Handling random errors and biases in methods used for short-term dietary assessment

Rossato, Sinara Laurini; Fuchs, Sandra Cristina Pereira Costa

doi:10.1590/s0034-8910.2014048005154

Cited by 12 publications

(12 citation statements)

References 7 publications

(19 reference statements)

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“…Estimates of food and nutrient intake involve random error (e.g. due to inaccurate food tables, limited days of recall affected by day-to-day variation) and systematic bias [ 20 ] (such as limited food tables and reporting bias; e.g. low energy reporters tend to under-estimate foods high in fats and sugars [ 21 ]).…”

Section: Resultsmentioning

confidence: 99%

DIET@NET: Best Practice Guidelines for dietary assessment in health research

Cade

Warthon-Medina

Albar

et al. 2017

BMC Med

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BackgroundDietary assessment is complex, and strategies to select the most appropriate dietary assessment tool (DAT) in epidemiological research are needed. The DIETary Assessment Tool NETwork (DIET@NET) aimed to establish expert consensus on Best Practice Guidelines (BPGs) for dietary assessment using self-report.MethodsThe BPGs were developed using the Delphi technique. Two Delphi rounds were conducted. A total of 131 experts were invited, and of these 65 accepted, with 48 completing Delphi round I and 51 completing Delphi round II. In all, a total of 57 experts from North America, Europe, Asia and Australia commented on the 47 suggested guidelines.ResultsForty-three guidelines were generated, grouped into the following four stages: Stage I. Define what is to be measured in terms of dietary intake (what? who? and when?); Stage II. Investigate different types of DATs; Stage III. Evaluate existing tools to select the most appropriate DAT by evaluating published validation studies; Stage IV. Think through the implementation of the chosen DAT and consider sources of potential biases.ConclusionsThe Delphi technique consolidated expert views on best practice in assessing dietary intake. The BPGs provide a valuable guide for health researchers to choose the most appropriate dietary assessment method for their studies. These guidelines will be accessible through the Nutritools website, www.nutritools.org.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-017-0962-x) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

DIET@NET: Best Practice Guidelines for dietary assessment in health research

Cade

Warthon-Medina

Albar

et al. 2017

BMC Med

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“…There was a 32 linear and significant increase of vegetable intake for women, whereas the variation was 33 non-linear and of lesser magnitude for men (4). 34 Food intake variation throughout the seasons, sex, and age might be relevant if the 35 variation increases the likelihood of diseases (8,9), but it is even more relevant if it 36 introduces measurement bias (10,11). Since they represent a source of variation and a 37 potential bias, the seasons can influence patterns of health.…”

Section: Introductionmentioning

confidence: 99%

Seasonal variation in food intake and the interaction effects of sex and age among adults in southern Brazil

et al. 2015

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“…This method of evidence collection has guided study design, sample size calculation, and strategies for data collection to reduce bias and random variation across populations (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). It has emphasized collecting dietary data in non-consecutive days to capture the within-person variation for food or nutrient intake (10).…”

Section: Introductionmentioning

confidence: 99%

Diet Data Collected Using 48-h Dietary Recall: Within—and Between-Person Variation

Rossato

2021

Front. Nutr.

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Background and Aims: Forty-eight-hour dietary recall is a valuable source of information regarding food consumption in a population-based sample. This method covers 2 consecutive days in a single interview. Nevertheless, the number of assessments and the sample size necessary to estimate usual intake are unknown. We aimed to assess sources of variation, sample sizes, and numbers of days necessary to estimate usual nutrient intake using the 48-h dietary recall.Methods: This was a population-based cross-sectional study including 237 participants, 11–90 years old, selected using multistage probabilistic sampling to obtain data using 48-h dietary recall. Analysis of variance was used to calculate within- and between-person variation and determine the statistical parameters necessary to calculate sample size and the number of days required to calculate the usual energy and nutrient intake.Results: Within-person variation was generally lower than between-person variation, except for calcium (CVw2 = 40.8; CVb2 = 38.4%), magnesium (CVw2 = 27.4; CVb2 = 18.7%), and monounsaturated fat (CVw2 = 20.0; CVb2 = 17.3%) for the entire group and magnesium for women (CVw2 = 28.3; CVb2 = 91.8%). The number of days and sample size required to determine usual energy and nutrient intake varied substantially with gender and age (e.g., vitamin C in women N = 9, in men N = 1,641).Conclusions: Energy and nutrient intake assessment using the 48-h dietary recall misrepresents within-person variation but can generate acceptable results for between-person variation. The calculation of sample size and number of days required to determine usual energy and nutrient intake might have been affected by inadequate assessment of the within-person variation.

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Handling random errors and biases in methods used for short-term dietary assessment

Cited by 12 publications

References 7 publications

DIET@NET: Best Practice Guidelines for dietary assessment in health research

DIET@NET: Best Practice Guidelines for dietary assessment in health research

Seasonal variation in food intake and the interaction effects of sex and age among adults in southern Brazil

Diet Data Collected Using 48-h Dietary Recall: Within—and Between-Person Variation

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