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. 2022 Jan 26;14(3):537.
doi: 10.3390/nu14030537.

Validating Accuracy of a Mobile Application against Food Frequency Questionnaire on Key Nutrients with Modern Diets for mHealth Era

Joyce D Kusuma  1   2 , Hsiao-Ling Yang  3 , Ya-Ling Yang  3 , Zhao-Feng Chen  4 , Shyang-Yun Pamela Koong Shiao  5
Affiliations

Validating Accuracy of a Mobile Application against Food Frequency Questionnaire on Key Nutrients with Modern Diets for mHealth Era

Joyce D Kusuma et al. Nutrients. .

Abstract

In preparation for personalized nutrition, an accurate assessment of dietary intakes on key essential nutrients using smartphones can help promote health and reduce health risks across vulnerable populations. We, therefore, validated the accuracy of a mobile application (app) against Food Frequency Questionnaire (FFQ) using artificial intelligence (AI) machine-learning-based analytics, assessing key macro- and micro-nutrients across various modern diets. We first used Bland and Altman analysis to identify and visualize the differences between the two measures. We then applied AI-based analytics to enhance prediction accuracy, including generalized regression to identify factors that contributed to the differences between the two measures. The mobile app underestimated most macro- and micro-nutrients compared to FFQ (ranges: -5% for total calories, -19% for cobalamin, -33% for vitamin E). The average correlations between the two measures were 0.87 for macro-nutrients and 0.84 for micro-nutrients. Factors that contributed to the differences between the two measures using total calories as an example, included caloric range (1000-2000 versus others), carbohydrate, and protein; for cobalamin, included caloric range, protein, and Chinese diet. Future studies are needed to validate actual intakes and reporting of various diets, and to examine the accuracy of mobile App. Thus, a mobile app can be used to support personalized nutrition in the mHealth era, considering adjustments with sources that could contribute to the inaccurate estimates of nutrients.

Keywords: Food Frequency Questionnaire (FFQ); agreement and bias; dietary record; generalized regression; mHealth; mobile applications (mobile app); modern diets; personalized nutrition.

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Conflict of interest statement

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1
Figure 1
(a) Correlation, (b) Bland and Altman plots between mobile application and Food Frequency Questionnaire (FFQ) on total calories.
Figure 2
Figure 2
(a) Correlation, (b) Bland and Altman plots between mobile application and Food Frequency Questionnaire (FFQ) for fat.
Figure 3
Figure 3
(a) Correlation, (b) Bland and Altman plots between mobile application and Food Frequency Questionnaire (FFQ) for folate.
Figure 4
Figure 4
(a) Correlation, (b) Bland and Altman plots between mobile applications and Food Frequency Questionnaire (FFQ) for cobalamin.
Figure 5
Figure 5
Predicting accuracy of total calories analyses using mobile application against Food Frequency Questionnaire: Area under the receiver operating characteristic curve (AUC) for baseline logistic regression model (a) and Elastic Net with validation model (b).
See this image and copyright information in PMC

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