Affective Reactivity to Food Consumption: Within- and Between-person Associations in Daily Life

Restricted (Penn State Only)
Author:
Brinberg, Mimi Ida
Graduate Program:
Human Development and Family Studies
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
March 17, 2017
Committee Members:
  • Nilam Ram, Thesis Advisor
  • Lori A. Francis, Committee Member
Keywords:
  • affect
  • meal consumption
  • snack consumption
  • longitudinal analysis
Abstract:
Objectives: The impact of food consumption on our emotions (and vice versa) has the potential to act as a risk factor or causal mechanism for overweight and obesity. Previous research on the relationship between affect and emotions on food consumption has been mixed. In this paper, I examine this relationship (positive and negative affect in relation to meal and snack consumption) using a multilevel modeling approach with data collected from a measurement burst study to address these conflicting results. Method: Using data from 140 adults (MAge= 46.20 years) who provided three weeks of daily reports as part of an intensive experience sampling study, I examine differences in how meal and snack consumption influence daily positive and negative affect. Results: Between-person differences in meal consumption are related to higher levels of PA and lower levels of NA, and between-person differences in snack consumption are related to lower levels of PA and higher levels of NA. Individuals who consumed more meals or snacks tended to have higher levels of PA and lower levels of NA. Within-person differences in daily affect were, on average not related to meal and snack consumption. Between-person associations were independent of daily physical activity, sleep quality, caffeine consumption, and alcohol consumption, but were not independent of between-person differences in income. Discussion: The examination of repeated measures data to understand the relationship between affect and food consumption may address prior conflicting findings within these relationships by parsing trait and state levels of food intake.