School of Human Nutrition, McGill University, Montreal, QC, Canada.
Department of Health, Kinesiology, and Applied Physiology, Concordia University, Montreal, QC, Canada.
JMIR Mhealth Uhealth. 2020 Sep 9;8(9):e16953. doi: 10.2196/16953.
Accurate dietary assessment is needed in studies that include analysis of nutritional intake. Image-based dietary assessment apps have gained in popularity for assessing diet, which may ease researcher and participant burden compared to traditional pen-to-paper methods. However, few studies report the validity of these apps for use in research. Keenoa is a smartphone image-based dietary assessment app that recognizes and identifies food items using artificial intelligence and permits real-time editing of food journals.
This study aimed to assess the relative validity of an image-based dietary assessment app - Keenoa - against a 3-day food diary (3DFD) and to test its usability in a sample of healthy Canadian adults.
We recruited 102 participants to complete two 3-day food records. For 2 weeks, on 2 non-consecutive days and 1 weekend day, in random order, participants completed a traditional pen-to-paper 3DFD and the Keenoa app. At the end of the study, participants completed the System Usability Scale. The nutrient analyses of the 3DFD and Keenoa data before (Keenoa-participant) and after they were reviewed by dietitians (Keenoa-dietitian) were analyzed using analysis of variance. Multiple tests, including the Pearson coefficient, cross-classification, kappa score, % difference, paired t test, and Bland-Altman test, were performed to analyze the validity of Keenoa (Keenoa-dietitian).
The study was completed by 72 subjects. Most variables were significantly different between Keenoa-participant and Keenoa-dietitian (P<.05) except for energy, protein, carbohydrates, fiber, vitamin B1, vitamin B12, vitamin C, vitamin D, and potassium. Significant differences in total energy, protein, carbohydrates, % fat, saturated fatty acids, iron, and potassium were found between the 3DFD and Keenoa-dietitian data (P<.05). The Pearson correlation coefficients between the Keenoa-dietitian and 3DFD ranged from .04 to .51. Differences between the mean intakes assessed by the 3DFD and Keenoa-dietitian were within 10% except for vitamin D (misclassification rate=33.8%). The majority of nutrients were within an acceptable range of agreement in the Bland-Altman analysis; no agreements were seen for total energy, protein, carbohydrates, fat (%), saturated fatty acids, iron, potassium, and sodium (P<.05). According to the System Usability Scale, 34.2% of the participants preferred using Keenoa, while 9.6% preferred the 3DFD.
The Keenoa app provides acceptable relative validity for some nutrients compared to the 3DFD. However, the average intake of some nutrients, including energy, protein, carbohydrates, % fat, saturated fatty acids, and iron, differed from the average obtained using the 3DFD. These findings highlight the importance of verifying data entries of participants before proceeding with nutrient analysis. Overall, Keenoa showed better validity at the group level than the individual level, suggesting it can be used when focusing on the dietary intake of the general population. Further research is recommended with larger sample sizes and objective dietary assessment approaches.
在包含营养摄入量分析的研究中,需要进行准确的饮食评估。基于图像的饮食评估应用程序在评估饮食方面越来越受欢迎,与传统的纸笔方法相比,可能会减轻研究人员和参与者的负担。然而,很少有研究报告这些应用程序在研究中的有效性。Keenoa 是一款智能手机基于图像的饮食评估应用程序,它使用人工智能识别和识别食物,并允许实时编辑食物日记。
本研究旨在评估基于图像的饮食评估应用程序 - Keenoa - 的相对有效性,该应用程序与 3 天饮食记录 (3DFD) 进行比较,并在加拿大健康成年人样本中测试其可用性。
我们招募了 102 名参与者完成两份 3 天的饮食记录。在 2 周的时间里,参与者随机在 2 天非连续日和 1 个周末日完成传统的纸笔 3DFD 和 Keenoa 应用程序。在研究结束时,参与者完成了系统可用性量表。在营养师审查之前(Keenoa-参与者)和之后(Keenoa-营养师)对 3DFD 和 Keenoa 数据进行了分析,使用方差分析进行分析。进行了多次测试,包括 Pearson 系数、交叉分类、kappa 评分、%差异、配对 t 检验和 Bland-Altman 检验,以分析 Keenoa(Keenoa-营养师)的有效性。
72 名受试者完成了研究。除了能量、蛋白质、碳水化合物、纤维、维生素 B1、维生素 B12、维生素 C、维生素 D 和钾外,Keenoa-参与者和 Keenoa-营养师之间的大多数变量差异均具有统计学意义(P<.05)。3DFD 和 Keenoa-营养师数据之间存在总能量、蛋白质、碳水化合物、%脂肪、饱和脂肪酸、铁和钾的显著差异(P<.05)。Keenoa-营养师和 3DFD 之间的 Pearson 相关系数范围为.04 至.51。除了维生素 D(错误分类率=33.8%)外,3DFD 和 Keenoa-营养师评估的平均摄入量差异在 10% 以内。在 Bland-Altman 分析中,大多数营养素都在可接受的一致性范围内;总能量、蛋白质、碳水化合物、脂肪(%)、饱和脂肪酸、铁、钾和钠没有一致性(P<.05)。根据系统可用性量表,34.2%的参与者更喜欢使用 Keenoa,而 9.6%的参与者更喜欢 3DFD。
与 3DFD 相比,Keenoa 应用程序对某些营养素提供了可接受的相对有效性。然而,一些营养素的平均摄入量,包括能量、蛋白质、碳水化合物、%脂肪、饱和脂肪酸和铁,与使用 3DFD 获得的平均摄入量不同。这些发现强调了在进行营养素分析之前验证参与者数据输入的重要性。总体而言,Keenoa 在群体水平上的有效性优于个体水平,这表明它可以在关注一般人群的饮食摄入量时使用。建议进一步开展研究,扩大样本量并采用客观的饮食评估方法。