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.

Validity and reliability of a new whole room indirect calorimeter to assess metabolic response to small calorie loads.

We overview of our whole room indirect calorimeter (WRIC), demonstrate validity and reliability of our WRIC, and explore a novel application of Bayesian hierarchical modeling to assess responses to small carbohydrate loads. To assess WRIC validity seven gas infusion studies were performed using a gas blender and profiles designed to mimic resting and postprandial metabolic events. Sixteen participants underwent fasting and postprandial measurements, during which they consumed a 75-kcal drink containing sucrose, dextrose, or fructose in a crossover design. Linear mixed effects models were used to compare resting and postprandial metabolic rate (MR) and carbohydrate oxidation. Postprandial carbohydrate oxidation trajectories for each participant and condition were modeled using Bayesian Hierarchical Modeling. Mean total error in infusions were 1.27 ± 0.67% and 0.42 ± 0.70% for VO2 and VCO2 respectively, indicating a high level of validity. Mean resting MR was similar across conditions ([Formula: see text] = 1.05 ± 0.03 kcal/min, p = 0.82, ICC: 0.91). While MR increased similarly among all conditions (~13%, p = 0.29), postprandial carbohydrate oxidation parameters were significantly lower for dextrose compared with sucrose or fructose. We provide evidence validating our WRIC and a novel application of statistical methods useful for research using WRIC.