Predictors of nutrition label viewing during food purchase decision making: an eye tracking investigation

Public Health Nutr. 2012 Feb;15(2):189-97. doi: 10.1017/S1368980011001303. Epub 2011 Jul 7.

Abstract

Objective: Nutrition label use could help consumers eat healthfully. Despite consumers reporting label use, diets are not very healthful and obesity rates continue to rise. The present study investigated whether self-reported label use matches objectively measured label viewing by monitoring the gaze of individuals viewing labels.

Design: The present study monitored adults viewing sixty-four food items on a computer equipped with an eye-tracking camera as they made simulated food purchasing decisions. ANOVA and t tests were used to compare label viewing across various subgroups (e.g. normal weight v. overweight v. obese; married v. unmarried) and also across various types of foods (e.g. snacks v. fruits and vegetables).

Setting: Participants came to the University of Minnesota's Epidemiology Clinical Research Center in spring 2010.

Subjects: The 203 participants were ≥18 years old and capable of reading English words on a computer 76 cm (30 in) away.

Results: Participants looked longer at labels for 'meal' items like pizza, soup and yoghurt compared with fruits and vegetables, snack items like crackers and nuts, and dessert items like ice cream and cookies. Participants spent longer looking at labels for foods they decided to purchase compared with foods they decided not to purchase. There were few between-group differences in nutrition label viewing across sex, race, age, BMI, marital status, income or educational attainment.

Conclusions: Nutrition label viewing is related to food purchasing, and labels are viewed more when a food's healthfulness is ambiguous. Objectively measuring nutrition label viewing provides new insight into label use by various sociodemographic groups.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Body Mass Index
  • Choice Behavior
  • Computer Simulation
  • Consumer Health Information / statistics & numerical data*
  • Decision Making*
  • Educational Status
  • Eye Movements*
  • Female
  • Food Labeling*
  • Health Behavior
  • Health Knowledge, Attitudes, Practice
  • Humans
  • Male
  • Marital Status
  • Middle Aged
  • Nutritional Sciences / education
  • Public Health*