Mitigating errors in mobile-based dietary assessments: Effects of a data modification process on the validity of an image-assisted food and nutrition app

Dang Khanh Ngan Ho; Wan-Chun Chiu; Jing-Wen Kao; Hsiang-Tung Tseng; Chih-Yuan Yao; Hsiu-Yueh Su; Pin-Hui Wei; Nguyen Quoc Khanh Le; Hung Trong Nguyen; Jung-Su Chang

doi:10.1016/j.nut.2023.112212

Mitigating errors in mobile-based dietary assessments: Effects of a data modification process on the validity of an image-assisted food and nutrition app

Nutrition. 2023 Dec:116:112212. doi: 10.1016/j.nut.2023.112212. Epub 2023 Sep 9.

Authors

Affiliations

¹ School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan.
² School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan; Department of Nutrition, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
³ Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
⁴ School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan; Department of Dietetics, Taipei Medical University Hospital, Taipei, Taiwan.
⁵ Department of Nutrition, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
⁶ Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
⁷ Department of Adult Nutrition Counselling, National Institute of Nutrition, Hanoi, Vietnam; Department of Clinical Nutrition and Dietetics, National Hospital of Endocrinology, Hanoi, Vietnam.
⁸ School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Metabolism and Obesity Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei, Taiwan; Chinese Taipei Society for the Study of Obesity (CTSSO), Taipei, Taiwan; TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei, Taiwan. Electronic address: susanchang@tmu.edu.tw.

PMID: 37776838
DOI: 10.1016/j.nut.2023.112212

Abstract

Objective: Mobile nutrition applications (apps) provide a simple way for individuals to record their diet, but the validity and inherent errors need to be carefully evaluated. The aim of this study was to assess the validity and clarify the sources of measurement errors of image-assisted mobile nutrition apps.

Methods: This was a cross-sectional study with 98 students recruited from School of Nutrition and Health Sciences, Taipei Medical University. A 3-d nutrient intake record by Formosa Food and Nutrient Recording App (FoodApp) was compared with a 24-h dietary recall (24-HDR). A two-stage data modification process, manual data cleaning, and reanalyzing of prepackaged foods were employed to address inherent errors. Nutrient intake levels obtained by the two methods were compared with the recommended daily intake (DRI), Taiwan. Paired t test, Spearman's correlation coefficients, and Bland-Altman plots were used to assess agreement between the FoodApp and 24-HDR.

Results: Manual data cleaning identified 166 food coding errors (12%; stage 1), and 426 food codes with missing micronutrients (32%) were reanalyzed (stage 2). Positive linear trends were observed for total energy and micronutrient intake (all P_trend < 0.05) after the two stages of data modification, but not for dietary fat, carbohydrates, or vitamin D. There were no statistical differences in mean energy and macronutrient intake between the FoodApp and 24-HDR, and this agreement was confirmed by Bland-Altman plots. Spearman's correlation analyses showed strong to moderate correlations (r = 0.834 ∼ 0.386) between the two methods. Participants' nutrient intake tended to be lower than the DRI, but no differences in proportions of adequacy/inadequacy for DRI values were observed between the two methods.

Conclusions: Mitigating errors significantly improved the accuracy of the Formosa FoodApp, indicating its validity and reliability as a self-reporting mobile-based dietary assessment tool. Dietitians and health professionals should be mindful of potential errors associated with self-reporting nutrition apps, and manual data cleaning is vital to obtain reliable nutrient intake data.

Keywords: Food records; Mobile application; Mobile health; Smartphone application.

MeSH terms

Cross-Sectional Studies
Diet
Diet Records
Dietary Fats
Energy Intake
Humans
Mobile Applications*
Nutrition Assessment
Reproducibility of Results

Substances

Dietary Fats