Development of MacroPics: A novel food picture set to dissociate the effects of carbohydrate and fat on eating behaviors

Creation and validation of a NOVA scored picture set to evaluate ultra-processed foods

There has been a rapid shift in the modern food environment towards increased processing in foods consumed in the United States (US) and globally. The NOVA system (not an acronym) for classifying food on degree of processing currently has the most empirical support. Consumption of foods in the NOVA 4 category, ultra-processed foods (UPF), is a risk factor for a host of poor health outcomes including heart disease, stroke, and cancer. Despite these poor health outcomes, UPF make up 58% of calories consumed in the US. Methodologies for assessing the reinforcing and rewarding properties of these foods are necessary tools. The Becker-DeGroot-Marschak auction paradigm (BDM) is a well validated tool for measuring value and is amenable to neuromonitoring environments. To allow for the testing of hypotheses based on level of food processing, we present a picture set of 14 UPF and 14 minimally-processed foods (MPF) matched on visual properties, food characteristics (fat, carbohydrate, cost, etc.), and rated perceptual properties. Further, we report our scoring of these foods using the NOVA classification system and provide additional data from credentialed nutrition professionals and on inter-rater reliability using NOVA, a critique of the system. Finally, we provide all pictures, data, and code used to create this picture set as a tool for researchers.

Evidence that carbohydrate-to-fat ratio and taste, but not energy density or NOVA level of processing, are determinants of food liking and food reward

This virtual (online) study tested the common but largely untested assumptions that food energy density, level of processing (NOVA categories), and carbohydrate-to-fat (CF) ratio are key determinants of food reward. Individual participants (224 women and men, mean age 35 y, 53% with healthy weight, 43% with overweight or obesity) were randomised to one of three, within-subjects, study arms: energy density (32 foods), or level of processing (24 foods), or CF ratio (24 foods). They rated the foods for taste pleasantness (liking), desire to eat (food reward), and sweetness, saltiness, and flavour intensity (for analysis averaged as taste intensity). Against our hypotheses, there was not a positive relationship between liking or food reward and either energy density or level of processing. As hypothesised, foods combining more equal energy amounts of carbohydrate and fat (combo foods), and foods tasting more intense, scored higher on both liking and food reward. Further results were that CF ratio, taste intensity, and food fibre content (negatively), independent of energy density, accounted for 56% and 43% of the variance in liking and food reward, respectively. We interpret the results for CF ratio and fibre in terms of food energy-to-satiety ratio (ESR), where ESR for combo foods is high, and ESR for high-fibre foods is low. We suggest that the metric of ESR should be considered when designing future studies of effects of food composition on food reward, preference, and intake.

What's on your plate? Collecting multimodal data to understand commensal behavior

Eating is a fundamental part of human life and is, more than anything, a social activity. A new field, known as Computational Commensality has been created to computationally address various social aspects of food and eating. This paper illustrates a study on remote dining we conducted online in May 2021. To better understand this phenomenon, known as Digital Commensality, we recorded 11 pairs of friends sharing a meal online through a videoconferencing app. In the videos, participants consume a plate of pasta while chatting with a friend or a family member. After the remote dinner, participants were asked to fill in the Digital Commensality questionnaire, a validated questionnaire assessing the effects of remote commensal experiences, and provide their opinions on the shortcomings of currently available technologies. Besides presenting the study, the paper introduces the first Digital Commensality Data-set, containing videos, facial landmarks, quantitative and qualitative responses. After surveying multimodal data-sets and corpora that we could exploit to understand commensal behavior, we comment on the feasibility of using remote meals as a source to build data-sets to investigate commensal behavior. Finally, we explore possible future research directions emerging from our results.

Fat and Carbohydrate Interact to Potentiate Food Reward in Healthy Weight but Not in Overweight or Obesity

Prior work suggests that actual, but not estimated, energy density drives the reinforcing value of food and that energy from fat and carbohydrate can interact to potentiate reward. Here we sought to replicate these findings in an American sample and to determine if the effects are influenced by body mass index (BMI). Thirty participants with healthy weight (HW; BMI 21.92 ± 1.77; M ± SD) and 30 participants with overweight/obesity (OW/OB; BMI 29.42 ± 4.44) rated pictures of common American snacks in 120-kcal portions for liking, familiarity, frequency of consumption, expected satiety, healthiness, energy content, energy density, and price. Participants then completed an auction task where they bid for the opportunity to consume each food. Snacks contained either primarily carbohydrate, primarily fat, or roughly equal portions of fat and carbohydrate (combo). Replicating prior work, we found that participants with HW bid the most for combo foods in linear mixed model analyses. This effect was not observed among individuals with OW/OB. Additionally, in contrast with previous reports, our linear regression analyses revealed a negative relationship between the actual energy density of the snacks and bid amount that was mediated by food price. Our findings support altered macronutrient reinforcement in obesity and highlight potential influences of the food environment on the regulation of food reward.