RESUMO
Background: Adolescent nutrition has faced a policy neglect, partly owing to the gaps in dietary intake data for this age group. The Food Recognition Assistance and Nudging Insights (FRANI) is a smartphone application validated for dietary assessment and to influence users toward healthy food choices. Objectives: This study aimed to assess the feasibility (adherence, acceptability, and usability) of FRANI and its effects on food choices and diet quality in female adolescents in Vietnam. Methods: Adolescents (N = 36) were randomly selected from a public school and allocated into 2 groups. The control group received smartphones with a version of FRANI limited to dietary assessment, whereas the intervention received smartphones with gamified FRANI. After the first 4 wk, both groups used gamified FRANI for further 2 wk. The primary outcome was the feasibility of using FRANI as measured by adherence (the proportion of completed food records), acceptability and usability (the proportion of participants who considered FRANI acceptable and usable according to answers of a Likert questionnaire). Secondary outcomes included the percentage of meals recorded, the Minimum Dietary Diversity for Women (MDDW) and the Eat-Lancet Diet Score (ELDS). Dietary diversity is important for dietary quality, and sustainable healthy diets are important to reduce carbon emissions. Poisson regression models were used to estimate the effect of gamified FRANI on the MDDW and ELDS. Results: Adherence to the application was 82% and the percentage of meals recorded was 97%. Acceptability and usability were 97%. MDDW in the intervention group was 1.07 points (95% CI: 0.98, 1.18; P = 0.13) greater than that in the control (constant = 4.68); however, the difference was not statistically significant. Moreover, ELDS in the intervention was 1.09 (95% CI: 1.01, 1.18; P = 0.03) points greater than in the control (constant = 3.67). Conclusions: FRANI was feasible and may be effective to influence users toward healthy food choices. Research is needed for FRANI in different contexts and at scale.The trial was registered at the International Standard Randomized Controlled Trial Number as ISRCTN 10681553.
RESUMO
BACKGROUND: Important gaps exist in the dietary intake of adolescents in low- and middle-income countries (LMICs), partly due to expensive assessment methods and inaccuracy in portion-size estimation. Dietary assessment tools leveraging mobile technologies exist but only a few have been validated in LMICs. OBJECTIVE: We validated Food Recognition Assistance and Nudging Insights (FRANI), a mobile artificial intelligence (AI) dietary assessment application in adolescent females aged 12-18 y (n = 36) in Ghana, against weighed records (WR), and multipass 24-hour recalls (24HR). METHODS: Dietary intake was assessed during 3 nonconsecutive days using FRANI, WRs, and 24HRs. Equivalence of nutrient intake was tested using mixed-effect models adjusted for repeated measures, by comparing ratios (FRANI/WR and 24HR/WR) with equivalence margins at 10%, 15%, and 20% error bounds. Agreement between methods was assessed using the concordance correlation coefficient (CCC). RESULTS: Equivalence for FRANI and WR was determined at the 10% bound for energy intake, 15% for 5 nutrients (iron, zinc, folate, niacin, and vitamin B6), and 20% for protein, calcium, riboflavin, and thiamine intakes. Comparisons between 24HR and WR estimated equivalence at the 20% bound for energy, carbohydrate, fiber, calcium, thiamine, and vitamin A intakes. The CCCs by nutrient between FRANI and WR ranged between 0.30 and 0.68, which was similar for CCC between 24HR and WR (ranging between 0.38 and 0.67). Comparisons of food consumption episodes from FRANI and WR found 31% omission and 16% intrusion errors. Omission and intrusion errors were lower when comparing 24HR with WR (21% and 13%, respectively). CONCLUSIONS: FRANI AI-assisted dietary assessment could accurately estimate nutrient intake in adolescent females compared with WR in urban Ghana. FRANI estimates were at least as accurate as those provided through 24HR. Further improvements in food recognition and portion estimation in FRANI could reduce errors and improve overall nutrient intake estimations.
