We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure [email protected]
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
To highlight the differences between the food list required in a food-frequency questionnaire (FFQ) to rank people by their intake and the food items that contribute to absolute intake, and to discuss possible applications.
Methods:
We conducted a nutritional survey among 1173 adults using an adapted 24-hour recall questionnaire.
Statistical analysis:
To develop an FFQ, we analysed the 24-hour recall survey data by performing a stepwise multiple regression after grouping conceptually similar food items into 175 food groups.
Results:
In total, 126 food groups were included in the developed FFQ in order to explain at least 80% of the variance in the consumption of each of 27 nutrients. The nutrients that were explained by a few food groups were vitamin A (one food group), alcohol (two), β-carotene (two), vitamin E (three) and cholesterol (five). Nutrients that were explained by a large number of food groups were energy (37 food groups), potassium (31), magnesium (31), dietary fibre (30), phosphorus (31) and sodium (29). Using energy intake as an example, soft drinks were the best between-person energy classifiers, while providing only 2.4% of the total energy intake. Wine, seeds and nuts, which contributed highly to the variance, were minor energy contributors. In contrast, milk, sugar, fried chicken/turkey breast or whole chicken/turkey, which explained little of the variation in the population, were major energy contributors.
Conclusions:
Developing an FFQ on the basis of common foods may not explain the between-person variation required for ranking individual intake in diet–disease studies. Producing lists of ‘discriminating items’ can be a useful application in developing mini-FFQs for selected nutrients.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.