Development, validation and utilisation of food-frequency questionnaires - a review.
ABSTRACT The purpose of this review is to provide guidance on the development, validation and use of food-frequency questionnaires (FFQs) for different study designs. It does not include any recommendations about the most appropriate method for dietary assessment (e.g. food-frequency questionnaire versus weighed record).
A comprehensive search of electronic databases was carried out for publications from 1980 to 1999. Findings from the review were then commented upon and added to by a group of international experts.
Recommendations have been developed to aid in the design, validation and use of FFQs. Specific details of each of these areas are discussed in the text.
FFQs are being used in a variety of ways and different study designs. There is no gold standard for directly assessing the validity of FFQs. Nevertheless, the outcome of this review should help those wishing to develop or adapt an FFQ to validate it for its intended use.
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ABSTRACT: To assess the relationships and possible interactions between polymorphisms related to HDL levels and alcohol consumption. Cross-sectional population-based study including 2863 women and 2546 men aged 35-75 years (CoLaus study). Alcohol intake was assessed by the reported alcohol consumption of the last 7 days. Nineteen candidate genes known to influence HDL levels were studied. Alcohol consumption increased HDL cholesterol levels in both genders. After multivariate adjustment for gender, age, body mass index, smoking, hypolipidaemic drug treatment, physical activity and alcohol consumption, APOA5, CETP, LIPC and LPL gene polymorphisms were significantly (10(-5) threshold) related with HDL cholesterol levels, while no genexalcohol intake interaction was found for all SNPs studied. ABCA1 polymorphisms were related to HDL cholesterol levels on bivariate analysis but the relationship was no longer significant after multivariate analysis. Our data confirm the association of alcohol consumption and of APOA5, CETP, LIPC and LPL gene polymorphisms with HDL cholesterol levels. Conversely, no genexalcohol consumption interactions were found, suggesting that the effect of alcohol consumption on HDL cholesterol levels is not mediated via a modulation of HDL related genes.Atherosclerosis 08/2010; 211(2):551-7. · 3.71 Impact Factor
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ABSTRACT: Our purpose was to design and evaluate a shorter version of our 126-item food frequency questionnaire (long FFQ) for use with adolescents. A shorter FFQ is needed that can reliably rank research subjects according to their intakes of energy, macronutrients and selected micronutrients. Dietary data were collected annually, for 3 years, using the full-list FFQ from 16 882 participants of the Growing Up Today Study (GUTS). From this full-list FFQ data, the top ten food contributors for energy and each macronutrient, and the top five food contributors for eight other selected micronutrients were compiled to create a 26-item (short-list) FFQ. Arithmetic means and Pearson correlations were computed to assess relationships between nutrient intakes estimated from the short- and full-list FFQs. We further compared both FFQs with three 24-hour recalls (approximately every 4 months over a 1-year period). Linear regression models were fitted, using energy and nutrients estimated from the short-list FFQ and separately from the full-list FFQ, to see how results may differ. As expected, mean nutrient values from the short-list FFQ were substantially below those from the full-list FFQ. Pearson correlations >0.85 between the short- and full-list FFQs were found for most nutrients. However, correlations between nutrients from the short-list FFQ and the three 24-hour recalls were lower (mean correlation =0.40) than the full-list FFQ. Linear regression models suggested that the short-list FFQ performed nearly as well as the full-list FFQ, for studying associations between energy and several nutrients (trans fatty acids, saturated fat, and glycemic load) and the non-dietary factor, TV viewing. Model betas for energy and nutrients from the short-list FFQ were slightly smaller than betas obtained from the full-list FFQ, but all were statistically significant. The short-list FFQ can assess nutrient values of a population for analytic research purposes, such as studying associations between certain dietary intakes and non-dietary factors.International Journal of Pediatric Obesity 02/2007; 2(1):31-9. · 2.28 Impact Factor
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ABSTRACT: This study aimed to investigate health belief as a major motive for diet and lifestyle behaviors of 100 vegans in the United States; and to determine congruence with selected health and nutrition outcomes. Response data from an administered questionnaire was analyzed. Statistical analyses determined the most common factors influencing diet choice; the number of vegans practicing particular lifestyle behaviors; body mass index; and prevalence of self-reported chronic disease diagnoses. Nutrient intakes were analyzed and assessed against Dietary Reference Intakes. Health was the most reported reason for diet choice (47%). In the health belief, animal welfare, and religious/other motive categories, low percentages of chronic disease diagnoses were reported: 27%, 11%, and 15%, respectively. There were no significant differences in health behaviors and indices among vegan motive categories, except for product fat content choices. Within the entire study population, health-related vegan motive coincided with regular exercise; 71% normal BMI (mean=22.6); minimal alcohol and smoking practices; frequently consumed vegetables, nuts, and grains; healthy choices in meal types, cooking methods, and low-fat product consumption; and adequate intakes for most protective nutrients when compared to reference values. But incongruence was found with 0% intake adequacy for vitamin D; and observation of excessive sodium use.Appetite 04/2013; · 2.54 Impact Factor
Development, validation and utilisation of food-frequency
questionnaires – a review
Janet Cade1,*, Rachel Thompson2, Victoria Burley1and Daniel Warm2
1Nutrition Epidemiology Group, Division of Public Health, Nuffield Institute for Health, 71–75 Clarendon Road,
University of Leeds, Leeds LS2 9PL, UK:2Institute of Human Nutrition, Southampton General Hospital,
University of Southampton, Southampton SO16 6YD, UK
Submitted 13 August 2001: Accepted 6 November 2001
Objective: The purpose of this review is to provide guidance on the development,
validation and use of food-frequency questionnaires (FFQs) for different study
designs. It does not include any recommendations about the most appropriate
method for dietary assessment (e.g. food-frequency questionnaire versus weighed
Methods: A comprehensive search of electronic databases was carried out for
publications from 1980 to 1999. Findings from the review were then commented
upon and added to by a group of international experts.
Results: Recommendations have been developed to aid in the design, validation and
use of FFQs. Specific details of each of these areas are discussed in the text.
Conclusions: FFQs are being used in a variety of ways and different study designs.
There is no gold standard for directly assessing the validity of FFQs. Nevertheless, the
outcome of this review should help those wishing to develop or adapt an FFQ to
validate it for its intended use.
Definition of a food-frequency questionnaire
of a food-frequency questionnaire (FFQ) was used1:
A questionnaire in which the respondent is presented
with a list of foods and is required to say how often
each is eaten in broad terms such as x times per day/per
week/per month, etc. Foods chosen are usually chosen
for the specific purposes of a study and may not assess
Although food-frequency questionnaires may frequently
form part of a total dietary assessment technique, as for
example in the dietary history method, we have not
included these types of FFQ usage in this consensus
Although this document assumes that a food-frequency
questionnaire is appropriate for use in a particular study, it
is important to be aware of the strengths and limitations of
the method. No dietary methodcan measure dietary intake
without error. Thus it is important that sources of error are
taken into account2.
A review was undertaken of all dietary studies conducted
or published since 1980 in which the development,
validation or use of a food-frequency questionnaire was
described. In order to identify relevant studies that
described the design, evaluation and/or use of FFQs, a
comprehensive search procedure was developed. Elec-
tronic databases including Medline, Embase, Cancerlit,
CAB Abstracts and Dissertation Abstracts and the online
Dietary Assessment Calibration/Validation Register
(http://www-dacv.ims.nci.nih.gov/) were searched from
1980 to September 1999. Hand searches of published
conference proceedings, key nutrition journals and
reference lists of retrieved articles were also undertaken.
