Abstract
Background: We previously validated a four-day complementary food frequency questionnaire (CFFQ) to estimate the nutrient intake in New Zealand infants aged 9-12 months. However, manual entry of the CFFQ data into nutritional analysis software was time-consuming. Therefore, we developed an automated algorithm and evaluated its accuracy by comparing the nutrient estimates with those obtained from the nutritional analysis software. Methods: We analysed 50 CFFQ completed at 9- and 12-months using Food Works nutritional analysis software. The automated algorithm was programmed in SAS by multiplying the average daily consumption of each food item by the nutrient content of the portion size. We considered the most common brands for commercially prepared baby foods. Intakes of energy, macronutrients, and micronutrients were compared between methods using Bland-Altman analysis. Results: The automated algorithm did not have any significant bias for estimates of energy (kJ) (MD 15, 95% CI -27, 58), carbohydrate (g) (MD -0.1, 95% CI -1.2,1.0), and fat (g) (-0.1, 95% CI -0.3,0.1), but slightly underestimated intake of protein (MD -0.4 g, 95% CI -0.7,-0.1), saturated fat, PUFA, dietary fibre, and niacin. The algorithm provided accurate estimates for other micronutrients. The limits of agreement were relatively narrow. Conclusion: This automated algorithm is an efficient tool to estimate the nutrient intakes from CFFQ accurately. The small negative bias observed for few nutrients was clinically insignificant and can be minimised. This algorithm is suitabl
Highlights
Nutrition in the first year can have a profound impact on later metabolic health
complementary food frequency questionnaire (CFFQ) data were obtained from participants in the BabyGEMS Study, a prospective cohort study of a subgroup of infants born to women enrolled in the ongoing Gestational Diabetes Mellitus Study of
We found that the automated algorithm provided accurate estimates, compared with nutritional analysis software, for the intake of energy and most macronutrients and micronutrients, except for a slight negative bias in estimating protein (0.4 g), saturated fat (0.1 g), poly-unsaturated fatty acid (PUFA) (0.1 g), dietary fibre (0.3 g) and niacin (0.5 g)
Summary
Nutrition in the first year can have a profound impact on later metabolic health. Both under- and overnutrition may increase the risk of obesity in childhood, and insulin resistance, impaired glucose tolerance, hypertension, and dyslipidaemia in adulthood [1,2,3,4,5]. Assessing dietary intake in infants can be challenging due to the different types of milk consumed, the transition from milk feeding to complementary feeding, and wide variations in feeding patterns. We previously validated a four-day complementary food frequency questionnaire (CFFQ) to estimate the nutrient intake in New Zealand infants aged 9-12 months. We developed an automated algorithm and evaluated its accuracy by comparing the nutrient estimates with those obtained from the nutritional analysis software
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