A Class Act - Revising Food Classifications to Enable Automated Assessment of Compliance with Food-based Dietary Guidelines

Food classification systems make it easier to compare data from various sources and aids in the creation of public health guidelines. These systems can include a large amount of foods divided into various food groups and broader food categories.(1) Nutritics employs a standardised food categorisation system containing 114 unique categories. Food-based dietary guidelines (FBDG) categorise food types into general food groups to enable communication around amounts to consume daily basis to promote good health.(2) The correct classification of foods is essential to enable the implementation of an automated feature in Nutritics to assess compliance with FBDGs.This project aims to revise Nutritics categories associated with foods from the McCance and Widdowson’s Composition of Food Integrated Dataset (CoFID) – the food composition data applicable for use in the UK and Ireland. Also, to quantify the number of foods in each unique category and to calculate the quantity of foods that require a category update. The CoFID dataset, containing a total of 3,291 foods, was revised. Foods were assigned an appropriate category from one of the 114 Nutritics categories or remained in their original category. The number of foods in each Nutritics category originally and the number of foods in each new Nutritics category were recorded and compared to determine the total number of foods that required an update. Similar food categories were grouped together for analysis. Alcoholic beverages and baby foods were excluded from this study. A total of 3,237 foods and beverages from the dataset were analysed across 94 unique Nutritics categories. A total of 77 categories out of 94 had foods that required a category update (82%). The total number of foods that needed a category update was 1,045 (32%). The food category that needed the largest number of updates was the Vegetables-General group (n = 144). Thirteen categories contained only 1 food that required re-classification. The mean number of foods that needed a category update were calculated for each category grouping as follows: Protein categories (n = 27), 7 (0 min – 32 max); Starchy categories (n = 14), 6 (0 min – 22 max); Fruit and Vegetable categories (n = 10), 44 (4 min – 144 max); Dairy and Alternative categories (n = 9), 6 (0 min – 18 max); Other Dishes and Snacks categories (n = 6), 22 (0 min - 117 max); Desserts and Treats (n = 7), 16 (min 0/ max 85); Beverage categories (n = 10), 2 (min 0/ max 5); Miscellaneous categories (n = 11) 2 (0 min – max 6). The optimisation of foods assigned to a Nutritics category is necessary to enable appropriate rules to be applied to foods at a category level. Nutritics will use this optimised data to implement a feature to support users to automatically assess compliance with food group targets defined by FBDGs. 1. European Food Safety Authority (2015) [Available at: https://efsa.onlinelibrary.wiley.com/doi/pdf/10.2903/sp.efsa.2015.EN-804] 2. Herforth, A., Arimond, M., Álvarez-Sánchez, C., et al. (2019) Advances in Nutrition 10, 4, 590–60

Development and validation of a new methodological platform to measure behavioral, cognitive, and physiological responses to food interventions in real time

To fully understand the causes and mechanisms involved in overeating and obesity, measures of both cognitive and physiological determinants of eating behavior need to be integrated. Effectively synchronizing behavioral measures such as meal micro-structure (e.g., eating speed), cognitive processing of sensory stimuli, and metabolic parameters, can be complex. However, this step is central to understanding the impact of food interventions on body weight. In this paper, we provide an overview of the existing gaps in eating behavior research and describe the development and validation of a new methodological platform to address some of these issues. As part of a controlled trial, 76 men and women self-served and consumed food from a buffet, using a portion-control plate with visual stimuli for appropriate amounts of main food groups, or a conventional plate, on two different days, in a random order. In both sessions participants completed behavioral and cognitive tests using a novel methodological platform that measured gaze movement (as a proxy for visual attention), eating rate and bite size, memory for portion sizes, subjective appetite and portion-size perceptions. In a sub-sample of women, hormonal secretion in response to the meal was also measured. The novel platform showed a significant improvement in meal micro-structure measures from published data (13 vs. 33% failure rate) and high comparability between an automated gaze mapping protocol vs. manual coding for eye-tracking studies involving an eating test (ICC between methods 0.85; 90% CI 0.74, 0.92). This trial was registered at Clinical Trials.gov with Identifier NCT03610776

Development and validation of a new methodological platform to measure behavioral, cognitive, and physiological responses to food interventions in real time

To fully understand the causes and mechanisms involved in overeating and obesity, measures of both cognitive and physiological determinants of eating behavior need to be integrated. Effectively synchronizing behavioral measures such as meal micro-structure (e.g., eating speed), cognitive processing of sensory stimuli, and metabolic parameters, can be complex. However, this step is central to understanding the impact of food interventions on body weight. In this paper, we provide an overview of the existing gaps in eating behavior research and describe the development and validation of a new methodological platform to address some of these issues. As part of a controlled trial, 76 men and women self-served and consumed food from a buffet, using a portion-control plate with visual stimuli for appropriate amounts of main food groups, or a conventional plate, on two different days, in a random order. In both sessions participants completed behavioral and cognitive tests using a novel methodological platform that measured gaze movement (as a proxy for visual attention), eating rate and bite size, memory for portion sizes, subjective appetite and portion-size perceptions. In a sub-sample of women, hormonal secretion in response to the meal was also measured. The novel platform showed a significant improvement in meal micro-structure measures from published data (13 vs. 33% failure rate) and high comparability between an automated gaze mapping protocol vs. manual coding for eye-tracking studies involving an eating test (ICC between methods 0.85; 90% CI 0.74, 0.92). This trial was registered at Clinical Trials.gov with Identifier NCT03610776. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-021-01745-9