Search terms used were based on the MESH terms
and keyword searches for ‘food frequency question-
naires’, ‘reproducibility’, ‘validity/validation’, ‘diet-study-
techniques’ and ‘calibration’. Questionnaires that assessed
only vitamin/mineral supplement intakes, alcohol or
contaminants (such as heavy metals) were excluded, as
were articles written in languages other than English. For
the purposes of the search, we defined a food-frequency
questionnaire as ‘any list of one or more foods with
frequency of intake categories’. However, since the focus
of the review was the design, validation and utilisation of
food-frequency questionnaires, we excluded papers in
which the results of the FFQ were combined with another
dietary assessment technique, as for example in the dietary
history method. All references were downloaded into the
computerised bibliographic program Reference Manager,
q The Authors 2002*Corresponding author: Email firstname.lastname@example.org
Public Health Nutrition: 5(4), 567–587
which facilitated handling of the large numbers of
Owing to the large number of references relating to
utilisation of FFQs, only references published in 1998 were
included in that part of the review. A one-year sample was
thought to be adequate to generate data on the way FFQs
are currently being used.
A form was set up using Microsoft Access that facilitated
data entry from each study. Papers were broadly divided
into one of three study types: validation, reproducibility or
utilisation. Data from these papers were then compiled
into an Access database for analysis.
Following extraction of the data, initial results were
presented at a meeting of the Nutrition Epidemiology
Group, a group of UK national experts in the field, which
was held at King’s College in London in December 1999.
The group members agreed to comment on the text of the
consensus document that was in preparation. The
document was also subsequently sent to a number of
international experts (see list of contributors in Acknow-
ledgements). Following receipt of these comments the
review was amended taking the whole body of evidence
into account. These personal experiences were particu-
larly useful to add detail to some of the design issues
discussed in the review, since this is often not included in
published accounts of research using FFQs. The full
document resulting from this process can be found on the
web at http://www.leeds.ac.uk/nuffield/pubs/ffq.pdf.
Development of food-frequency questionnaires
Questionnaires may either be developed from basic
principles or adapted from existing questionnaires. Of the
227 validation studies in the review, 54% used a modified
version of an existing questionnaire. Of these, 25%
(26/104) were adapted from a questionnaire originally
devised by Block et al. (the NCI/Block Health Habits and
History Questionnaire)3and 27% (28/104) were adapted
from one devised by Willett and colleagues (the Harvard
Semiquantitative Food Frequency Questionnaire)4.
Examples of validation studies that have used the Block
questionnaire include Refs. 5–29 and those that have used
the Willett questionnaire include Refs. 6, 19, 20 and 30–53.
There are a number of publications that discuss the
relative merits of each of these FFQs6,42,54–58.
Purpose of the food-frequency questionnaire
Before selecting or designing a food-frequency ques-
tionnaire, careful consideration should be paid to its
purpose. The purpose of the food-frequency question-
naire was omitted or not clearly stated in many papers.
Fifty-two per cent (115/223) of food-frequency ques-
tionnaires were designed to assess foods or food groups
and 74% (166/223) to assess nutrient intakes.
The group of experts felt that there were some situations
when the use of food-frequency questionnaires was not
advisable. These were: in studies with small numbers of
subjects; for surveillance and monitoring of current levels
where accurate absolute intakes are required; using an
FFQ developed for one country in another country unless
dietary habits are very similar; and in some clinical work
when precise intakes are required. There was less
agreement on the suitability of food-frequency question-
naires for assessment of past diet59–65and individual
Modifying an existing questionnaire
Where time and finances are limited, the use of a pre-
existing questionnaire may be particularly appealing.
Although modification of an existing FFQ is a simpler and
faster method than developing a questionnaire from
scratch, a few points need to be considered before
embarking on this approach.
1. What was the original purpose of the questionnaire?
2. Who was the target population?
3. When was the questionnaire developed?
4. Has a previous validation been carried out, and was it
Careful scrutiny of any publications relating to the
development and/or validation studies of the question-
naire is required to determine whether the original
objectives of the questionnaire meet the requirements of
the new study. The questionnaire may have been
developed a number of years ago and thus may not
cover all commonly eaten foods today: it may be literally
‘out-of-date’. Furthermore, adapting the analysis program
may also take more time than is generally appreciated,
depending on how the program has been written.
Correlation coefficients from papers in the review were
compared for newly designed food-frequency question-
naires and those adapted from other questionnaires.
Newly developed questionnaires had a higher correlation
for energy (0.49 vs. 0.44) and fat (0.52 vs. 0.49) than
modified questionnaires. Adapted questionnaires had a
higher correlation for vitamin C (0.50 vs. 0.44) and vitamin
A (0.41 vs. 0.34) than newly developed questionnaires.
There were no differences (new vs. adapted) for calcium
(0.54 vs. 0.55) and iron (0.45 vs. 0.44). Overall, agreement
between reference dietary measures and FFQs did not
appear to be worse with adapted FFQs than with FFQs
developed de novo.
Development from basic principles
Development of the food list is crucial to the success of a
food-frequency questionnaire. The full variability of the
population’s diet, which includes many different foods,
brands and preparation practices, cannot be captured fully
with a finite food list.
For a food item to either contribute to absolute intake or
to differentiate between individuals, it must be eaten
reasonably often by an appreciable number of the
J Cade et al.568
population and contain a substantial amount of the
nutrient/food group of interest. Also, the use of the food
under study must vary from person to person.
If the aim of the study is aetiological, then it is preferable
to have a comprehensive food list enabling computation
of the full range of nutrients rather than a restricted list to
determine the intakes of a few nutrients. The advantages
of a comprehensive list include the ability to adjust for
energy intake in order to investigate fully diet–disease
relationships68. To assess energy intake requires a
comprehensive food list. Also, if the nutrient of interest
is highly correlated with other nutrients, unless the whole
diet is assessed it may not be possible to explore this. Data
obtained may have long-term use and it is difficult to
predict the dietary factors of future interest, particularly if a
number of research groups have potential access to the
data and opportunities to re-survey the study population
are limited. For example, a recent analysis was carried out
of information collected on 4-year-olds in 1950that are still
being followed-up as adults69. In order to discuss the
bioavailability of a nutrient, it may be important to gather
information about intakes of other nutrients that may
interact with the nutrient of interest.
There are, however, certain circumstances in which the
purpose of the food-frequency questionnaire may be very
specific and a comprehensive food list may be unnecess-
ary or even prohibitive. For example, as a tool for the
identification of high-fat consumers for enrolment in an
intervention trial70,71. A number of short FFQs have been
successfully developed along these lines, with a view to
the assessment of intake of calcium and other nutrients
thought to be related to bone health16,72–78.
How to choose appropriate foods?
Previous dietary survey information collected on the
appropriate population can be used to identify commonly
eaten foods and recipe dishes to be included on a
questionnaire3,79–81. Recent data are required because of
the problem of new foods. This is particularly an issue
with children’s diets.
Stepwise regression analysis on a dietary dataset from
an appropriate population can be used to identify foods
that most discriminate between individuals. There are
available computerised dietary analysis packages or
general statistical programs that can carry out this step82,83.
