Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA.
Société des Produits Nestlé, Nestlé Research Center, Lausanne, Switzerland.
J Nutr. 2021 May 11;151(5):1347-1356. doi: 10.1093/jn/nxab012.
Accurate measurement of dietary intake is vital for providing nutrition interventions and understanding the complex role of diet in health. Traditional dietary assessment methods are very resource intensive and burdensome to participants. Technology may help mitigate these limitations and improve dietary data capture.
Our objective was to evaluate the accuracy of a novel mobile application (PIQNIQ) in capturing dietary intake by self-report. Our secondary objective was to assess whether food capture using PIQNIQ was comparable with an interviewer-assisted 24-h recall (24HR).
This study was a single-center randomized clinical trial enrolling 132 adults aged 18 to 65 y from the general population. Under a provided-food protocol with 3 menus designed to include a variety of foods, participants were randomly assigned to 1 of 3 food capture methods: simultaneous entry using PIQNIQ, photo-assisted recall using PIQNIQ, and 24HR. Primary outcomes were energy and nutrient content (calories, total fat, carbohydrates, protein, added sugars, calcium, dietary fiber, folate, iron, magnesium, potassium, saturated fat, sodium, and vitamins A, C, D, and E) captured by the 3 methods.
The majority of nutrients reported were within 30% of consumed intake in all 3 food capture methods (n = 129 completers). Reported intake was highly (>30%) overestimated for added sugars in both PIQNIQ groups and underestimated for calcium in the photo-assisted recall group only (P < 0.001 for all). However, in general, both PIQNIQ methods had similar levels of accuracy and were comparable to the 24HR except in their overestimation (>30%) of added sugars and total fat (P < 0.001 for both).
Our results suggest that intuitive, technology-based methods of dietary data capture are well suited to modern users and, with proper execution, can provide data that are comparable to data obtained with traditional methods. This trial was registered at clinicaltrials.gov as NCT03578458.
准确测量饮食摄入量对于提供营养干预措施以及理解饮食在健康中的复杂作用至关重要。传统的饮食评估方法非常耗费资源，并且给参与者带来负担。技术可能有助于缓解这些限制并改善饮食数据的采集。
我们的目的是评估一种新型移动应用程序（PIQNIQ）通过自我报告来捕捉饮食摄入的准确性。我们的次要目的是评估使用 PIQNIQ 进行食物采集是否与经过访谈员协助的 24 小时回忆法（24HR）相当。
这是一项单中心随机临床试验，共纳入了来自普通人群的 132 名年龄在 18 至 65 岁之间的成年人。在提供食物的方案下，使用 3 种设计用于包含各种食物的菜单，参与者被随机分配到 3 种食物采集方法中的 1 种：使用 PIQNIQ 同时输入、使用 PIQNIQ 拍照辅助回忆和 24HR。主要结局是通过这 3 种方法采集的能量和营养素含量（卡路里、总脂肪、碳水化合物、蛋白质、添加糖、钙、膳食纤维、叶酸、铁、镁、钾、饱和脂肪、钠和维生素 A、C、D 和 E）。
在所有 3 种食物采集方法中（n=129 名完成者），报告的大多数营养素都在消耗摄入量的 30%以内。在使用 PIQNIQ 的 2 个组中，添加糖的报告摄入量均被高度高估（均>30%），而仅在拍照辅助回忆组中，钙的报告摄入量被低估（均<0.001）。然而，总体而言，这两种 PIQNIQ 方法的准确性水平相似，与 24HR 相当，除了高估添加糖和总脂肪（均<0.001）。
我们的结果表明，基于直观的技术的饮食数据采集方法非常适合现代用户，并且如果执行得当，可以提供与传统方法相当的数据。该试验在 clinicaltrials.gov 上注册为 NCT03578458。
