Measures of the food environment: A systematic review of the field, 2007-2015

Health Place. 2017 Mar;44:18-34. doi: 10.1016/j.healthplace.2016.12.007. Epub 2017 Jan 27.


Background: Many studies have examined the relationship between the food environment and health-related outcomes, but fewer consider the integrity of measures used to assess the food environment. The present review builds on and makes comparisons with a previous review examining food environment measures and expands the previous review to include a more in depth examination of reliability and validity of measures and study designs employed.

Methods: We conducted a systematic review of studies measuring the food environment published between 2007 and 2015. We identified these articles through: PubMed, Embase, Web of Science, PsycINFO, and Global Health databases; tables of contents of relevant journals; and the National Cancer Institute's Measures of the Food Environment website. This search yielded 11,928 citations. We retained and abstracted data from 432 studies.

Results: The most common methodology used to study the food environment was geographic analysis (65% of articles) and the domination of this methodology has persisted since the last review. Only 25.9% of studies in this review reported the reliability of measures and 28.2% reported validity, but this was an improvement as compared to the earlier review. Very few of the studies reported construct validity. Studies reporting measures of the school or worksite environment have decreased since the previous review. Only 13.9% of the studies used a longitudinal design.

Conclusions: To strengthen research examining the relationship between the food environment and population health, there is a need for robust and psychometrically-sound measures and more sophisticated study designs.

Keywords: Food environment; Measurement; Psychometric properties.

Publication types

  • Review
  • Systematic Review

MeSH terms

  • Environment*
  • Feeding Behavior
  • Food*
  • Geographic Information Systems / statistics & numerical data*
  • Humans
  • Psychometrics / statistics & numerical data*
  • Reproducibility of Results
  • Research Design