Foods may also be included in the questionnaire on the
basis of prior information, epidemiological or otherwise,
that an association might exist (e.g. calcium and bone
The food list can be piloted in a sample of the
population of interest. This may be particularly useful
when working with groups whose dietary habits are not
well documented. If no previous recent dietary surveys
can be located, then information can be obtained from a
sample of the target population using 24-hour recalls, diet
histories or participant observations.
Use of individual foods versus groups of foods
Obtaining accurate reports for foods eaten both alone and
in mixed dishes is particularly problematic (e.g. vegetables
as a whole portion or in a mixed dish). Food-frequency
questionnaires may ask the respondent to report either a
combined frequency for a particular food eaten both alone
and in mixed dishes (e.g. beef). Alternatively, they may
ask the respondent to report separate frequencies for
foods eaten alone or in combination (e.g. separate
questions on different meat dishes containing beef, such
as roast beef, beef casserole, chilli con carne, etc). The first
approach is cognitively complex and difficult for people
not involved in cooking, but the second approach may
lead to double counting and overestimation of intake. In
addition, when a group of foods is covered as a single
question, assumptions about the relative frequencies of
intakes and portion sizes of the foods must be made when
calculating gram weights or nutrient intakes.
Grouping of items has been shown to lead to an
underestimation of intake87. It may be better to ask
separate questions, although it has also been shown that
increasing the number of items can lead to an over-
estimation of intake88(there are methods available to
adjust for this – see section on Cross-check questions).
The consensus from the group of experts was that single
items are better than grouped foods at least for some items
in a questionnaire. The advantages being that single items
can differentiate between similar foods, e.g. full-fat
versions and low-fat versions of the same food. It is
possible to aggregate single items but not to separate
grouped items. Grouped items can complicate the
question and lengthen the time and effort of completion.
The food list cannot be endless and for practical reasons
some grouping is often necessary. It is important that the
key characteristics for grouping of foods are based on a
priori hypothesis. This will primarily depend on the
purpose of the questionnaire.
Number of food items
The number of food items listed in a food-frequency
questionnaire tends to vary widely. The review found that
the number of food items on a questionnaire ranged from
5 to 350. The median number was 79. Questionnaires with
a more specific remit, such as an FFQ on foods rich in
vitamin A, or fruit and vegetable consumption, may often
be shorter than questionnaires intended to assess the
whole diet. The length of the questionnaire may partly be
determined by the characteristics of the target population
and the number of other questionnaires the subject may
need to complete.
The number of foods included should be considered
along with the validity and reproducibility of the
questionnaire and level of accuracy of dietary data
required. If only a crude assessment of dietary data is
required, it may be that a short food-frequency
questionnaire is sufficient if this measures dietary intake
Development, validation and utilisation of FFQs – review569
to the required accuracy89. Others have shortened longer
questionnaires and re-validated the resultant FFQ16,17,90,91.
Willett67, citing a study conducted by Pietinen et al.92,93
in which a 44-item FFQ was compared with a more
detailed 273-item one, suggests that there is a rapidly
decreasing marginal gain in information obtained with
increasingly detailed questionnaires. There would there-
fore appear to be little to gain in unnecessarily elongating
the number of food items included when developing a
Assembling a list of selected foods
Once the single and grouped items have been selected it is
important to consider the order of the foods in the
questionnaire. To facilitate dietary reporting, food group-
ings should fit within respondents’ conceptual framework.
Related items should be clustered together, such as
traditional food groups.
For closely related foods, more specific items should be
placed before general items (e.g. low-calorie salad
dressing before other salad dressing). Focus groups can
be a useful strategy to help construct lists for culturally
specific questionnaires or to provide information about
which foods should be grouped together.
Food groups of particular interest should be placed near
the beginning of the questionnaire but not at the start.
Errors may be made in the responses to the first few
questions while the participant is getting used to the
format of the questionnaire. Additionally, towards the end
of the questionnaire, the accuracy of responses may
decline due to boredom or fatigue. Therefore it is best to
start with something simple and unambiguous and place
the more important items shortly after this.
Some research groups have experimented with the use
of open-ended questions to record specific types or
brands of foods, such as margarine or breakfast cereals.
However, the limited research that has been conducted in
this area suggests that there is little or no improvement in
validity when subjects are allowed to specify the brand of
breakfast cereal, cooking oil or multivitamin consumed
compared with being asked to select from a more limited
Frequency and portion size
Once the food list is compiled, the next step is to obtain
some measure of the frequency with which each item is
consumed and possibly also some indication of the
amount eaten. Questions on frequency and portion size
should be closed rather than open. This reduces coding
time and transcription errors, and reduces the number of
questionnaires that have to be rejected because responses
are incomplete or cannot be adequately interpreted. If it is
necessary to use open questions it is best to use well-
trained interviewers so that they can ensure the questions
are completed adequately.
Frequency categories should always be continuous,
with no gaps, as the sensitivity of the questionnaire will be
reduced and respondents will be frustrated if they cannot
find their response. The number of choices should range
between 1 and 12 but will mainly depend on the intended
use of the questionnaire.
The range of frequency choices should reflect the time
frame of interest. The frequency categories should
emphasise the more frequent end of the distribution for
most foods (e.g. number of times per week). However, for
foods that are eaten infrequently but make a significant
contribution to nutrient intake (e.g. liver), it is important to
include a less frequent option, say less than once a month.
A few foods are consumed more than once a day. If there
are options of more than once a day this tends to lead to
gross overestimates for some people. The review found
that a variety of different frequency options were used.
Some used ascending whilst other used descending
frequency choices. Some concentrated on foods eaten
on a weekly basis, ignoring foods eaten less than once a
week, whilst others were also interested in foods eaten
more than once a day.
Seasonally consumed items can be problematic when
reporting frequency as they may be consumed very
frequently when in season and then not at all out of
season. A separate section can be included that asks about
consumption of seasonal items when ‘in season’. The data
can then be adjusted at analysis to reflect length of time in
Inclusion of portion size is necessary if gram weights or
nutrient intakes are required. We found from the review
that 22% of food-frequency questionnaires did not present
portion size information, 42% specified a portion size and
36% allowed participants to describe their own portion
size. Research has shown that individuals have difficulty in
estimating portion sizes of foods, both when examining
displayed foods and when reporting about foods
previously consumed94–96. If an individual or the
researcher cannot assign portion size, absolute nutrient
intake cannot be calculated.
When there are no questions in the questionnaire on
portion size, gram weights and nutrient intakes can be
calculated using existing data on average portion sizes
appropriate for the population being studied3. Gender-
specific portion sizes have also been used96.
A portion size may be specified on the questionnaire
and the participants can select a frequency category
according to how often they consume the specified
portion size. If the frequency question is combined with a
specified portion size (e.g. the Harvard Semiquantitative
Food Frequency Questionnaire4), this presents cognitive
challenges for subjects that should be addressed. This is
particularly important when a subject does not consume
the food item in the amounts specified. Under these
circumstances, it is unclear whether the subject will just
ignore the portion size or will select a different frequency
category to allow for the difference in portion.
