choices made by 221 mothers for their children.
• Participants were randomized into three groups receiving different levels of obesity risk information.
• Food choices were recorded in a virtual reality food buffet simulation.

Main Results:

• Micro-environmental factors, like food order in the buffet, significantly impacted food selection.
• Choice inertia, the tendency to stick with previous choices, also played a role in food selection.
• In the highest intervention group, choice inertia decreased, and the rate of food selection increased.

Conclusions:

• Modeling micro-level food selection processes highlights intervention effectiveness points.
• Findings offer insights into subconscious and uncontrollable aspects of food selection.
• This research can improve the design of future dietary behavior interventions.