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@example.com
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.
A wide variety of methods are available to assess dietary intake, each one with different strengths and weaknesses. Researchers face multiple challenges when diet and nutrition need to be accurately assessed, particularly in the selection of the most appropriate dietary assessment method for their study. The goal of the current collaborative work is to present a collection of available resources for dietary assessment implementation.
As a follow-up to the 9th International Conference on Diet and Physical Activity Methods held in 2015, developers of dietary assessment toolkits agreed to collaborate in the preparation of the present paper, which provides an overview of each toolkit. The toolkits presented include: the Diet, Anthropometry and Physical Activity Measurement Toolkit (DAPA; UK); the National Cancer Institute’s (NCI) Dietary Assessment Primer (USA); the Nutritools website (UK); the Australasian Child and Adolescent Obesity Research Network (ACAORN) method selector (Australia); and the Danone Dietary Assessment Toolkit (DanoneDAT; France). An at-a-glance summary of features and comparison of the toolkits is provided.
The present review contains general background on dietary assessment, along with a summary of each of the included toolkits, a feature comparison table and direct links to each toolkit, all of which are freely available online.
This overview of dietary assessment toolkits provides comprehensive information to aid users in the selection and implementation of the most appropriate dietary assessment method, or combination of methods, with the goal of collecting the highest-quality dietary data possible.
To evaluate the Canadian Diet History Questionnaire I (C-DHQ I) food list and to adapt the US DHQ II for Canada using Canadian dietary survey data.
Twenty-four-hour dietary recalls reported by adults in a national Canadian survey were analysed to create a food list corresponding to C-DHQ I food questions. The percentage contribution of the food list to the total survey intake of seventeen nutrients was used as the criterion to evaluate the suitability of the C-DHQ I to capture food intake in Canadian populations. The data were also analysed to identify foods and to modify portion sizes for the C-DHQ II.
The Canadian Community Health Survey (CCHS) – Cycle 2.2 Nutrition (2004).
Adults (n 20 159) who completed 24 h dietary recalls during in-person interviews.
Four thousand five hundred and thirty-three foods and recipes were grouped into 268 Food Groups, of which 212 corresponded to questions on the C-DHQ I. Nutrient intakes captured by the C-DHQ I ranged from 79 % for fat to 100 % for alcohol. For the new C-DHQ II, some food questions were retained from the original US DHQ II while others were added based on foods reported in CCHS and foods available on the Canadian market since 2004. Of 153 questions, 143 were associated with portion sizes of which fifty-three were modified from US values. Sex-specific nutrient profiles for the C-DHQ II nutrient database were derived using CCHS data.
The C-DHQ I and II are designed to optimize the capture of foods consumed by Canadian populations.
Nitrate and nitrite are probable human carcinogens when ingested under conditions that increase the formation of N-nitroso compounds. There have been limited efforts to develop US databases of dietary nitrate and nitrite for standard FFQ. Here we describe the development of a dietary nitrate and nitrite database and its calibration.
We analysed data from a calibration study of 1942 members of the NIH–AARP (NIH–AARP, National Institutes of Health–AARP) Diet and Health Study who reported all foods and beverages consumed on the preceding day in two non-consecutive 24 h dietary recalls (24HR) and completed an FFQ. Based on a literature review, we developed a database of nitrate and nitrite contents for foods reported on these 24HR and for food category line items on the FFQ. We calculated daily nitrate and nitrite intakes for both instruments, and used a measurement error model to compute correlation coefficients and attenuation factors for the FFQ-based intake estimates using 24HR-based values as reference data.
FFQ-based median nitrate intake was 68·9 and 74·1 mg/d, and nitrite intake was 1·3 and 1·0 mg/d, in men and women, respectively. These values were similar to 24HR-based intake estimates. Energy-adjusted correlation coefficients between FFQ- and 24HR-based values for men and women respectively were 0·59 and 0·57 for nitrate and 0·59 and 0·58 for nitrite; energy-adjusted attenuation factors were 0·59 and 0·57 for nitrate and 0·47 and 0·38 for nitrite.
The performance of the FFQ in assessing dietary nitrate and nitrite intakes is comparable to that for many other macro- and micronutrients.
To assess the strength of the relationships between serum carotenoids and three self-reported dietary intake instruments often used to characterize carotenoid intake in studies of diet and disease.