J Cade et al.570
A third option is to include an additional recording
option for each food to describe the usual portion. This
can be achieved by asking the respondent to describe their
serving as small, medium or large (where the medium
portion is specified). Alternatively, photographs or food
models can be used for respondents to select their own
portion size. However, even within populations the use of
small, medium and large as a description for portion size
may not have an accepted meaning. Between populations,
the value allocated to small, medium or large may be very
Portion sizes should reflect known consumption
patterns in the population, and the questionnaire should
allow for a sufficient range of expression of portion size to
enable subjects with the same frequency of consumption
but different portion sizes to be adequately distinguished.
Use of ‘standard’ portions applied equally to all subjects
simplifies the questionnaire but will reduce sensitivity if
portion sizes vary within the population89.
The choice of whether to include an assessment of
portion size will depend on a number of factors. These
include the availability of average portion size data; the
variability of portion sizes in the population (if there is
little variation in portion sizes then assessment may not be
necessary, especially if absolute intakes are not required);
and ability of the population to accurately assess portion
size and level of accuracy required. Where there is little
information on the usual portion sizes of a population
appropriate serving sizes can be determined by work with
The review found that measures of agreement between
FFQs and a reference dietary measure were highest when
subjects were able to describe their own portion size
(correlation coefficients 0.5–0.6) compared with no
portion size specified (use of average portion weights to
compute intakes: correlation coefficients 0.2–0.5) or
portion size specified on the questionnaire (correlation
coefficients 0.4–0.5). In general, there was little difference
between no portion size specified and whether a portion
size was specified. In terms of repeatability, in general,
correlation coefficients were higher when subjects were
allowed to specify their own portion sizes.
Although not totally consistent across studies, the results
from the review showed that some estimation of portion
size rather than using average portion weights appeared
advantageous. It may be, however, that the minor
improvement in validity obtained when allowing subjects
to specify their own portion sizes does not justify the extra
cost and time involved in development97. The issue of
whether to assess portion size and the best method of
doing it are still matters for discussion and further
research. The group of experts was divided on their
views of the usefulness of portion size estimation. On the
whole there seemed to be agreement that estimation by
subjects of their portion sizes was useful. However, it was
acknowledged that this was not easy to do and more work
was necessary in this area. Many of the experts advocated
using photographs to estimate portion size. Practical
guidelines on the design and analysis of studies to validate
portion size estimates and on the development and use of
photographic atlases for assessing food portion size have
Method of administration
Questionnaires may be either interviewer- or self-adminis-
tered according to the needs of the study. Self-administered
questionnaires require more careful preparation and
A useful way of overcoming limited interviewer
resources is to design a questionnaire that is self-
administered, but to include in the study protocol an
opportunity for the responses to be reviewed and any
queries clarified in a face-to-face or telephone interview99.
Computer-readable forms are useful as they can be
scanned into the computer, hence eliminating data-entry
errors and reducing time. One problem with self-
administered food-frequency questionnaires is incomplete
answers; some respondents will only complete the
questionnaire for items they usually eat. Another common
problem is that complete pages may be missed. A solution
is to check the questionnaire for completeness soon after it
is returned so that incomplete answers can be kept to a
minimum. In their recent paper, Caan et al.100provided a
list of questions for use as a food frequency review probe.
These authors found that using a nutritionist to probe for
correct responses on a self-administered FFQ improved
agreement with a food record used as a reference method.
An alternative to the use of face-to-face interviews is to
administer the FFQ by telephone. The advantages of
telephone interviewing have been reviewed by Fox
et al.101and include higher response rates than postal
surveys and the potential to reach largenumbers of people
in widely scattered geographic areas. Interview by
telephone can be substantially less expensive than
face-to-face interviews, but cost comparisons vary with
the research setting. Posting picture booklets or other
portion size estimation aids to the participants before
the telephone interview can simplify the reporting of
portion sizes by telephone102. However, there are
further cost implications and the booklets also need to
The review found that 67% of questionnaires validated
were self-administered. Correlation coefficients (inter-
viewer vs. self-administered) between FFQs and reference
measures were higher for interviewer-administered ques-
tionnaires than for self-administered questionnaires for
fat (0.55 vs. 0.50), energy (0.55 vs. 0.46) and vitamin A
(0.47 vs. 0.37), were similar for calcium (0.56 vs. 0.55)
and slightly higher for self-administered questionnaires
for vitamin C (0.45 vs. 0.49). Correlation coefficients for
repeatability between interviewer-administered and self-
Development, validation and utilisation of FFQs – review571
for fat (0.65 vs. 0.60), energy (0.67 vs. 0.63) and vitamin A
(0.59 vs. 0.58), but worse for vitamin C (0.59 vs. 0.66).
Computation of food and nutrient intakes
In order to convert frequency estimates of food intake to
nutrient values, an appropriate nutrient database needs to
be constructed. Ritenbaugh et al.103demonstrated that the
choice of nutrient database can have an impact on the
strength of association between a biomarker reference
method and an FFQ designed to assess carotenoids. The
limitations of food tables/databases also need to be taken
into consideration, particularly the extent to which missing
values interfere with the aspects of diet that are to be
assessed, and if and how the limitations can be
If the nutrient content of a food is not known, samples
should be collected and the food analysed chemically.
Alternatively, the nutrient content of mixed dishes can be
estimated from recipes that include foods for which the
nutrient composition is known. All recipe ingredients
need to be weighed or measured. Data on weight losses
associated with cooking (e.g. due to water evaporation)
should be recorded to ensure accurate nutrient density of
the portion size consumed.If individual foods aregrouped
on a questionnaire, then a composite nutrient value for the
group needs to be established. This will depend on the
relative frequency and portion sizes of the individual
A database of portion sizes will also need to be
compiled. Sources of portion size data used for estimation
can be either published values, data from surveys using
weighed records or estimates of specific portion sizes,
ideally by the population group in question94–96,105–109.
Total nutrient intake can be calculated from the sum of
the products of the frequency weight and nutrient content
of the portion of food. Frequency weights can be assigned
to assess weekly or daily consumption (e.g. for daily/once
a day ¼ 1; four times a week, 4=7 ¼ 0:57). In more
complex questionnaires, the nutrient content of some
foods may be modified by responses to other questions,
e.g. margarine by type of margarine. The edible portion of
a food should also be taken into consideration in order to
provide the nutrient values for weight as eaten (for
example, removal of fat or bone weights from meat
Missing data on food-frequency questionnaires can be
treated in a number of ways. Firstly, questionnaires with a
large percentage of incomplete questions should be
excluded. This value needs to be decided a priori and will
depend onthe purpose and level of accuracyrequired. For
questionnaires not exceeding the limit for incomplete
data, a value of zero (food not eaten) may be used;
alternatively an average value for the population could be
Clear instructions should be given at the beginning of the
questionnaire if it is to be self-administered. These are
usually enhanced by the use of relevant examples.
Additional questions on the treatment of fat on meat can
be used to adjust fat intake. Intuitively it might seem that
this type of qualitative information would improve the
validity of total fat or fatty acid estimates, but there is little
evidence, in practice, to suggest that it does110. These
types of additional question on methods of food
preparation and cooking can be placed at the end of the
food frequency section.
Some foods are less easy to assess via FFQ than others
due to the pattern of intake (e.g. milk). Milk may be
consumed frequently in small amounts (e.g. in drinks)
and also less often in large amounts (on cereals, glasses
of milk). It is therefore useful to ask specific questions
about milk in the additional information section. Other
examples that can be included in a separate cross-check
section are questions on bread, sugar and alcohol.
