Short Method of Dietary Analysis as Based on the New Data on Food Composition

1952 ◽  
Vol 28 (9) ◽  
pp. 806-808
Author(s):  
Jane M. Leichsenring
Keyword(s):  
Food Composition ◽  
Dietary Analysis ◽  
Short Method

scholarly journals Accuracy of Nutrient Calculations Using the Consumer-Focused Online App MyFitnessPal: Validation Study (Preprint)

2020 ◽  
Author(s):  
Charlotte Evenepoel ◽  
Egbert Clevers ◽  
Lise Deroover ◽  
Wendy Van Loo ◽  
Christophe Matthys ◽  
...  
Keyword(s):  
Total Energy ◽  
Energy Intake ◽  
Nutrient Intake ◽  
Food Composition ◽  
Total Energy Intake ◽  
Outcome Variable ◽  
Dietary Analysis ◽  
Limit Values ◽  
T1 And T2 ◽  
The Impact

BACKGROUND Digital food registration via online platforms that are coupled to large food databases obviates the need for manual processing of dietary data. The reliability of such platforms depends on the quality of the associated food database. OBJECTIVE In this study, we validate the database of MyFitnessPal versus the Belgian food composition database, Nubel. METHODS After carefully given instructions, 50 participants used MyFitnessPal to each complete a 4-day dietary record 2 times (T1 and T2), with 1 month in between T1 and T2. Nutrient intake values were calculated either manually, using the food composition database Nubel, or automatically, using the database coupled to MyFitnessPal. First, nutrient values from T1 were used as a training set to develop an algorithm that defined upper limit values for energy intake, carbohydrates, fat, protein, fiber, sugar, cholesterol, and sodium. These limits were applied to the MyFitnessPal dataset extracted at T2 to remove extremely high and likely erroneous values. Original and cleaned T2 values were correlated with the Nubel calculated values. Bias was estimated using Bland-Altman plots. Finally, we simulated the impact of using MyFitnessPal for nutrient analysis instead of Nubel on the power of a study design that correlates nutrient intake to a chosen outcome variable. RESULTS Per food portion, the following upper limits were defined: 1500 kilocalories for total energy intake, 95 grams (g) for carbohydrates, 92 g for fat, 52 g for protein, 22 g for fiber, 70 g for sugar, 600 mg for cholesterol, and 3600 mg for sodium. Cleaning the dataset extracted at T2 resulted in a 2.8% rejection. Cleaned MyFitnessPal values demonstrated strong correlations with Nubel for energy intake (r=0.96), carbohydrates (r=0.90), fat (r=0.90), protein (r=0.90), fiber (r=0.80), and sugar (r=0.79), but weak correlations for cholesterol (ρ=0.51) and sodium (ρ=0.53); all <i>P</i> values were ≤.001. No bias was found between both methods, except for a fixed bias for fiber and a proportional bias for cholesterol. A 5-10% power loss should be taken into account when correlating energy intake and macronutrients obtained with MyFitnessPal to an outcome variable, compared to Nubel. CONCLUSIONS Dietary analysis with MyFitnessPal is accurate and efficient for total energy intake, macronutrients, sugar, and fiber, but not for cholesterol and sodium.

scholarly journals Accuracy of Nutrient Calculations Using the Consumer-Focused Online App MyFitnessPal: Validation Study

10.2196/18237 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e18237
Author(s):  
Charlotte Evenepoel ◽  
Egbert Clevers ◽  
Lise Deroover ◽  
Wendy Van Loo ◽  
Christophe Matthys ◽  
...  
Keyword(s):  
Total Energy ◽  
Energy Intake ◽  
Nutrient Intake ◽  
Food Composition ◽  
Total Energy Intake ◽  
Outcome Variable ◽  
Dietary Analysis ◽  
Limit Values ◽  
T1 And T2 ◽  
The Impact

Background Digital food registration via online platforms that are coupled to large food databases obviates the need for manual processing of dietary data. The reliability of such platforms depends on the quality of the associated food database. Objective In this study, we validate the database of MyFitnessPal versus the Belgian food composition database, Nubel. Methods After carefully given instructions, 50 participants used MyFitnessPal to each complete a 4-day dietary record 2 times (T1 and T2), with 1 month in between T1 and T2. Nutrient intake values were calculated either manually, using the food composition database Nubel, or automatically, using the database coupled to MyFitnessPal. First, nutrient values from T1 were used as a training set to develop an algorithm that defined upper limit values for energy intake, carbohydrates, fat, protein, fiber, sugar, cholesterol, and sodium. These limits were applied to the MyFitnessPal dataset extracted at T2 to remove extremely high and likely erroneous values. Original and cleaned T2 values were correlated with the Nubel calculated values. Bias was estimated using Bland-Altman plots. Finally, we simulated the impact of using MyFitnessPal for nutrient analysis instead of Nubel on the power of a study design that correlates nutrient intake to a chosen outcome variable. Results Per food portion, the following upper limits were defined: 1500 kilocalories for total energy intake, 95 grams (g) for carbohydrates, 92 g for fat, 52 g for protein, 22 g for fiber, 70 g for sugar, 600 mg for cholesterol, and 3600 mg for sodium. Cleaning the dataset extracted at T2 resulted in a 2.8% rejection. Cleaned MyFitnessPal values demonstrated strong correlations with Nubel for energy intake (r=0.96), carbohydrates (r=0.90), fat (r=0.90), protein (r=0.90), fiber (r=0.80), and sugar (r=0.79), but weak correlations for cholesterol (ρ=0.51) and sodium (ρ=0.53); all P values were ≤.001. No bias was found between both methods, except for a fixed bias for fiber and a proportional bias for cholesterol. A 5-10% power loss should be taken into account when correlating energy intake and macronutrients obtained with MyFitnessPal to an outcome variable, compared to Nubel. Conclusions Dietary analysis with MyFitnessPal is accurate and efficient for total energy intake, macronutrients, sugar, and fiber, but not for cholesterol and sodium.

