title: A generic coding approach for the examination of meal patterns.
abstract: BACKGROUND
  Meal pattern analysis can be complex because of the large variability in meal consumption. The use of aggregated, generic meal data may address some of these issues.
  
  
  OBJECTIVE
  The objective was to develop a meal coding system and use it to explore meal patterns.
  
  
  METHODS
  Dietary data were used from the National Adult Nutrition Survey (2008-2010), which collected 4-d food diary information from 1500 healthy adults. Self-recorded meal types were listed for each food item. Common food group combinations were identified to generate a number of generic meals for each meal type: breakfast, light meals, main meals, snacks, and beverages. Mean nutritional compositions of the generic meals were determined and substituted into the data set to produce a generic meal data set. Statistical comparisons were performed against the original National Adult Nutrition Survey data. Principal component analysis was carried out by using these generic meals to identify meal patterns.
  
  
  RESULTS
  A total of 21,948 individual meals were reduced to 63 generic meals. Good agreement was seen for nutritional comparisons (original compared with generic data sets mean ± SD), such as fat (75.7 ± 29.4 and 71.7 ± 12.9 g, respectively, P = 0.243) and protein (83.3 ± 26.9 and 80.1 ± 13.4 g, respectively, P = 0.525). Similarly, Bland-Altman plots demonstrated good agreement (<5% outside limits of agreement) for many nutrients, including protein, saturated fat, and polyunsaturated fat. Twelve meal types were identified from the principal component analysis ranging in meal-type inclusion/exclusion, varying in energy-dense meals, and differing in the constituents of the meals.
  
  
  CONCLUSIONS
  A novel meal coding system was developed; dietary intake data were recoded by using generic meal consumption data. Analysis revealed that the generic meal coding system may be appropriate when examining nutrient intakes in the population. Furthermore, such a coding system was shown to be suitable for use in determining meal-based dietary patterns.
authors.affiliation: Institute of Food and Health, University College Dublin, Belfield, Ireland.
authors.forename: Clara
authors.identifier: http://orcid.org/0000-0001-6323-0012
authors.initials: C
authors.lastname: Woolhead
authors.affiliation: Institute of Food and Health, University College Dublin, Belfield, Ireland.
authors.forename: Michael J
authors.identifier: http://orcid.org/0000-0001-5250-361X
authors.initials: MJ
authors.lastname: Gibney
authors.affiliation: Institute of Food and Health, University College Dublin, Belfield, Ireland.
authors.forename: Marianne C
authors.identifier: http://orcid.org/0000-0002-6651-2434
authors.initials: MC
authors.lastname: Walsh
authors.affiliation: Institute of Food and Health, University College Dublin, Belfield, Ireland.
authors.forename: Lorraine
authors.identifier: http://orcid.org/0000-0002-7711-7499
authors.initials: L
authors.lastname: Brennan
authors.affiliation: Institute of Food and Health, University College Dublin, Belfield, Ireland eileen.gibney@ucd.ie.
authors.forename: Eileen R
authors.identifier: http://orcid.org/0000-0001-9465-052X
authors.initials: ER
authors.lastname: Gibney
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D059019:Clinical Coding; D004032:Diet / Q000208:ethnology; D015930:Diet Records; D002149:Energy Intake / Q000208:ethnology; D005260:Female; D006801:Humans; D007494:Ireland; D008297:Male; D062407:Meals / Q000145:classification* / Q000208:ethnology; D008875:Middle Aged; D008954:Models, Biological; D009749:Nutrition Surveys; D025341:Principal Component Analysis; D055815:Young Adult
keywords: dietary patterns; food combinations; generic meal; meal coding; meal pattern