Development of a diet quality score and adherence to the Swiss dietary recommendations for vegans.

BACKGROUND: Vegan diets have recently gained popularity in Switzerland as well as globally. The aim of the present study was to develop a diet quality score for vegans (DQS-V) based on the Swiss dietary recommendations for vegans. METHODS: The dataset included 52 healthy vegan adults. Dietary intake data were assessed by three-day weighed food records. Body weight and height were measured, and a venous blood sample for the analysis of vitamin and mineral status was collected. Spearman rank correlation coefficients were used due to not-normally distributed data. Dietary patterns were identified using principal component analysis (PCA). RESULTS: The DQS-V score (mean ± SD) was 48.9 ± 14.7. Most vegans adhered to the recommended portions of vegetables, vitamin C-rich vegetables, fruits, omega-3-rich nuts, fats and oils, and iodized salt. However, the intake of green leafy vegetables, vitamin C-rich fruits, wholegrains, legumes, nuts and seeds, selenium-rich nuts, zero caloric liquid, and calcium-fortified foods was suboptimal. The sample overconsumed sweet-, salty-, fried foods, and alcohol. The DQS-V had a significantly positive correlation with intakes of fibre, polyunsaturated fatty acids, potassium, zinc, and phosphorus intakes (p's < 0.05) but was negatively correlated with vitamin B12 and niacin intakes (p's < 0.05). Two dietary patterns were derived from PCA: 1) refined grains and sweets and 2) wholegrains and nuts. The correlation between the DQS-V and the first dietary pattern was negative (- 0.41, p = 0.004) and positive for the second dietary pattern (0.37, p = 0.01). The refined grains and sweets dietary pattern was inversely correlated with beta-carotene status (- 0.41, p = 0.004) and vitamin C status (r = - 0.51, p = 0.0002). CONCLUSION: The newly developed DQS-V provides a single score for estimating diet quality among vegan adults. Further validation studies examining the DQS-V in relation to an independent dietary assessment method and to biomarkers of nutritional intake and status are still needed before the general application of the DQS-V.

The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App.

BACKGROUND: Technological advancements have enabled nutrient estimation by smartphone apps such as goFOOD. This is an artificial intelligence-based smartphone system, which uses food images or video captured by the user as input and then translates these into estimates of nutrient content. The quality of the data is highly dependent on the images the user records. This can lead to a major loss of data and impaired quality. Instead of removing these data from the study, in-depth analysis is needed to explore common mistakes and to use them for further improvement of automated apps for nutrition assessment. OBJECTIVE: The aim of this study is to analyze common mistakes made by participants using the goFOOD Lite app, a version of goFOOD, which was designed for food-logging, but without providing results to the users, to improve both the instructions provided and the automated functionalities of the app. METHODS: The 48 study participants were given face-to-face instructions for goFOOD Lite and were asked to record 2 pictures (1 recording) before and 2 pictures (1 recording) after the daily consumption of each food or beverage, using a reference card as a fiducial marker. All pictures that were discarded for processing due to mistakes were analyzed to record the main mistakes made by users. RESULTS: Of the 468 recordings of nonpackaged food items captured by the app, 60 (12.8%) had to be discarded due to errors in the capturing procedure. The principal problems were as follows: wrong fiducial marker or improper marker use (19 recordings), plate issues such as a noncompatible or nonvisible plate (8 recordings), a combination of various issues (17 recordings), and other reasons such as obstacles (hand) in front of the camera or matching recording pairs (16 recordings). CONCLUSIONS: No other study has focused on the principal problems in the use of automatic apps for assessing nutritional intake. This study shows that it is important to provide study participants with detailed instructions if high-quality data are to be obtained. Future developments could focus on making it easier to recognize food on various plates from its color or shape and on exploring alternatives to using fiducial markers. It is also essential for future studies to understand the training needed by the participants as well as to enhance the app's user-friendliness and to develop automatic image checks based on participant feedback.