Towards a European Food Composition Data Interchange Platform

2012 ◽  
Vol 82 (3) ◽  
pp. 209-215 ◽  
Author(s):  
Simone Bell ◽  
Heikki Pakkala ◽  
Michael P. Finglas
Keyword(s):  
Nutrient Content ◽  
Food Composition ◽  
Consumer Information ◽  
Health Claims ◽  
Composition Data ◽  
European Committee ◽  
Food Properties ◽  
Data Interchange ◽  
Food Composition Data ◽  
Definition Of

Food composition data (FCD) comprises the description and identification of foods, as well as their nutrient content, other constituents, and food properties. FCD are required for a range of purposes including food labeling, supporting health claims, nutritional and clinical management, consumer information, and research. There have been differences within and beyond Europe in the way FCD are expressed with respect to food description, definition of nutrients and other food properties, and the methods used to generate data. One of the major goals of the EuroFIR NoE project (2005 - 10) was to provide tools to overcome existing differences among member states and parties with respect to documentation and interchange of FCD. The establishment of the CEN’s (European Committee for Standardisation) TC 387 project committee on Food Composition Data, led by the Swedish Standards Institute, and the preparation of the draft Food Data Standard, has addressed these deficiencies by enabling unambiguous identification and description of FCD and their quality, for dissemination and data interchange. Another major achievement of the EuroFIR NoE project was the development and dissemination of a single, authoritative source of FCD in Europe enabling the interchange and update of data between countries, and also giving access to users of FCD.

Dietary Vitamin and Stability of Oral Anticoagulation: Proposal of a Diet with Constant Vitamin K1 Content

1997 ◽  
Vol 77 (03) ◽  
pp. 504-509 ◽  
Author(s):  
Sarah L Booth ◽  
Jacqueline M Charnley ◽  
James A Sadowski ◽  
Edward Saltzman ◽  
Edwin G Bovill ◽  
...  
Keyword(s):  
Dietary Intake ◽  
Vitamin K ◽  
High Performance ◽  
Case Reports ◽  
Food Composition ◽  
Dietary Guidelines ◽  
Dietary Vitamin ◽  
Vitamin K1 ◽  
The Stability ◽  
The Impact

SummaryCase reports cited in Medline or Biological Abstracts (1966-1996) were reviewed to evaluate the impact of vitamin K1 dietary intake on the stability of anticoagulant control in patients using coumarin derivatives. Reported nutrient-drug interactions cannot always be explained by the vitamin K1 content of the food items. However, metabolic data indicate that a consistent dietary intake of vitamin K is important to attain a daily equilibrium in vitamin K status. We report a diet that provides a stable intake of vitamin K1, equivalent to the current U.S. Recommended Dietary Allowance, using food composition data derived from high-performance liquid chromatography. Inconsistencies in the published literature indicate that prospective clinical studies should be undertaken to clarify the putative dietary vitamin K1-coumarin interaction. The dietary guidelines reported here may be used in such studies.

European Food Composition Tables in Translation

1987 ◽  
Author(s):  
Lenore Arab ◽  
Marion Wittler ◽  
Gotthard Schettler
Keyword(s):  
Food Composition ◽  
Food Composition Tables

Features of spring feeding of sterlet of the middle course of the Northern Dvina river

2020 ◽  
pp. 8-17
Author(s):  
A. Bykov ◽  
D. Palatov ◽  
I. Studenov ◽  
D. Chupov
Keyword(s):  
Benthic Communities ◽  
High Water ◽  
Food Composition ◽  
Fish Growth ◽  
High Water Content ◽  
Feeding Intensity ◽  
Biting Midges ◽  
Size Groups ◽  
Main Components ◽  
A Minor

The article provides information about the features of spring feeding of sterlet in the spawning grounds of the middle course of the Northern Dvina river in may 2019. The main and secondary groups of forage objects in the diet of this species of sturgeon are characterized. The article considers the variability of the sterlet food composition with an increase in the size of fish from 30 to 60 cm. In the process of fish growth in the diet of the Severodvinsk sterlet, the main components in terms of occurrence and mass in all size groups are the larvae of Brooks and chironomids. A minor occurrence was the larvae of midges, biting midges, stoneflies, mayflies and small clams. To random and seasonal food are the larvae of water bugs, butterflies, flies, beetles and eggs of other fish. The feeding intensity of the smaller sterlet (30–40 cm) was significantly higher than that of the fish in the size groups 40–50 and 50–60 cm. Fundamental changes in the diet of the Severodvinsk sterlet for the main food objects for more than sixty years of observations have not been established. During periods of high water content of the Northern Dvina due to seasonal changes in the structure of benthic communities, the value of Brooks in the diet of sterlet increases and the proportion of chironomids decreases.

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