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The added value of food frequency questionnaire (FFQ) information to estimate the usual food intake based on repeated 24-hour recalls

Overview of attention for article published in Archives of Public Health, October 2017
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Title
The added value of food frequency questionnaire (FFQ) information to estimate the usual food intake based on repeated 24-hour recalls
Published in
Archives of Public Health, October 2017
DOI 10.1186/s13690-017-0214-8
Pubmed ID
Authors

Cloë Ost, Karin A. A. De Ridder, Jean Tafforeau, Herman Van Oyen

Abstract

Statistical methods to model the usual dietary intake of foods in a population generally ignore the additional information on the never-consumers. The objective of this study is to determine the added value of Food Frequency Questionnaire (FFQ) data allowing distinguishing the never-consumers from the non-consumers while modeling the usual intake distribution. Three food items with a different proportion of never-consumers were selected from the database of the Belgian food consumption survey of 2004 (N = 3200). The usual intake distribution for these food items was modeled with the Statistical Program for Analysis of Dietary Exposure (SPADE) and modeling parameters were extracted. These parameters were used to simulate (a) a new database with two 24-h recalls per respondent and (b) a "true" usual intake distribution. The usual intake distribution from the new database was obtained by modeling the 24-h recalls with SPADE, once without and once with the inclusion of the FFQ data on the never-consumers. Ratios were calculated for the different percentiles of the usual intake distribution: the modeled usual intake (g/day) (for both SPADE with and without the inclusion of FFQ data on never-consumers) was divided by the corresponding percentile of the simulated "true" usual intake (g/day). The closer the ratio is to one, the better the model fits the data. Inclusion of the FFQ information to identify the never-consumers did not improve the estimation of the higher percentiles of the usual intake distribution. However, taking into account this FFQ information improved the estimation of the lower percentiles of the usual intake distribution even when the proportion of never-consumers was low. The inclusion of FFQ information to identify the never-consumers is beneficial when interested in the whole usual intake distribution or in the lower percentiles only, no matter how low the proportion of never-consumers for that food item may be. However, when interest is only in the higher percentiles of the usual intake distribution, inclusion of FFQ information to identify the never-consumers will have no benefit.

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X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 18%
Student > Bachelor 5 15%
Researcher 4 12%
Student > Doctoral Student 3 9%
Student > Ph. D. Student 3 9%
Other 1 3%
Unknown 12 35%
Readers by discipline Count As %
Medicine and Dentistry 7 21%
Nursing and Health Professions 4 12%
Chemical Engineering 1 3%
Biochemistry, Genetics and Molecular Biology 1 3%
Business, Management and Accounting 1 3%
Other 6 18%
Unknown 14 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 December 2018.
All research outputs
#14,605,790
of 25,382,440 outputs
Outputs from Archives of Public Health
#540
of 1,144 outputs
Outputs of similar age
#166,280
of 339,743 outputs
Outputs of similar age from Archives of Public Health
#16
of 22 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,144 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has gotten more attention than average, scoring higher than 51% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 339,743 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.