Reproducibility and validity of a 45 item food-frequency questionnaire for inuit in Greenland

Since 1993, dietary assessment has been carried out in Greenland as part of recurrent population health surveys. In preparation for the next survey in 2024, 91 participants from the survey in 2018 were selected for a validation study of the Food Frequency Questionnaire (FFQ). The 91 participants were reinterviewed 38-50 months after the first FFQ and invited to complete a food diary. As part of the 2018 survey, blood was analysed for mercury. The food diary was completed by 65 participants. The agreement between the two FFQ interviews was very good for macronutrients and fatty acids (p > 0.20), whereas the calculated intake of mercury was 22% higher in the second FFQ (p = 0.04) due to a higher intake of whale meat and muktuk (whale skin). The agreement between the second FFQ and the food diary was good for local food, imported meat and cakes/sweets/snacks but fruit and vegetables, dairy products, beverages and added sugar were significantly underreported in the food diary. Food items not included in the FFQ were identified from the food diaries. The correlation between the intake of marine mammals and blood mercury was moderate (Spearman's rho = 0.41-0.50; p < 0.0001). The results will inspire future dietary studies in the circumpolar North.

Validation of Mobile Artificial Intelligence Technology-Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana

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.

Validation of 24-h dietary recall for estimating nutrient intakes and adequacy in adolescents in Burkina Faso

Data on dietary nutrient intakes of adolescents in low‐ and middle‐income countries (LMIC) is lacking partly due to the absence of validation studies of the 24‐h recall method in adolescents. We conducted a validation study of 24‐h recall (24HR) compared with observed weighed records (OWR) in adolescents (n = 132, 10–11 years; n = 105, 12–14 years). Dietary data were collected for the same day by both methods by conducting the 24HR the day after the OWR. For OWR, all foods consumed by adolescents from the first to last meal of the day were weighed; for 24HR adolescents reported foods consumed using portion aids. Food intakes were converted to nutrients. Nutrient intakes by both methods were tested for equivalence by comparing the ratios (24HR/OWR) with equivalence margins of within ±10%, 15% and 20% of the ratio. Prevalences of inadequacy (POIs) were obtained using the NCI method. Mean ratios for energy were 0.88 and 0.92, for younger and older adolescents, respectively, and other nutrients ranged between 0.84 and 1.02. Energy intakes were equivalent within the 15% bound, and most nutrients fell within the 20% bound. POI was overestimated by 24HR, but differences were less than 25 percentage points for most nutrients. Half of adolescents omitted foods in recalls, mainly sweet or savoury snacks, fruits and beverages. Our study showed that adolescents underestimated intakes by 24HR; however, the degree of underestimation was generally acceptable for 12–14‐year‐olds within a bound of 15%. Errors could possibly be reduced with further training and targeted probing.

Can a Simple Dietary Index Derived from a Sub-Set of Questionnaire Items Assess Diet Quality in a Sample of Australian Adults?

Large, longitudinal surveys often lack consistent dietary data, limiting the use of existing tools and methods that are available to measure diet quality. This study describes a method that was used to develop a simple index for ranking individuals according to their diet quality in a longitudinal study. The RESIDential Environments (RESIDE) project (2004-2011) collected dietary data in varying detail, across four time points. The most detailed dietary data were collected using a 24-item questionnaire at the final time point (n = 555; age ≥ 25 years). At preceding time points, sub-sets of the 24 items were collected. A RESIDE dietary guideline index (RDGI) that was based on the 24-items was developed to assess diet quality in relation to the Australian Dietary Guidelines. The RDGI scores were regressed on the longitudinal sub-sets of six and nine questionnaire items at T4, from which two simple index scores (S-RDGI1 and S-RDGI2) were predicted. The S-RDGI1 and S-RDGI2 showed reasonable agreement with the RDGI (Spearman's rho = 0.78 and 0.84; gross misclassification = 1.8%; correct classification = 64.9% and 69.7%; and, Cohen's weighted kappa = 0.58 and 0.64, respectively). For all of the indices, higher diet quality was associated with being female, undertaking moderate to high amounts of physical activity, not smoking, and self-reported health. The S-RDGI1 and S-RDGI2 explained 62% and 73% of the variation in RDGI scores, demonstrating that a large proportion of the variability in diet quality scores can be captured using a relatively small sub-set of questionnaire items. The methods described in this study can be applied elsewhere, in situations where limited dietary data are available, to generate a sample-specific score for ranking individuals according to diet quality.

Comparison of Australian Recommended Food Score (ARFS) and Plasma Carotenoid Concentrations: A Validation Study in Adults

Diet quality indices can predict nutritional adequacy of usual intake, but validity should be determined. The aim was to assess the validity of total and sub-scale score within the Australian Recommended Food Score (ARFS), in relation to fasting plasma carotenoid concentrations. Diet quality and fasting plasma carotenoid concentrations were assessed in 99 overweight and obese adults (49.5% female, aged 44.6 ± 9.9 years) at baseline and after three months (198 paired observations). Associations were assessed using Spearman’s correlation coefficients and regression analysis, and agreement using weighted kappa (K_w). Small, significantly positive correlations were found between total ARFS and plasma concentrations of total carotenoids (r = 0.17, p < 0.05), β-cryptoxanthin (r = 0.18, p < 0.05), β-carotene (r = 0.20, p < 0.01), and α-carotene (r = 0.19, p < 0.01). Significant agreement between ARFS categories and plasma carotenoid concentrations was found for total carotenoids (K_w 0.12, p = 0.02), β-carotene (K_w 0.14, p < 0.01), and α-carotene (K_w 0.13, p < 0.01). In fully-adjusted regression models the only signification association with ARFS total score was for α-carotene (β = 0.19, p < 0.01), while ARFS meat and fruit sub-scales demonstrated significant relationships with α-carotene, β-carotene, and total carotenoids (p < 0.05). The weak associations highlight the issues with self-reporting dietary intakes in overweight and obese populations. Further research is required to evaluate the use of the ARFS in more diverse populations.