Additional questions could also be asked about key
sources of the nutrients of interest to improve the
accuracy of the data.
Collection of data on supplement use is potentially
important and should be considered at the design stage of
the FFQ8,111. This is a complex area and precise details of
the products consumed are required in order to assess
nutrients from supplements. Furthermore, setting up a
nutrient database for dietary supplements is a costly and
time-consuming process due to the expanding and highly
changeable market in these products.
Cross-check questions can be used to correct for
misreporting of certain food groups. These are often used
for fruits and vegetables as these tend to be overreported,
particularly if each fruit or vegetable is listed singly in a
long list. A cross-check question can be employed by
asking the number of servings consumed per week of
fruits and vegetables. Aweighting factor may then be used
to correct for any overreporting. A separate weighting can
be applied for each subject, but this does assume that all
items are misreported to the same extent112. The newly
estimated amount of foods is then used to estimate both
food and nutrient intakes.
However, the cross-check method may also lead to an
underestimation of intake. For example, people may not
consider fruit juice when asked about portions of fruit.
Although the inclusion of cross-check questions has been
used successfully as a strategy to identify possible
overreporting of fruit and vegetable intakes112, they may
not be as effective when used to assess other foods. Wolk
et al. found that there was a negligible increase in the
validityof fat estimates due touse of cross-check questions
about fat110. If cross-check questions are used to modify
the data, details of the methodology and adjusted and
unadjusted food and nutrient estimates should be
J Cade et al. 572
To ensure that an FFQ is acceptable and understood by the
population in which it is to be used, it is important to pre-
test the questionnaire in the field. Use of cognitive
interviewing techniques can help to pinpoint problems in
design and comprehension of the questionnaire113.
It is important to stress that, before proceeding with an
FFQ, it is recommended that the procedure for data entry,
whether manual or optical, is tested. The analysis program
should also be tested to ensure that there are no mistakes.
Reproducibility of food-frequency questionnaires
To determine whether a food-frequency questionnaire
provides reproducible results is important for all types of
study design. ‘Reproducibility’ can also be thought of as
‘reliability’. The reproducibility of FFQs has generally been
assessed by administering them at two points in time to the
same group of people and correlation coefficients (or
some other test of association) used to assess the
association between the two responses92,93,114–117.
When the food-frequency questionnaire is administered
by an interviewer, two aspects of reliability should be
distinguished: inter-rater reliability and intra-rater
reliability. Inter-rater reliability assesses whether different
interviewers use the questionnaire similarly and achieve
similar answers from the same subjects. Intra-rater
reliability assesses whether repeat administration by the
same interviewer yields the same answers, in the same
way as reproducibility is assessed for self-administered
questionnaires. The statistical methods used are the same
for both aspects of reproducibility.
Repeatability was assessed in only 47% of validation
studies in the review. It is not wise to administer a
questionnaire at a very short interval as respondents may
remember their previous responses. Alternatively, when a
longer interval is used, true changes in dietary habit as
well as variation in response contribute to reduced
The most common method, used in 90% of studies, for
assessing reproducibility was the correlation coefficient.
This method has recently been shown to be flawed
because it does not measure agreement between two
administrations of the questionnaire, only the degree to
which the two administrations are related. Since we use
the same questionnaire on the same people, we would
expect them to be closely related, but this is not the same
as agreement119–123. Other problems include the fact that
the strength of the correlation is dependent onthe range of
values in the population (which itself can be partly
influenced by size of the sample) and the characteristics of
the subjects in the particular sample used. However, due
to the widespread use of correlation for assessing
reproducibility, it may be helpful to use it in conjunction
with another more appropriate method124–128.
Where correlation is used, Pearson correlation coeffi-
cients should be used on normally distributed data and
Spearman rank correlation coefficients should be used
where data are not normally distributed. From the review,
correlation coefficients between the two administrations
of 0.5 to 0.7 were common. Correlations were somewhat
higher for repeat administrations 1 month or less apart
compared with those administered 6 months to 1 year
apart. The time interval between repeat administrations of
the food-frequency questionnaires in the review ranged
from 2 hours to 15 years. In 34% the repeat administration
was between 1 and 6 months later. In 31% it was between
6 and 12 months.
Within-person error can also be corrected for by
estimating the correlation coefficient based on an average
of a large number of replicates for each individual, though
this is not commonly done129.
Preferable to the use of correlation coefficients is the
Bland–Altman method, which assesses the agreement
between the methods across the range of intakes122. This
method was used in less than 10% of studies in the review.
It can determine if there is any systematic difference
between the administrations of the questionnaire (bias),
and to what extent the two administrations agree (limits of
agreement). It also provides a method of assessing
whether the difference between the methods is the same
across the range of intakes, and whether the extent of
agreement differs for low intakes compared with high
intakes. These may be assessed by plotting the difference
between the methods against the average of the two
administrations. The overall mean difference indicates if
one method tends to over- or underestimate and the limits
of agreement (mean difference ^ 2 standard deviations
(SD)) show how well the administrations agree.
The Kappa statistic can be used to compare categories of
food intakes such as frequencies of consumption
measured by two methods. Kappa statistics are not
appropriate for continuous measures, unless the intention
is to subsequently categorise the measure into a number of
Validation of food-frequency questionnaires
Validation of the FFQ method is essential, as incorrect
information may lead to false associations between dietary
factors and diseases or disease-related markers. For a more
detailed discussion of issues important to the correct
validation procedure in dietary studies, refer to publi-
cations by Burema et al.130and Nelson129.
Development, validation and utilisation of FFQs – review573
Validation studies may be carried out to assess whether
the questionnaire is measuring what it should measure or
to assess the degree to which the questionnaire agrees
with a ‘gold standard’ or other methods of measuring diet.
Alternatively, they may be undertaken to assess the level
of measurement error associated with use of the food-
frequency questionnaire (to allow adjustment of the
results of the main epidemiological study for measurement
As even subtle changes in the design of food-frequency
questionnaires may affect their performance, each new
instrument should be validated separately, even if it is
largely based on a previous questionnaire. Questionnaires
may also perform differently in different demographic
groups and cultures.
Sample/population selection in validation studies
In order to validate an FFQ, it needs to be tested on a sub-
sample of the main study population. Age, ethnic group,
gender and health status of the population can affect the
outcome of the validation study129. It is most important,
therefore, that the target population should be similar to
the main study population. The source of subjects for the
validation study should always be stated, and their
characteristics comprehensively described. These subject
characteristics will affect the way that they respond to the
task of completing an FFQ or undertake some other
possibly more demanding method of assessing dietary
intake. It has also been observed that the type of diet
consumed can have an impact on the outcome of the
validation study. McPherson and colleagues131obtained a
high agreement between an FFQ and food records for
estimates of energy, fats and cholesterol which they
attributed partly to the lack of dietary diversity in their
Subjects who volunteer to take part in validation sub-
studies are self-selected, and may therefore respond
differently to an FFQ than non-volunteers. Self-selected
study participants tend to provide more accurate
responses to questionnaires, and they may also have
different dietary habits9.
Sample size for validation studies
Sample size required will depend on the statistical method
being used to assess reproducibility and validity. The
review showed a wide range in sample sizes from 6 to
3750, with a median of 110. Expert statistical advice should
be sought when deciding on the number of subjects to
include in a validation study. The same issues discussed
here also apply to sample size for repeatability studies.
