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

Nutrients

Shyang-Yun Pamela Koong Shiao¹  Zhao-Feng Chen²  Joyce D. Kusuma³  Hsiao-Ling Yang⁴  Ya-Ling Yang⁴ 

[1] Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA;Department of Nursing, Yuanpei University of Medical Technology, Hsinchu 30015, Taiwan;Heritage Victor Valley Medical Group, Big Bear Lake, CA 92315, USA;School of Nursing, College of Medicine, National Taiwan University, Taipei 10051, Taiwan;

关键词: personalized nutrition;    mobile applications (mobile app);    Food Frequency Questionnaire (FFQ);    modern diets;    generalized regression;    mHealth;   

DOI  :  10.3390/nu14030537

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown