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. DESIGN: 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.
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. DESIGN: 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: Claire M Timon; Katie Evans; Laura Kehoe; Richard J Blain; Albert Flynn; Eileen R Gibney; Janette Walton Journal: Nutrients Date: 2017-04-25 Impact factor: 5.717
Authors: Linda Englund-Ögge; Bryndis Eva Birgisdottir; Verena Sengpiel; Anne Lise Brantsæter; Margareta Haugen; Ronny Myhre; Helle Margrete Meltzer; Bo Jacobsson Journal: PLoS One Date: 2017-03-01 Impact factor: 3.240
Authors: Irina Uzhova; Clara Woolhead; Claire M Timon; Aifric O'Sullivan; Lorraine Brennan; José L Peñalvo; Eileen R Gibney Journal: Nutrients Date: 2018-03-06 Impact factor: 5.717