University College Dublin Institute of Food and Health, Science Centre South, University College Dublin, Dublin, Ireland.
Insight Centre for Data Analytics, University College Dublin, Belfield, Dublin, Ireland.
J Med Internet Res. 2024 Feb 14;26:e48817. doi: 10.2196/48817.
Dietary intake assessment is an integral part of addressing suboptimal dietary intakes. Existing food-based methods are time-consuming and burdensome for users to report the individual foods consumed at each meal. However, ease of use is the most important feature for individuals choosing a nutrition or diet app. Intakes of whole meals can be reported in a manner that is less burdensome than reporting individual foods. No study has developed a method of dietary intake assessment where individuals report their dietary intakes as whole meals rather than individual foods.
This study aims to develop a novel, meal-based method of dietary intake assessment and test its ability to estimate nutrient intakes compared with that of a web-based, 24-hour recall (24HR).
Participants completed a web-based, generic meal-based recall. This involved, for each meal type (breakfast, light meal, main meal, snack, and beverage), choosing from a selection of meal images those that most represented their intakes during the previous day. Meal images were based on generic meals from a previous study that were representative of the actual meal intakes in Ireland. Participants also completed a web-based 24HR. Both methods were completed on the same day, 3 hours apart. In a crossover design, participants were randomized in terms of which method they completed first. Then, 2 weeks after the first dietary assessments, participants repeated the process in the reverse order. Estimates of mean daily nutrient intakes and the categorization of individuals according to nutrient-based guidelines (eg, low, adequate, and high) were compared between the 2 methods. P values of less than .05 were considered statistically significant.
In total, 161 participants completed the study. For the 23 nutrient variables compared, the median percentage difference between the 2 methods was 7.6% (IQR 2.6%-13.2%), with P values ranging from <.001 to .97, and out of 23 variables, effect sizes for the differences were small for 19 (83%) variables, moderate for 2 (9%) variables, and large for 2 (9%) variables. Correlation coefficients were statistically significant (P<.05) for 18 (78%) of the 23 variables. Statistically significant correlations ranged from 0.16 to 0.45, with median correlation of 0.32 (IQR 0.25-0.40). When participants were classified according to nutrient-based guidelines, the proportion of individuals who were classified into the same category ranged from 52.8% (85/161) to 84.5% (136/161).
A generic meal-based method of dietary intake assessment provides estimates of nutrient intake comparable with those provided by a web-based 24HR but with varying levels of agreement among nutrients. Further studies are required to refine and improve the generic recall across a range of nutrients. Future studies will consider user experience including the potential feasibility of incorporating image recognition of whole meals into the generic recall.
饮食摄入评估是解决饮食摄入不足的重要组成部分。现有的基于食物的方法对于用户来说既费时又费力,需要报告每餐所食用的各种食物。然而,对于个人来说,易用性是选择营养或饮食应用程序最重要的特征。报告完整的膳食可以比报告单个食物更轻松。目前还没有研究开发出一种通过报告完整膳食来评估饮食摄入的方法。
本研究旨在开发一种新的、基于膳食的饮食摄入评估方法,并测试其与基于网络的 24 小时回顾法(24HR)相比估计营养素摄入量的能力。
参与者完成了基于网络的通用膳食回顾。对于每种膳食类型(早餐、便餐、主餐、小吃和饮料),参与者从一组膳食图像中选择最能代表他们前一天摄入量的图像。膳食图像基于之前研究中的通用膳食,代表了爱尔兰实际的膳食摄入量。参与者还完成了基于网络的 24HR。这两种方法都是在同一天进行的,间隔 3 小时。在交叉设计中,参与者根据他们首先完成的方法进行随机分组。然后,在第一次饮食评估 2 周后,参与者以相反的顺序重复该过程。比较了两种方法之间的 23 种营养素变量的平均每日营养素摄入量估计值和根据营养素为基础的指南(例如,低、充足和高)对个体进行分类的情况。P 值小于 0.05 被认为具有统计学意义。
共有 161 名参与者完成了研究。对于比较的 23 个营养变量,两种方法之间的中位数百分比差异为 7.6%(IQR 2.6%-13.2%),P 值范围从<0.001 到 0.97,在 23 个变量中,19 个(83%)变量的差异效应大小较小,2 个(9%)变量的差异效应大小为中等,2 个(9%)变量的差异效应大小较大。18 个(78%)变量的相关系数具有统计学意义(P<.05)。18 个变量中有 18 个变量的相关系数有统计学意义(P<.05)。统计学显著的相关性范围从 0.16 到 0.45,中位数相关性为 0.32(IQR 0.25-0.40)。根据基于营养素的指南对个体进行分类时,个体被归入同一类别的比例范围为 52.8%(85/161)至 84.5%(136/161)。
一种基于通用膳食的饮食摄入评估方法可以提供与基于网络的 24HR 相似的营养素摄入量估计值,但不同营养素之间的一致性存在差异。需要进一步的研究来改进和完善通用回忆术,以涵盖一系列的营养物质。未来的研究将考虑用户体验,包括将整餐图像识别纳入通用回忆术的潜在可行性。