For the Bland–Altman method, the sample size should
be large enough to allow the limits of agreement to be
estimated precisely. Thus a sample size of at least 50, and
preferably much larger (100 or more subjects, say), is
desirable. It is also valuable to take two measurements
on each subject by each method to improve precision and
so that repeatability and validity can be assessed
For the correlation coefficient, the sample size will
depend on the expected association between the two
measures or methods. Based onthe correlation coefficient,
assuming a sufficient number of days of dietary
information are obtained to reasonably describe an
individual’s diet (typically 14 to 28 days), a sample size
of no more than 100 to 200 should be sufficient (as
illustrated by Refs. 65, 92, 93 and 133). Understandably,
however, few studies manage to achieve such a large
number of days of good quality dietary information from
theirsubjects and therefore mostuse between two and five
replicates (days) per subject (as illustrated by Refs.
134–136). If a strategy using a small number of replicates
per subject is employed, the number of subjects needs to
be increased to maintain the same precision of the
corrected correlation coefficient. The sample size used will
inevitably depend on resources.
Sequence of administration
Ideally, the test instrument should be administered prior to
the assessment of the reference measure. Subjects would
normally, in the course of the main investigation in which
the test measure was to be used, encounter it independent
of any other dietary assessment, and the validation process
should mimic this. Secondly, completing the assessment
using the reference measure may in itself draw
respondents’ attention to their diets.
Time frame of reference method
In order to validate the FFQ, the time frame of the
reference method in relation to that of the food-frequency
questionnaire needs to be taken into consideration. In
theory, the food-frequency questionnaire and the refer-
ence method should assess diet over the same time span
(current, past or usual intake). For example, a food-
frequency questionnaire that assesses intake over a period
of a year could be administered twice, a year apart, and
compared with diet records collected at intervals in the
intervening time. If the objective is to determine past
intake by food-frequency questionnaire, this makes the
validation process more difficult.
Reference method selection in validation studies
Avital component of the validation process is the selection
of the appropriate reference method against which to
assess the test measurement. There are considerable
problems involved with measurement of true habitual
dietary intake. Dietary assessments aimed at determining
current intake are likely to interfere with the subject’s
everyday habits and cause a distortion of intake, and
methods aimed at the assessment of past intake are reliant
upon the memory and conceptualisation skills of the
subject. Although there are now good biological measures
for energy137, nitrogen138and sodium intake139, there is no
J Cade et al.574
‘ideal’ method for the measurement of dietary intake as a
whole. In conducting a validation study, food-frequency
questionnaire measures are compared with an alternative,
but not necessarily more accurate, method of assessing
diet. Such a validation study can therefore only indicate
whether the methods give related answers or not. If there
is disagreement between methods the test cannot identify
which method is correct or even whether it accurately
assesses absolute or even relative intake.
The systematic review showed that 75% of studies
validated an FFQ against another dietary method and 19%
of an FFQ validation against another method, e.g. doubly
labelled water, energy expenditure studies or interviews.
Dietary methods used in FFQ validation studies
In theory, the measurement errors of the food-frequency
questionnaire and reference method should be independ-
ent. Possible dietary methods of choice are weighed or
household records or 24-hour recalls. Weighed records,
since portions are weighed, have the least correlated
errors with food-frequency questionnaires. As the errors
are largely independent, if anything, validity tends to be
understated. If the food-frequency questionnaire results
are compared with weighed records, the lack of
agreement can be attributed in part to the within-subject
variance that is inherent in the shorter but more accurate
reference measure. It should not be assumed that the FFQ
estimates true usual intake without the equivalent of
random measurement error (the within-subject variance of
the weighed records).
Weighed records or diet records should be the first
method of choice for validating food-frequency ques-
tionnaires. Although 24-hour recalls are less demanding
for the participant than diet recording and less likely to
influence the actual diet of the subjects, their sources of
error tend to be more correlated with the error in a dietary
questionnaire (e.g. reliance upon memory, conceptualis-
ation of portion sizes and distortion of reported diet).
However, when co-operation or literacy of study subjects
is limited, 24-hour recalls may be more appropriate (as
illustrated in Ref. 135).
or 24-hour recalls shouldbe kept for a sufficient number of
days to represent average intake and cover the interval of
time corresponding to the questionnaire (typically one
year). For example, four days of dietary information
collected four times a year (four days for each season) to
compare with a food-frequency questionnaire assessing
intake over one year140. There is some evidence that
method improves the apparent validity of a question-
naire141. It would appear that efforts to increase the
duration of recording in the reference method provide a
better measure of habitual intake, which is generally more
similar to the type of information generated by an FFQ.
In practice it may be better to collect a sufficient number
of ‘independent’ replicate 24-hour recalls to allow
estimation of the variance components and then use this
information to statistically adjust the comparison of FFQ
and reference method. Although such an approach has
serious flaws (including the necessity to accept a pooled
variance estimate as if it applies correctly to each
individual), it goes a long way towards eliminating the
impact of the random measurement error in the 24-hour
recall and exposing the error term of the FFQ. Using data
from two studies, Stram et al.142presented calculations to
determine the ‘ideal’ number of days of dietary recording
to use in a validation study. They concluded that, in most
settings, the optimal study design will rarely require more
than four or five diet records per subject.
One error common to both test and reference methods
is the use of national food composition tables (e.g. Ref.
143). For a full discussion of the construction, errors and
use of food composition tables in epidemiological
research, see West and van Staveren144.
Any dietary assessment methodology is prone to a
degree of mis- or underreporting. Weighed records and
24-hour recalls are not without errors; therefore it may be
useful to assess their completeness. Levels of under-
reporting were 31% in the Second National Health and
Nutrition Examination Survey (NHANES II)145and 46% for
women and 29% for men in the National Diet and
Nutrition Survey of British Adults146. The use of the
Schofield equations to predict minimum energy intakes
could be employed147to eliminate participants with
unfeasibly low energy intakes.
The review showed that a variety of different dietary
assessment tools were used as a reference measure. Fifty-
six (25%) used the weighed record; 59 (26%) used a food
record/diary (not including weighed diaries); 50 (22%)
used the 24-hour recall; 14 (6%) used the diet history
questionnaire; and 27 (12%) used another food-frequency
questionnaire. One hundred and forty-four (64%) vali-
dation studies used only one reference method (14
another FFQ, 43 weighed record, 29 24 hour-recall, 58
either a food record or diet history questionnaire). Seven
(3%) validation studies used both the weighed record and
24-hour recall as reference methods.
There was little difference in correlation coefficient
between the different reference measures for energy
ðr ¼ 0:47Þ; fat ðr ¼ 0:51Þ; vitamin A ðr ¼ 0:39Þ and
calcium ðr ¼ 0:54Þ: For iron and vitamin C a higher
correlation coefficient was found using the weighed
record (iron, r ¼ 0:51; vitamin C, r ¼ 0:50) compared with
the non-weighed record (0.41, 0.46) or 24-hour recall
(0.43, 0.41, respectively).