Participants completed a Diet History Questionnaire (DHQ), two 24 h dietary recalls (24HR), a fruit and vegetable screener and a fasting blood draw. We derived dietary intake estimates of α-carotene, β-carotene, cryptoxanthin, lutein, zeaxanthin and lycopene from each diet instrument and calculated sex-specific multivariate correlations between dietary intake estimates and their corresponding serum values.
Montgomery County, Maryland, USA.
Four hundred and seventy women and men aged 40–69 years in the National Cancer Institute's Observing Protein and Energy Nutrition (OPEN) Study.
Serum carotenoids correlated more strongly with the DHQ (r = 0·34–0·54 for women; r = 0·38–0·56 for men) than with the average of two recalls (r = 0·26–0·47 for women; r = 0·26–0·40 for men) with the exception of zeaxanthin, for which the correlations using recalls were higher. With adjustment for within-person variation, correlations between serum carotenoids and recalls were greatly improved (r = 0·38–0·83 for women; r = 0·42–0·74 for men). In most cases, correlations between serum carotenoids and the fruit and vegetable screener resembled serum–DHQ correlations.
Evidence from the study provides support for the use of the DHQ, a fruit and vegetable screener and deattenuated recalls for estimating carotenoid status in studies without serum measures, and draws attention to the importance of adjusting for intra-individual variability when using recalls to estimate carotenoid values.
To develop a method to validate an FFQ for reported intake of episodically consumed foods when the reference instrument measures short-term intake, and to apply the method in a large prospective cohort.
The FFQ was evaluated in a sub-study of cohort participants who, in addition to the questionnaire, were asked to complete two non-consecutive 24 h dietary recalls (24HR). FFQ-reported intakes of twenty-nine food groups were analysed using a two-part measurement error model that allows for non-consumption on a given day, using 24HR as a reference instrument under the assumption that 24HR is unbiased for true intake at the individual level.
The National Institutes of Health–AARP Diet and Health Study, a cohort of 567 169 participants living in the USA and aged 50–71 years at baseline in 1995.
A sub-study of the cohort consisting of 2055 participants.
Estimated correlations of true and FFQ-reported energy-adjusted intakes were 0·5 or greater for most of the twenty-nine food groups evaluated, and estimated attenuation factors (a measure of bias in estimated diet–disease associations) were 0·4 or greater for most food groups.
The proposed methodology extends the class of foods and nutrients for which an FFQ can be evaluated in studies with short-term reference instruments. Although violations of the assumption that the 24HR is unbiased could be inflating some of the observed correlations and attenuation factors, results suggest that the FFQ is suitable for testing many, but not all, diet–disease hypotheses in a cohort of this size.
We evaluated the performance of the food-frequency questionnaire (FFQ) administered to participants in the US NIH–AARP (National Institutes of Health–American Association of Retired Persons) Diet and Health Study, a cohort of 566 404 persons living in the USA and aged 50–71 years at baseline in 1995.
The 124-item FFQ was evaluated within a measurement error model using two non-consecutive 24-hour dietary recalls (24HRs) as the reference.
Participants were from six states (California, Florida, Pennsylvania, New Jersey, North Carolina and Louisiana) and two metropolitan areas (Atlanta, Georgia and Detroit, Michigan).
A subgroup of the cohort consisting of 2053 individuals.
For the 26 nutrient constituents examined, estimated correlations with true intake (not energy-adjusted) ranged from 0.22 to 0.67, and attenuation factors ranged from 0.15 to 0.49. When adjusted for reported energy intake, performance improved; estimated correlations with true intake ranged from 0.36 to 0.76, and attenuation factors ranged from 0.24 to 0.68. These results compare favourably with those from other large prospective studies. However, previous biomarker-based studies suggest that, due to correlation of errors in FFQs and self-report reference instruments such as the 24HR, the correlations and attenuation factors observed in most calibration studies, including ours, tend to overestimate FFQ performance.
The performance of the FFQ in the NIH–AARP Diet and Health Study, in conjunction with the study’s large sample size and wide range of dietary intake, is likely to allow detection of moderate (≥1.8) relative risks between many energy-adjusted nutrients and common cancers.
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.
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.
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.
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.
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.
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.
US adult population.
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.
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.
When coupled with appropriate reference data, the screener approach described may yield useful estimates of intake, for both surveillance and epidemiological purposes.
Email your librarian or administrator to recommend adding this to your organisation's collection.