| Popis: |
Accurate dietary assessment is crucial for nutrition and health research. Traditional methods, such as food records, food frequency questionnaires, and 24-hour dietary recalls (24HR), have limitations, such as the need for trained interviewers, time-consuming procedures, and inaccuracies in estimations. Novel technologies, such as image-based dietary assessment apps, have been developed to overcome these limitations. SNAQ is a novel image-based food-recognition app which, based on computer vision, assesses food type and volume, and provides nutritional information about dietary intake. This cross-sectional observational study aimed to investigate the validity of SNAQ as a dietary assessment tool for measuring energy and macronutrient intake in adult women with normal body weight (n = 30), compared to doubly labeled water (DLW), a reference method for total daily energy expenditure (TDEE). Energy intake was also estimated using a one-day 24HR for direct comparison. Bland–Altman plots, paired difference tests, and Pearson’s correlation coefficient were used to assess agreement and relationships between the methods. SNAQ showed a slightly higher agreement (bias = −329.6 kcal/day) with DLW for total daily energy intake (TDEI) compared to 24HR (bias = −543.0 kcal/day). While both SNAQ and 24HR tended to underestimate TDEI, only 24HR significantly differed from DLW in this regard (p |