Isotope and biochemical techniques
In recent years, there has been an increase in the use of
biochemical measurements (biomarkers) of nutrients in
blood or other tissues both as a general determinant of
Development, validation and utilisation of FFQs – review575
nutritional status and also to provide a comparison with
other dietary reference methods. Although biomarkers can
provide an estimate of dietary intake that is independent
of the subject’s reported dietary intake (and therefore less
prone to errors involved with underreporting or poor
memory), they are often expensive, invasive and nutrient-
specific, so may only be used to validate one nutrient at a
In general, there is a need to establish how tissue levels
equate to consumption. Certain biomarkers, for example
urinary nitrogen, relate directly to nitrogen intake. In
others (e.g. vitamin C) the relationship is much more
complex. In terms of validation studies, there is a need to
be clear about just what the biomarker measures. Many, if
not most, biomarkers do not permit an assessment of true
Biochemical reference standards are subject to three
sources of error:
1. the difference between the dietary assessment and the
2. the effects of digestion, absorption, uptake, utilisation,
metabolism, excretion and homeostatic mechanisms,
all of which bear on the relationship between the
amount ingested and the biochemical measurement;
3. the error associated with the biochemical assay itself.
It is clear, therefore, that the biochemical marker and
dietary assessment method do not measure the same
thing. The errors for biochemical measures are independ-
entof errors associated
When correlation coefficients for studies using a dietary
reference method were compared with those of studies
using biomarkers, little difference was observed for
energy, fat, vitamin C or vitamin A. Correlations were in
the region of 0.50 for all of these nutrients except vitamin
A, where the correlation coefficient for FFQ vs. dietary
method was 0.40 and that for FFQ vs. biomarker was 0.35.
The repeatability of the biomarker should also be
evaluated. Diurnal variation estimates are available for
some of the markers and should be taken into
account unless blood-sampling time is standardised
or incorporated as a variable in analyses.
One further factor to be taken into account when using
a biomarker within a validation study is how biological
variation in the biomarker relates to variation in intake. For
example, it is known that, with an appropriate lag period,
urinary urea tracks protein intake. Comparing protein
intake estimated from daily urinary urea samples against
an FFQ estimate of habitual protein intake may not be
appropriate. On the other hand, with a marker that can be
expected to show a wide seasonal variation, for example
serum carotenoids, it is essential that the biomarker
information is collected on days that are representative of
the total frame of the FFQ.
An absolute bias has a very limited impact in many
epidemiological studies but is devastating in any attempt
to assess apparent nutrient adequacy. Random measure-
ment error has serious repercussions in epidemiological
studies. Bias that is consistent within an individual but
random between individuals can be misleading.
There are several statistical approaches to validation,
and often several reference methods to validate the food-
frequency questionnaire against. Using more than one
approach demonstrates the robustness of the validation
Correlation, regression and the Bland–Altman method
The same arguments apply to statistical assessment of
validity as to reproducibility. However, correlation and
regression can be useful in helping to assess validity,
because investigation of the association between different
methods can be informative. Correlation coefficients were
by far the most common statistical method and were used
in 168 (83%) of validation studies in the review.
Regression can be used to calibrate one method
compared with another. Regression analysis was under-
taken by eight (4%) of the studies. Where correlation or
regression is used, this should be alongside the Bland–
Altman analysis and not as a replacement119–123. These
methods apply to continuous data; however, with ordered
categorical data, Kappa should be used. Where Kappa
statistics are not practical, Spearman’s correlation may be
used instead as the best tool available. Sensitivity,
specificity, etc. may also be useful for binary data.
Theimportant aspects of validity will vary depending on
the purpose of the food-frequency questionnaire. It is not
possible to produce recommendations on an ideal mean
difference, limits of agreement, correlation or regression
slope, as these will depend on the study objectives.
However, for lower correlations, say below 0.3 or 0.4,
attenuation will be so severe that it will be difficult to
Comparison of group means
Where differences between subject groups are required or
when absolute intakes are important, the validation study
should assess the ability of the test measure to reflect the
group mean129. This may be achieved by using paired
t-tests (on normally distributed data), which is the
equivalent of testing the overall bias in the Bland–Altman
method. For food data, distributions are less likely to be
parametric and non-parametric tests may be more
appropriate (such as the Wilcoxon signed rank sum test).
Statistical tests used depend on the variation in the data.
For example, the unpaired t-test depends on the standard
deviation of the differences and hence on the width of the
limits of agreement from the Bland–Altman analysis. If
these limits are wide, then a substantial overall bias (big
difference between the two methods of assessing diet)
J Cade et al.576
may well be non-significant, and therefore overlooked.
P-values should therefore be used with caution, and a
general assessment of the magnitude of possible
Classification into categories of consumption
For both the test and reference methods subjects may be
divided into categories relating to the distribution of
dietary intake (e.g. fifths of intake). A comparison of the
subjects’ categories shows whether subjects were classi-
fied in the same or different categories by the two
methods. The results permit an assessment of the
proportion of subjects who are classified correctly. This
method gives a much clearer and undistorted picture of
how well the instrument is doing compared with
correlation coefficients. Data are usually divided into
three or five categories. Results can be reported as an exact
agreement (classified in the same category by both
methods), þ/21 category and gross misclassification.
Agreement can be assessed using the Kappa statistic or
sensitivity/specificity can be calculated for dichotomised
Other statistical methods for validation studies
Other modelling-based approaches to validation have
been developed. These include using components of
variance148to calculate the intra-class correlation coeffi-
cient149. Analternative approach is the method of triads150.
Thirdly, in recent years there has been increased interest in
the use of more advanced modelling, e.g. using structural
equation models, for dietary validation studies (for
illustration see Refs. 151–154). These can be seen as a
generalisation of the approaches based on components of
variance and the method of triads, incorporating them in
one framework. More complicated models are possible
which may, in principle, resolve the problem of correlated
random errors between FFQ and the reference method151.
The statistical methods underlying these approaches are
complex and expert statistical advice should be sought if
these methods are to be used.
Utilisation of food-frequency questionnaires
Food-frequency questionnaires have been used in a large
number of different studies. The review located 164
studies, published in 1998, in which food-frequency
questionnaires were used to assess dietary intakes (not
validation studies). Of the studies considered, 60%
reported data on nutrient intakes and 46% reported data
on food/food group consumption. Sixty-one per cent of
studies used self-administered questionnaires and 58%
reported an associated validation paper.
The FFQs were used in randomised control trials (2%),
cohort studies (20%), case–control studies (26%) and
cross-sectional studies (51%). The aim of the FFQ was to
assess general dietary information only (foods, food
patterns or nutrient intakes) in 32 (20%); diet–disease
relationships in 82 (50%) studies; and dietary intakes with
biochemical or physical measures in 31 (19%).
There are a number of points to consider when
reporting the use of an FFQ in a paper. The key aspects to
include are summarised in the recommendations arising
from this review. Examples of good practice in terms of
description of an FFQ can be found in Refs. 155–158 and
for reporting previous validation in Refs. 159–162. If as a
result of a validation study the dietary results are adjusted
for measurement error, then details of the method of
adjustment must be given. The consensus from the group
of experts was that they have reservations about adjusting
for measurement error especially if the adjustments
generate large changes in the dietary estimates.
Issues specific to different study designs
Food-frequency questionnaires have been designed and
used in a wide range of situations, and types of dietary
study. In this section, FFQ design and validation issues
specific to each type of study design are discussed.
Cross-sectional studies investigate relationships at a single
point in time and, as such, are unable to generate
information on causality. However, they have been used
to provide group comparisons, ranking of individuals and
an assessment of usual dietary intake163–166. If the
questionnaire aims to look at the percentage failing to
meetnutritional requirements thenissues ofsensitivity and
specificity also need to be addressed.
