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 no-reply@cambridge.org
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 evaluate five popular fast-food chains’ menus in relation to dietary guidance.
Design
Menus posted on chains’ websites were coded using the Food and Nutrient Database for Dietary Studies and MyPyramid Equivalents Database to enable Healthy Eating Index-2005 (HEI-2005) scores to be assigned. Dollar or value and kids’ menus and sets of items promoted as healthy or nutritious were also assessed.
Setting
Five popular fast-food chains in the USA.
Subjects
Not applicable.
Results
Full menus scored lower than 50 out of 100 possible points on the HEI-2005. Scores for Total Fruit, Whole Grains and Sodium were particularly dismal. Compared with full menus, scores on dollar or value menus were 3 points higher on average, whereas kids’ menus scored 10 points higher on average. Three chains marketed subsets of items as healthy or nutritious; these scored 17 points higher on average compared with the full menus. No menu or subset of menu items received a score higher than 72 out of 100 points.
Conclusions
The poor quality of fast-food menus is a concern in light of increasing away-from-home eating, aggressive marketing to children and minorities, and the tendency for fast-food restaurants to be located in low-income and minority areas. The addition of fruits, vegetables and legumes; replacement of refined with whole grains; and reformulation of offerings high in sodium, solid fats and added sugars are potential strategies to improve fast-food offerings. The HEI may be a useful metric for ongoing monitoring of fast-food menus.
We describe the methods used to develop and score a 17-item ‘screener’ designed to estimate intake of fruit and vegetables, percentage energy from fat and fibre. The ability of this screener and a food-frequency questionnaire (FFQ) to measure these exposures is evaluated.
Design:
Using US national food consumption data, stepwise multiple regression was used to identify the foods to be included on the instrument; multiple regression analysis was used to develop scoring algorithms. The performance of the screener was evaluated in three different studies. Estimates of intakes measured by the screener and the FFQ were compared with true usual intake based on a measurement error model.
Setting:
US adult population.
Subjects:
For development of instrument, n = 9323 adults. For testing of instrument, adult men and women in three studies completing multiple 24-hour dietary recalls, FFQ and screeners, n = 484, 462 and 416, respectively.
Results:
Median recalled intakes for examined exposures were generally estimated closely by the screener. In the various validation studies, the correlations between screener estimates and estimated true intake were 0.5–0.8. In general, the performances of the screener and the full FFQ were similar; estimates of attenuation were lower for screeners than for full FFQs.
Conclusions:
When coupled with appropriate reference data, the screener approach described may yield useful estimates of intake, for both surveillance and epidemiological purposes.
Despite assumed similarities in Canadian and US dietary habits, some differences in food availability and nutrient fortification exist. Food-frequency questionnaires designed for the USA may therefore not provide the most accurate estimates of dietary intake in Canadian populations. Hence, we undertook to evaluate and modify the National Cancer Institute's Diet History Questionnaire (DHQ) and nutrient database.
Methods
Of the foods queried on the DHQ, those most likely to differ in nutrient composition were identified. Where possible these foods were matched to comparable foods in the Canadian Nutrient File. Nutrient values were examined and modified to reflect the Canadian content of minerals (calcium, iron, zinc) and vitamins (A, C, D, thiamin, riboflavin, niacin, B6, folate and B12). DHQs completed by 13 181 Alberta Cohort Study participants aged 35–69 years were analysed to estimate nutrient intakes using the original US and modified versions of the DHQ databases. Misclassification of intake for meeting the Dietary Reference Intake (DRI) was determined following analysis with the US nutrient database.
Results
Twenty-five per cent of 2411 foods deemed most likely to differ in nutrient profile were subsequently modified for folate, 11% for vitamin D, 10% for calcium and riboflavin, and between 7 and 10% for the remaining nutrients of interest. Misclassification with respect to meeting the DRI varied but was highest for folate (7%) and vitamin A (7%) among men, and for vitamin D (7%) among women over 50 years of age.
Conclusion
Errors in nutrient intake estimates owing to differences in food fortification between the USA and Canada can be reduced in Canadian populations by using nutrient databases that reflect Canadian fortification practices.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.