School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, Fudan University, 130 Dongan Road, Shanghai, 200032, People's Republic of China.
Institute of Otolaryngology, Clinical Research Center, Eye and ENT Hospital, Fudan University, Shanghai, 200031, People's Republic of China.
Int J Behav Nutr Phys Act. 2024 Jul 4;21(1):70. doi: 10.1186/s12966-024-01619-1.
Dietary assessment methods have limitations in capturing real-time eating behaviour accurately. Equipped with automated dietary-data-collection capabilities, the "intelligent ordering system" (IOS) has potential applicability in obtaining long-term consecutive, relatively detailed on-campus dietary records among university students with little resource consumption. We investigated (1) the relative validity of IOS-derived nutrient/food intakes compared to those from the 7-day food diary (7DFD); (2) whether including a supplemental food frequency questionnaire (SFFQ) improves IOS accuracy; and (3) sex differences in IOS dietary intake estimation.
Medical students (n = 221; age = 22.2 ± 2.4 years; 38.5% male and 61.5% female) completed the 7DFD and SFFQ. During the consecutive 7-day survey period, students weighed and photographed each meal before and after consumption. Then, students reviewed their 3-month diet and completed the SFFQ, which includes eight underprovided school-canteen food items (e.g., dairy, fruits, nuts). Meanwhile, 9385 IOS dietary data entries were collected. We used Spearman coefficients and linear regression models to estimate the associations among the different dietary intake assessment methods. Individual- and group-level agreement was assessed using the Wilcoxon signed-rank test, cross-classification, and Bland‒Altman analysis.
IOS mean daily energy, protein, fat, and carbohydrate intake estimations were significantly lower (-15-20%) than those of the 7DFD. The correlation coefficients varied from 0.52 (for added sugar) to 0.88 (for soybeans and nuts), with fruits (0.37) and dairy products (0.29) showing weaker correlations. Sixty-two (milk and dairy products) to 97% (soybeans and nuts) of participants were classified into the same or adjacent dietary intake distribution quartile using both methods. The energy and macronutrient intake differences between the IOS + SFFQ and 7DFD groups decreased substantially. The separate fruit intake measurements from each assessment method did not significantly differ from each other (p > 0.05). IOS and IOS + SFFQ regression models generally yielded higher R values for males than for females.
Despite estimation differences, the IOS can be reliable for medical student dietary habit assessment. The SFFQ is useful for measuring consumption of foods that are typically unavailable in school cafeterias, improving the overall dietary evaluation accuracy. The IOS assessment was more accurate for males than for females.
饮食评估方法在准确捕捉实时饮食行为方面存在局限性。配备自动饮食数据采集功能的“智能点餐系统”（IOS）在获取大学生长期连续、相对详细的校内饮食记录方面具有潜在的适用性，且资源消耗较少。我们研究了（1）IOS 得出的营养素/食物摄入量与 7 天饮食日记（7DFD）相比的相对有效性；（2）是否通过补充食物频率问卷（SFFQ）来提高 IOS 的准确性；以及（3）IOS 饮食摄入估计的性别差异。
医学生（n=221；年龄=22.2±2.4 岁；38.5%为男性，61.5%为女性）完成了 7DFD 和 SFFQ。在连续 7 天的调查期间，学生在进食前后对每餐进行称重和拍照。然后，学生回顾了他们的 3 个月饮食并完成了 SFFQ，其中包括 8 种供应不足的学校食堂食物（如乳制品、水果、坚果）。同时，收集了 9385 个 IOS 饮食数据条目。我们使用 Spearman 系数和线性回归模型来估计不同饮食摄入评估方法之间的关联。使用 Wilcoxon 符号秩检验、交叉分类和 Bland‒Altman 分析评估个体和群体水平的一致性。
IOS 平均每日能量、蛋白质、脂肪和碳水化合物摄入量的估计值明显低于 7DFD（低 15-20%）。相关系数从 0.52（添加糖）到 0.88（大豆和坚果）不等，水果（0.37）和乳制品（0.29）的相关性较弱。使用两种方法，62%（牛奶和乳制品）至 97%（大豆和坚果）的参与者被分类到相同或相邻的饮食摄入分布四分位数中。IOS+SFFQ 与 7DFD 组之间的能量和宏量营养素摄入量差异显著减小。来自每种评估方法的单独水果摄入量测量值彼此之间没有显著差异（p>0.05）。IOS 和 IOS+SFFQ 回归模型通常为男性产生的 R 值高于女性。
尽管存在估计差异，但 IOS 可用于可靠地评估医学生的饮食习惯。SFFQ 可用于测量学校食堂通常无法供应的食物的摄入量，从而提高整体饮食评估的准确性。IOS 评估对男性比女性更准确。
