Komal Manerkar, J. Harding, C. Conlon, C. McKinlay
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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","PeriodicalId":43030,"journal":{"name":"International Journal of Child Health and Nutrition","volume":" ","pages":""},"PeriodicalIF":0.1000,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and Evaluation of an Automated Algorithm to Estimate the Nutrient Intake of Infants from an Electronic Complementary Food Frequency Questionnaire\",\"authors\":\"Komal Manerkar, J. Harding, C. Conlon, C. McKinlay\",\"doi\":\"10.6000/1929-4247.2020.09.04.1\",\"DOIUrl\":null,\"url\":null,\"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. 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引用次数: 0
摘要
背景:我们之前验证了一项为期四天的辅食频率问卷(CFFQ),以估计新西兰9-12个月婴儿的营养摄入量。然而,手工将CFFQ数据输入营养分析软件非常耗时。因此,我们开发了一种自动化算法,并通过将营养估算值与营养分析软件获得的数据进行比较来评估其准确性。方法:采用Food Works营养分析软件对50例9月和12月完成的CFFQ进行分析。通过将每种食物的平均每日消耗量乘以每份食物的营养含量,自动算法在SAS中编程。我们考虑了最常见的商业准备婴儿食品品牌。使用Bland-Altman分析方法比较了能量、常量营养素和微量营养素的摄入量。结果:自动算法对能量(kJ) (MD 15, 95% CI - 27,000, 58)、碳水化合物(g) (MD -0.1, 95% CI -1.2,1.0)和脂肪(g) (MD -0.1, 95% CI -0.3,0.1)的估计没有任何显著偏差,但稍微低估了蛋白质(MD -0.4 g, 95% CI -0.7,-0.1)、饱和脂肪、PUFA、膳食纤维和烟酸的摄入量。该算法提供了对其他微量营养素的准确估计。协议的范围相对狭窄。结论:该算法是一种准确估计猪粪营养摄入量的有效工具。对少数营养物质观察到的小负偏倚在临床上是不显著的,可以最小化。该算法是合适的
Development and Evaluation of an Automated Algorithm to Estimate the Nutrient Intake of Infants from an Electronic Complementary Food Frequency Questionnaire
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