Ho Dang Khanh Ngan, Chiu Wan-Chun, Kao Jing-Wen, Tseng Hsiang-Tung, Yao Chih-Yuan, Su Hsiu-Yueh, Wei Pin-Hui, Le Nguyen Quoc Khanh, Nguyen Hung Trong, Chang Jung-Su
School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan.
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.
Nutrition. 2023 Dec;116:112212. doi: 10.1016/j.nut.2023.112212. Epub 2023 Sep 9.
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.
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.
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 < 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.
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.
移动营养应用程序（应用）为个人记录饮食提供了一种简单的方式，但需要仔细评估其有效性和固有误差。本研究的目的是评估图像辅助移动营养应用的有效性，并阐明测量误差的来源。
这是一项横断面研究，从台北医学大学营养与健康科学学院招募了98名学生。将福尔摩沙食物与营养记录应用程序（FoodApp）记录的3天营养摄入量与24小时膳食回顾（24-HDR）进行比较。采用两阶段数据修正过程、手动数据清理以及对预包装食品进行重新分析来处理固有误差。将两种方法获得的营养摄入量水平与台湾的每日推荐摄入量（DRI）进行比较。采用配对t检验、Spearman相关系数和Bland-Altman图来评估FoodApp与24-HDR之间的一致性。
手动数据清理识别出166个食物编码错误（12%；第1阶段），并对426个缺少微量营养素的食物编码（32%）进行了重新分析（第2阶段）。经过两阶段的数据修正后，观察到总能量和微量营养素摄入量呈正线性趋势（所有P<0.05），但膳食脂肪、碳水化合物或维生素D的摄入量未呈现此趋势。FoodApp与24-HDR之间的平均能量和常量营养素摄入量无统计学差异，Bland-Altman图证实了这种一致性。Spearman相关分析显示两种方法之间存在强到中度的相关性（r=0.834～0.386）。参与者的营养摄入量往往低于DRI，但两种方法之间在DRI值充足/不足比例方面未观察到差异。
减少误差显著提高了福尔摩沙FoodApp的准确性，表明其作为基于移动设备的自我报告膳食评估工具的有效性和可靠性。营养师和健康专业人员应注意与自我报告营养应用相关的潜在误差，手动数据清理对于获得可靠的营养摄入量数据至关重要。
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