title: Reduced-Item Food Audits Based on the Nutrition Environment Measures Surveys.
abstract: BACKGROUND
  The community food environment may contribute to obesity by influencing food choice. Store and restaurant audits are increasingly common methods for assessing food environments, but are time consuming and costly. A valid, reliable brief measurement tool is needed. The purpose of this study was to develop and validate reduced-item food environment audit tools for stores and restaurants.
  
  
  METHODS
  Nutrition Environment Measures Surveys for stores (NEMS-S) and restaurants (NEMS-R) were completed in 820 stores and 1,795 restaurants in West Virginia, San Diego, and Seattle. Data mining techniques (correlation-based feature selection and linear regression) were used to identify survey items highly correlated to total survey scores and produce reduced-item audit tools that were subsequently validated against full NEMS surveys. Regression coefficients were used as weights that were applied to reduced-item tool items to generate comparable scores to full NEMS surveys. Data were collected and analyzed in 2008-2013.
  
  
  RESULTS
  The reduced-item tools included eight items for grocery, ten for convenience, seven for variety, and five for other stores; and 16 items for sit-down, 14 for fast casual, 19 for fast food, and 13 for specialty restaurants-10% of the full NEMS-S and 25% of the full NEMS-R. There were no significant differences in median scores for varying types of retail food outlets when compared to the full survey scores. Median in-store audit time was reduced 25%-50%.
  
  
  CONCLUSIONS
  Reduced-item audit tools can reduce the burden and complexity of large-scale or repeated assessments of the retail food environment without compromising measurement quality.
authors.affiliation: Department of Animal and Nutritional Sciences and the Regional Research Institute, West Virginia University, Morgantown, West Virginia. Electronic address: susan.partington@mail.wvu.edu.
authors.forename: Susan N
authors.initials: SN
authors.lastname: Partington
authors.affiliation: Lane Department of Computer Sciences and Electrical Engineering, West Virginia University, Morgantown, West Virginia.
authors.forename: Tim J
authors.initials: TJ
authors.lastname: Menzies
authors.affiliation: Seattle Children's Research Institute, Seattle, Washington.
authors.forename: Trina A
authors.initials: TA
authors.lastname: Colburn
authors.affiliation: Seattle Children's Research Institute and the University of Washington School of Medicine, Seattle, Washington.
authors.forename: Brian E
authors.initials: BE
authors.lastname: Saelens
authors.affiliation: Perelman School of Medicine and School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Karen
authors.initials: K
authors.lastname: Glanz
mesh_terms: D002140:California; D002947:Cities / Q000706:statistics & numerical data; D004777:Environment; D005502:Food / Q000706:statistics & numerical data*; D005523:Food Supply / Q000706:statistics & numerical data*; D000069550:Machine Learning; D009749:Nutrition Surveys; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D012147:Restaurants / Q000706:statistics & numerical data*; D014861:Washington; D014903:West Virginia