■控制饱和脂肪和胆固醇的摄入对于预防心血管疾病很重要。尽管移动饮食跟踪应用程序的使用一直在增加,营养应用程序在跟踪不同国家的饱和脂肪和胆固醇方面的可靠性仍然不足。
■这项研究旨在检查营养应用程序的可靠性和一致性,重点关注不同国家背景下的饱和脂肪和胆固醇摄入量。这项研究集中在三个关键问题上:数据遗漏,应用程序中饱和脂肪和胆固醇值的不一致(可变性),以及不同国家环境下商业应用的可靠性。
■来自4个消费级应用程序的营养数据(COFIT,MyFitness还是中国人,MyFitnessPlayer-English,和LoseIt!)和一个学术应用程序(FormosaFoodApp)与2个国家参考数据库(美国农业部[USDA]-膳食研究食品和营养素数据库[FNDDS]和台湾食品成分数据库[FCD])进行了比较。记录了缺失营养素的百分比,和变异系数被用来计算数据的不一致性。单向ANOVA用于检查应用程序之间的差异,并使用配对的2尾t检验将应用程序与国家参考数据进行比较。通过比较MyFitnessAl的中文和英文版本与USDA-FNDDS和台湾FCD,研究了不同国家背景下的可靠性。
■在5个应用程序中,分析了来自42个项目的836个食品代码。四个应用程序,包括COFIT,MyFitness还是中国人,MyFitnessPlayer-English,输了!,严重低估了饱和脂肪,误差范围为-13.8%至-40.3%(所有P<0.05)。所有的应用都低估了胆固醇,误差范围为-26.3%至-60.3%(所有P<0.05)。COFIT忽略了47%的饱和脂肪数据,MyFitnessAl-Chinese遗漏了62%的胆固醇数据。牛肉的变异系数,鸡肉,海鲜从78%到145%不等,从74%到112%,从97%到124%,MyFitnessPlayer-English,输了!,分别,表明不同食物组之间的饱和脂肪差异很大。同样,在所有选定的应用程序中,乳制品(71%-118%)和预包装食品(84%-118%)的胆固醇变异性始终很高.在检查MyFitnessAl在不同国家环境中的可靠性时,在不同的国家FCD(美国农业部-FNDSS和台湾FCD)中,MyFitnessAl的错误是一致的。不管FCD用作参考,这些错误仍然具有统计学意义,表明应用的核心数据库是问题的根源,而不仅仅是外部FCD中的不匹配或差异。
这些发现揭示了饮食跟踪应用程序中饱和脂肪和胆固醇报告的大量不准确和不一致之处。这些问题引起了人们对使用消费者级营养应用程序在不同国家背景下以及应用程序本身的心血管疾病预防中的有效性的担忧。
UNASSIGNED: Controlling saturated fat and cholesterol intake is important for the prevention of cardiovascular diseases. Although the use of mobile diet-tracking apps has been increasing, the reliability of nutrition apps in tracking saturated fats and cholesterol across different nations remains underexplored.
UNASSIGNED: This study aimed to examine the reliability and consistency of nutrition apps focusing on saturated fat and cholesterol intake across different national contexts. The study focused on 3 key concerns: data omission, inconsistency (variability) of saturated fat and cholesterol values within an app, and the reliability of commercial apps across different national contexts.
UNASSIGNED: Nutrient data from 4 consumer-grade apps (COFIT, MyFitnessPal-Chinese, MyFitnessPal-English, and LoseIt!) and an academic app (Formosa FoodApp) were compared against 2 national reference databases (US Department of Agriculture [USDA]-Food and Nutrient Database for Dietary Studies [FNDDS] and Taiwan Food Composition Database [FCD]). Percentages of missing nutrients were recorded, and coefficients of variation were used to compute data inconsistencies. One-way ANOVAs were used to examine differences among apps, and paired 2-tailed t tests were used to compare the apps to national reference data. The reliability across different national contexts was investigated by comparing the Chinese and English versions of MyFitnessPal with the USDA-FNDDS and Taiwan FCD.
UNASSIGNED: Across the 5 apps, 836 food codes from 42 items were analyzed. Four apps, including COFIT, MyFitnessPal-Chinese, MyFitnessPal-English, and LoseIt!, significantly underestimated saturated fats, with errors ranging from -13.8% to -40.3% (all P<.05). All apps underestimated cholesterol, with errors ranging from -26.3% to -60.3% (all P<.05). COFIT omitted 47% of saturated fat data, and MyFitnessPal-Chinese missed 62% of cholesterol data. The coefficients of variation of beef, chicken, and seafood ranged from 78% to 145%, from 74% to 112%, and from 97% to 124% across MyFitnessPal-Chinese, MyFitnessPal-English, and LoseIt!, respectively, indicating a high variability in saturated fats across different food groups. Similarly, cholesterol variability was consistently high in dairy (71%-118%) and prepackaged foods (84%-118%) across all selected apps. When examining the reliability of MyFitnessPal across different national contexts, errors in MyFitnessPal were consistent across different national FCDs (USDA-FNDSS and Taiwan FCD). Regardless of the FCDs used as a reference, these errors persisted to be statistically significant, indicating that the app\'s core database is the source of the problems rather than just mismatches or variances in external FCDs.
UNASSIGNED: The findings reveal substantial inaccuracies and inconsistencies in diet-tracking apps\' reporting of saturated fats and cholesterol. These issues raise concerns for the effectiveness of using consumer-grade nutrition apps in cardiovascular disease prevention across different national contexts and within the apps themselves.