TY - JOUR
T1 - Relative validity of a mobile AI-technology-assisted dietary assessment in adolescent females in Vietnam
AU - Nguyen, Phuong Hong
AU - Tran, Lan Mai
AU - Hoang, Nga Thu
AU - Truong, Duong Thuy Thi
AU - Tran, Trang Huyen Thi
AU - Huynh, Phuong Nam
AU - Koch, Bastien
AU - McCloskey, Peter
AU - Gangupantulu, Rohit
AU - Folson, Gloria
AU - Bannerman, Boateng
AU - Arrieta, Alejandra
AU - Braga, Bianca C.
AU - Arsenault, Joanne
AU - Kehs, Annalyse
AU - Doyle, Frank
AU - Hughes, David
AU - Gelli, Aulo
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press on behalf of the American Society for Nutrition.
PY - 2022/10/1
Y1 - 2022/10/1
N2 - Background: There 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. Objectives: We 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). Methods: Dietary 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. Results: Equivalence 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. Conclusions: AI-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.
AB - Background: There 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. Objectives: We 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). Methods: Dietary 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. Results: Equivalence 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. Conclusions: AI-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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U2 - 10.1093/ajcn/nqac216
DO - 10.1093/ajcn/nqac216
M3 - Article
C2 - 35945309
AN - SCOPUS:85139572329
SN - 0002-9165
VL - 116
SP - 992
EP - 1001
JO - American Journal of Clinical Nutrition
JF - American Journal of Clinical Nutrition
IS - 4
ER -