Innovative ways of understanding and measuring eating behavior
Authors
G. Sproesser
M. Stok
Abstract
Aims: This symposium aims, firstly, to present state-of-the-art
research in the domain of understanding the complex behavior of food and drink intake, and,
secondly, to provide health psychologists with an overview of innovative methods of capturing
and measuring eating behavior. Relevance: Unhealthy eating behavior contributes substantially
to excess body weight. With the prevalence of overweight and obesity continually increasing, it
is of crucial importance to find better ways to understand and assess the complex and
multifaceted behavior of eating. Overview: The first two presentations in this symposium
describe new ways of understanding eating behavior. Marijn Stok (University of Konstanz) will
present research from the European DEDIPAC project, in which the immense diversity of outcomes
belonging to the fuzzy umbrella term of ‘eating behavior’ is captured. Second, Astrid Junghans
(Utrecht University) will present results from a study on choice blindness, demonstrating a new
way of investigating how people make decisions about the foods they want to eat. Presentations
three and four demonstrate innovative methods of measuring eating behavior in the laboratory
and real world. Gudrun Sproesser (University of Konstanz) will present findings on the food
choices people make for themselves and others. These findings were obtained using a buffet of
fake foods, a recently developed and highly promising new method of assessing eating behavior.
The fourth presentation will show how new media can be used effectively both to measure, as
well as to intervene on eating behavior. Jennifer Inauen (Columbia University) will present a
study employing ecological momentary assessment to decrease unhealthy snacking. People were
asked to keep a photo diary of the unhealthy snacks they consumed. Finally, Laura König
(University of Konstanz) will show how different methods of assessing eating behavior result in
the finding of different predicting variables.