PubMed · 13645875
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M H GOODLOE, C B QUINLAN, J G BARROW. 1959. A diet record technique.. https://pubmed.ncbi.nlm.nih.gov/13645875/
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OBJECTIVE: The primary goal was to test the validity of digital photography for measuring food portion sizes compared with weighed foods and with direct visual estimation. SAMPLES: A total of 60 test meals consisting of 10 different portion sizes from six different university cafeteria menus were prepared and weighed. DESIGN: Food selections and plate waste, as estimated by digital photography and direct visual estimation, were compared with weighed foods. For each method, three observers independently estimated portion sizes of each food. Observers expressed the portion sizes as a percentage of a standard serving. These percentages were multiplied by the weight of the standard portion to yield estimated weights. Statistical analyses To test validity, the estimates of food weights derived from both methods were compared with weighed foods using correlations and were compared with each other using Bland-Altman regression analysis. RESULTS: For the digital photography and direct visual estimation methods, estimates of the portion sizes for food selections, plate waste, and food intake were highly correlated with weighed foods. Both methods tended to yield small overestimates or underestimates. Bland-Altman regression found the two estimation methods to yield comparable results (bias less than 1.5 g). APPLICATIONS/CONCLUSIONS: These findings support the validity of the digital photography method for measuring portion sizes. Digital photography may be most useful for measuring food intake in settings that allow for the direct observation of food selections and plate waste but require minimum disruption of the eating environment, and allow unhurried estimates of portion sizes.
OBJECTIVE: To assess the validity of a semi-quantitative food-frequency questionnaire (SFFQ) used in a large nation-wide dietary survey among 12-month-old Norwegian infants. METHODS: The SFFQ was administered to the parents about 1 week before the infants turned 12-month-old. The participants filled in the SFFQ and 1-2 weeks later they kept weighed food records for a total of 7 days. Both methods were completed for 64 infants. RESULTS: The SFFQ overestimated energy intake with 25% and gave significantly higher estimates of all nutrients compared with the records, except for calcium. Much of the difference between the methods disappeared when nutrient density was compared. Spearman correlation coefficients between pairwise measurements of nutrient intakes from the food records and the SFFQ ranged from 0.18 for vitamin D to 0.72 for polyunsaturated fatty acid intake (median r=0.50). On average 38% of the infants were classified in the same quartile with the two methods, and 3% in the opposite quartile. The correlations for food items varied from 0.28 for sweetened drinks to 0.83 for commercial porridge (median r=0.62). CONCLUSION: This study indicated that the SFFQ overestimates average absolute nutrient intakes. However, the questionnaire gave better estimates for average nutrient densities than for absolute nutrient intakes. The capability of the questionnaire to rank infants according to intake of nutrients and food items was moderate, but at the same level as others have observed with food-frequency questionnaires.
A routine activity for a sports dietitian is to estimate energy and nutrient intake from an athlete's self-reported food intake. Decisions made by the dietitian when coding a food record are a source of variability in the data. The aim of the present study was to determine the variability in estimation of the daily energy and key nutrient intakes of elite athletes, when experienced coders analyzed the same food record using the same database and software package. Seven-day food records from a dietary survey of athletes in the 1996 Australian Olympic team were randomly selected to provide 13 sets of records, each set representing the self-reported food intake of an endurance, team, weight restricted, and sprint/power athlete. Each set was coded by 3-5 members of Sports Dietitians Australia, making a total of 52 athletes, 53 dietitians, and 1456 athlete-days of data. We estimated within- and between- athlete and dietitian variances for each dietary nutrient using mixed modeling, and we combined the variances to express variability as a coefficient of variation (typical variation as a percent of the mean). Variability in the mean of 7-day estimates of a nutrient was 2- to 3-fold less than that of a single day. The variability contributed by the coder was less than the true athlete variability for a 1-day record but was of similar magnitude for a 7-day record. The most variable nutrients (e.g., vitamin C, vitamin A, cholesterol) had approximately 3-fold more variability than least variable nutrients (e.g., energy, carbohydrate, magnesium). These athlete and coder variabilities need to be taken into account in dietary assessment of athletes for counseling and research.