ArticleThe American journal of clinical nutrition2022
Relative validity of a mobile AI-technology-assisted dietary assessment in adolescent females in Vietnam.
Article in The American journal of clinical nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Validity and accuracy of artificial intelligence-based dietary intake assessment methods: a systematic review.The British journal of nutrition · 2025Pooled it
- An Image-Recognition Dietary Assessment App for Adolescents With Obesity: Pilot Randomized Controlled Trial.JMIR formative research · 2024Trial
- Adolescent-Selected Food Intake and Engagement with the External Food Environment Contribute to Unhealthy Diets in Accra, Ghana.Current developments in nutrition · 2026Article
- Misreporting of energy intake estimated by food records among adolescents in Lao People's Democratic Republic: a cross-sectional study.BMC nutrition · 2026Article
- Digital and Technology-Enabled Approaches in Dietary Assessment: Addressing Bias, Error, and Feasibility in Population- and Community-Based Research.Advances in nutrition (Bethesda, Md.) · 2026Review
- Artificial Intelligence for Weight Management in Children: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Relative Validity of Interviewer-Administered 24-Hour Recalls Collected By Telephone and In-person Compared With Weighed Food Records Among Rural Sri Lankan Adults.Current developments in nutrition · 2026Article
- Artificial intelligence in child nutrition and eating behavior: from prediction to gastronomic mediation.Frontiers in nutrition · 2026Review
- Application of an Ecological Approach to Assessment of Nutrition in School-Aged Children-A Report from the "Biomarkers of Nutrition for Development: Knowledge Indicating Dietary Sufficiency (BOND-KIDS)" Project Working Group 3.The Journal of nutrition · 2025Review
- Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings.Nature communications · 2025Review
- Progress in Improving Low-Cost Measurement of Feeding Behaviors and Diets.Current developments in nutrition · 2025Article
- Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications.Frontiers in nutrition · 2025Review
- Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.Journal of medical Internet research · 2024Article
- Article
- Development of the Diet Quality Questionnaire for Measurement of Dietary Diversity and Other Diet Quality Indicators.Current developments in nutrition · 2024Article
- Feasibility of Using an Artificial Intelligence-based Telephone Application for Dietary Assessment and Nudging to Improve the Quality of Food Choices of Female Adolescents in Vietnam: Evidence from a Randomized Pilot Study.Current developments in nutrition · 2024Article
- Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention.Journal of personalized medicine · 2024Article
- Mobile Apps for Dietary and Food Timing Assessment: Evaluation for Use in Clinical Research.JMIR formative research · 2023Article
- A generic theory of change-based framework with core indicators for monitoring the effectiveness of large-scale food fortification programs in low- and middle-income countries.Frontiers in nutrition · 2023Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a gap in data on dietary intake of adolescents in low- and middle-income countries (LMICs). Traditional methods for dietary assessment are resource intensive and lack accuracy with regard to portion-size estimation. Technology-assisted dietary assessment tools have been proposed but few have been validated for feasibility of use in LMICs.
objectivesWe assessed 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 12-18 y in Vietnam, against a weighed records (WR) standard and compared FRANI performance with a multi-pass 24-h recall (24HR).
methodsDietary intake was assessed using 3 methods: FRANI, WR, and 24HRs undertaken on 3 nonconsecutive days. Equivalence of nutrient intakes was tested using mixed-effects 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.
resultsEquivalence between the FRANI app and WR was determined at the 10% bound for energy, protein, and fat and 4 nutrients (iron, riboflavin, vitamin B-6, and zinc), and at 15% and 20% bounds for carbohydrate, calcium, vitamin C, thiamin, niacin, and folate. Similar results were observed for differences between 24HRs and WR with a 20% equivalent bound for all nutrients except for vitamin A. The CCCs between FRANI and WR (0.60, 0.81) were slightly lower between 24HRs 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.
conclusionsAI-assisted dietary assessment and 24HRs accurately estimate nutrient intake in adolescent females when compared with WR. Errors could be reduced with further improvements in AI-assisted food recognition and portion estimation.
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