Purpose of Review The aim of this narrative review was to summarize and critique recent evidence evaluating the association between ultra-processed food intake and obesity. Recent Findings Four of five studies found that higher purchases or consumption of ultra-processed food was associated with overweight/obesity. Additional studies reported relationships between ultra-processed food intake and higher fasting glucose, metabolic syndrome, increases in total and LDL cholesterol, and risk of hypertension. It remains unclear whether associations can be attributed to processing itself or the nutrient content of ultra-processed foods. Only three of nine studies used a prospective design, and the potential for residual confounding was high. Summary Recent research provides fairly consistent support for the association of ultra-processed food intake with obesity and related cardiometabolic outcomes. There is a clear need for further studies, particularly those using longitudinal designs and with sufficient control for confounding, to potentially confirm these findings in different populations and to determine whether ultra-processed food consumption is associated with obesity independent of nutrient content.

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Background Adolescents’ consumption of healthy foods is suboptimal in low- and middle-income countries. Adolescents’ fondness for games and social media and the increasing access to smartphones make apps suitable for collecting dietary data and influencing their food choices. Little is known about how adolescents use phones to track and shape their food choices. Objective This study aimed to examine the acceptability, usability, and likability of a mobile phone app prototype developed to collect dietary data using artificial intelligence–based image recognition of foods, provide feedback, and motivate users to make healthier food choices. The findings were used to improve the design of the app. Methods A total of 4 focus group discussions (n=32 girls, aged 15-17 years) were conducted in Vietnam. Qualitative data were collected and analyzed by grouping ideas into common themes based on content analysis and ground theory. Results Adolescents accepted most of the individual- and team-based dietary goals presented in the app prototype to help them make healthier food choices. They deemed the overall app wireframes, interface, and graphic design as acceptable, likable, and usable but suggested the following modifications: tailored feedback based on users’ medical history, anthropometric characteristics, and fitness goals; new language on dietary goals; provision of information about each of the food group dietary goals; wider camera frame to fit the whole family food tray, as meals are shared in Vietnam; possibility of digitally separating food consumption on shared meals; and more appealing graphic design, including unique badge designs for each food group. Participants also liked the app’s feedback on food choices in the form of badges, notifications, and statistics. A new version of the app was designed incorporating adolescent’s feedback to improve its acceptability, usability, and likability. Conclusions A phone app prototype designed to track food choice and help adolescent girls from low- and middle-income countries make healthier food choices was found to be acceptable, likable, and usable. Further research is needed to examine the feasibility of using this technology at scale.

Background There is a gap in data on dietary intake of adolescents in low- and middle-income countries. Traditional methods for dietary assessment are resource intensive and lack accuracy with regards to portion size estimation. Technology assisted dietary assessment tools have been proposed but few have been validated for feasibility of use in LMICs. Objectives We assess the relative validity of FRANI (Food Recognition Assistance and Nudging Insights), a mobile Artificial Intelligence (AI) application for dietary assessment in adolescent females (n = 36) aged 12–18y in Vietnam, against weighed records (WR) standard, and compared FRANI performance to a multi-pass 24-hour recall (24HR). Methods Dietary intake was assessed using 3 methods: FRANI, WRs and 24HRs undertaken on three non-consecutive days. Equivalence of nutrient intakes was tested using mixed effect models adjusting for repeated measures, using 10%, 15% and 20% bounds. The concordance correlation coefficient (CCC) was used to assess the agreement between methods. Sources of errors were identified for memory and portion size estimation bias. Results Equivalence between FRANI app and WR was determined at the 10% bound for energy, protein and fat and four nutrients (iron, riboflavin, vitamin B6 and zinc), and at 15% and 20% bounds for carbohydrate, calcium, vitamin C, thiamin, niacin, and folate. Similar results were observed for differences between 24HR and WR with 20% equivalent bound for all nutrients except for vitamin A. The CCCs between FRANI and WR (0.60,0.81) were slightly lower CCCs between 24HR and WR (0.70,0.89) for energy and most nutrients. Memory error (food omissions or intrusions) was ∼21% with no clear pattern apparent on portion size estimation bias for foods. Conclusions AI assisted dietary assessment and 24HR accurately estimate nutrient intake in adolescent females when compared to WR. Errors could be reduced with further improvements of AI-assisted food recognition and portion estimation.