From Theory to Application: The Measurement of Dietary Decisions, Intentions, and Behavior Open Access
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To improve health outcomes and address the obesity epidemic through dietary change, it is essential to understand how individuals process information related to dietary decisions to intervene or change these decisions and subsequent behavior. A fundamental aspect to the understanding of dietary decision-making involves the measurement of the process by which individuals come to make these decisions, and the constructs within it. Within this challenge are at least three central questions, which are addressed in three papers (Chapters 2 – 4). First, what is the process by which dietary decisions are determined? Second, how (if at all) can the measurement of the constructs involved in the decision-making process affect the predictive utility of these constructs to predict eating-related intentions and behavior? Finally, and arguably most fundamental to eating behavior research, how should we measure eating behavior? Specifically, how can we use technological advances to increase the validity of eating behavior assessments? The first study of this dissertation (Chapter 2) uses functional measurement to empirically test how expectancies and values are cognitively integrated to determine eating-related intentions and provides evidence to suggest that the cognitive process of integrating expectancies and values related to eating behavior may obey an additive or averaging, as opposed to multiplicative, integration model of expectancies and values related to eating behavior. The second study of this dissertation (Chapter 3) varied aggregation and scaling of expectancies and values to assess their relative effects on intentions and behaviors for two types of dietary behavior (i.e., fruit & vegetable consumption and junk food restriction) within the framework of the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB) models. The results from Chapter 3 demonstrate that differential aggregation, scaling and eating-related behaviors can have profound effects on the ability of EV and non-EV factors to predict eating behavior-related outcomes. Finally, Chapter 4 adds to the burgeoning literature on the usability of digital technology to measure dietary intake in community-based participatory research (CBPR) by demonstrating that a digital food record (DFR) is an acceptable tool in CBPR and identifying contributors and barriers to DFR feasibility for future validation research. Increasing knowledge of the usability of digital technology to measure dietary intake in CBPR, combined with heightened understanding of dietary decision making, could provide opportunities to tailor methods to the needs of the community, improve interventions that promote healthy eating, reduce obesity-related health disparities, and improve health outcomes among at-risk populations.