Brief questionnaires designed to measure specific
dietary behaviours (e.g. fruit and vegetable consumption)
may be useful in lifestyle type surveys in which the
number of dietary questions needs to be limited167–169. If
the cross-sectional study aims to compare different
subgroups of the population, for example the effect of
age group or gender, then the food-frequency ques-
tionnaire should be validated for each of the important
Case–control (retrospective) studies
Unlike cross-sectional studies, case–control studies have
been used to provide support for a causal link between
diet and disease (as illustrated by Refs. 170–172). Food-
frequency questionnaires are a popular tool in this type of
study, although the need to obtain dietary information
retrospectively, i.e. before the onset of the disease, raises a
number of design and validation concerns.
When designing the FFQ for use in a retrospective
case–control study, the food list used should reflect
dietary consumption at the relevant time point. The effect
of memory is important and largely relates to the omission
of foods. The number of foods recalled tends to be
correlated with total intake of energy and nutrients, thus
Development, validation and utilisation of FFQs – review577
differential misclassification will occur between those with
good and poor memories.
Evidence exists that people whose dietary habits are
relatively stable are more likely to be able to successfully
recall past diet. Additionally, greater total diet reproduci-
bility has been found among men with higher education,
among women of less than 110% desirable weight
reporting no special diet and among women reporting
The presence of disease in cases may interfere with the
ability to complete a questionnaire and it may be that an
interview-based design would be required. Recall or
memory of past diet may also vary between cases and
controls due to the effects of the disease process itself or
drugs used in treatment. An understanding of the typical
development time of the disease may also be required in
order to set the appropriate time frame of reference for the
Validation is a particular issue in retrospective studies. It
is not easy to validate a questionnaire inquiring about
eating habits in the past, and the experts were divided
about the ability of FFQs to function in this way. Some
research groups have reported useable recall of past diet
with FFQs173–175, whereas others have found that the past
diet correlates as well with the current diet as with the
The questionnaire used should ideally be validated for
use with both cases and controls as the questionnaire may
be handled differently in each group64. Some research
groups have looked at the reliability of response in
hospital controls, where there is a danger that recall of past
diet may be confused with diet consumed while in
hospital. D’Avanzo et al.178found satisfactory compar-
ability of dietary information from subjects interviewed at
home with that provided during their original interview in
the hospital, and a good reproducibility of information
collected in the two settings.
Cohort (prospective) studies
In general, the sample size for a cohort study will be
considerably greater than for a case–control study and,
due to their ease of completion and analysis, FFQs have
been used extensively in this type of study (as illustrated
by Refs. 179–185).
In terms of FFQ development, a number of issues are
specific to their use in cohort studies. As the duration of
cohort studies generally stretches over a number of years
or even decades, there may be a need to repeatedly assess
diet in the cohort. However, the questionnaire may
ultimately become somewhat ‘out-of-date’ as new foods
become available over the duration of the study and
dietary patterns change.If the FFQ is to be repeatedduring
the study it may need to be adjusted to include these new
Since the dietary component implicated in the devel-
opment of the disease may not be known at the start of the
study, and new issues may develop over time, it may be
better to comprehensively measure the whole diet at the
onset of the study. The FFQ should be designed to allow
this. The number of times diet is measured in a cohort
study will partly depend on resources and whether dietary
changes are anticipated. If dietary habits do change over
time and different versions of the questionnaire are used, it
is important to assess whether the differences are real or a
result of different questionnaires. The food-frequency
questionnaire may also be used to cluster participants in
terms of dietary patterns rather than just nutrient
Validation will be undertaken at baseline, but may also
be assessed at follow-ups to ensure the level of validity has
not changed. However, under these circumstances it is
difficult to tell if validity has changed or if there has been a
change in dietary pattern, due for example to the changes
in the food market65.
In an intervention study, a food-frequency questionnaire
may be used to track changes in diet as a response to some
form of intervention (e.g. education). As such, it must be
sensitive enough to detect sometimes quite subtle dietary
changes. However, food-frequency questionnaires may
not be the most appropriate method to use in intervention
studies, as they may not be specific enough to detect
changes in diet. More importantly, the subjects may report
what they consider to be the desirable responses – this
would be more difficult to maintain if reporting diet
prospectively over a number of days. If an intervention is
trying to improve the diet as a whole, intermediate
behavioural targets (such as trimming fat from meat,
substituting fruit for pastry snacks, etc.) should be
measured directly by including additional questions on
the food-frequency questionnaire.
Dietary screening in clinical settings
The main objective of questionnaires being used in a
clinical setting may be to discriminate between high and
low consumers of certain foods or nutrients. Time and cost
are usually constraints under these circumstances and
questionnaires with a long food list may not be practical.
Potentially more useful are shorter questionnaires that
include foods/food groups that discriminate between high
and low intakes and that are suitable for administration by
staff without specialised nutrition training, as for example
with the DINE questionnaire devised by Roe and
colleagues71. However, the questionnaires will need to
be both sensitive and specific in identifying ‘at risk’
In clinical settings, such questionnaires have been used
to screen for low-fat diets11, to assess diet in children with
diabetes189or those at risk of iron-deficiency anaemia190,
to assess dietary behaviour in the workplace70, and for use
in practice nurse dietary assessments191. Food-frequency
J Cade et al. 578
questionnaires have also been used as a screening tool to
determine study eligibility, for example, as used by
Ritenbaugh and colleagues192to exclude high fibre
consumers for a cancer prevention trial.
Issues of determining absolute intakes may not apply
when used as a general screening tool. If the questionnaire
is to be used to identify patients who require dietary
advice, for example to identify patients with high fat
intakes or low fruit and vegetable intakes, then the
sensitivity and specificity characteristics of the instrument
are more important than absolute intakes. For example, it
is important to correctly classify those with high-fat diets
so that they can subsequently receive the appropriate
advice. Alternatively, the questionnaire must be specific so
that those with the correct amount of fat in the diet are not
classified as having a high-fat diet and given inappropriate
Many studies have been devoted to the methods of
measuring an individual’s usual dietary intake. Currently,
food-frequency questionnaires are being used in a variety
of ways and different study designs. They are most
commonly used to obtain estimates of an individual’s food
intake in relation to the development of various diseases.
This review was prepared to guide the individual about
to embark on the development and/or use of a food-
frequency questionnaire as a dietary assessment tool.
Since the development of a new FFQ is costly, both in
terms of time and resources, the issues considered to be of
key importance have been summarised in the recommen-
dations. Similarly, the adoption of a pre-existing FFQ
poses particular problems according to its ultimate
function, and these are also highlighted in this document.
It is well recognised that there is no gold standard for
directly assessing the validity of FFQs. However,
consideration has been given to the methods available
and the overall design of validation studies, and this may
provide guidance for those wishing to conduct a
validation study on either a new or pre-existing FFQ.
Lastly, the review also provides a breakdown of the
ways in which FFQs are currently being used either
clinically or in research. It is hoped that these data may
guide the individual who is seeking advice about the
design and or/validation issues surrounding the use of
FFQs under these different circumstances.
This project was funded by the Ministry of Agriculture,
Fisheries and Food (Project AN0850).
Thanks to all those scientists who contributed their
views and comments. In particular: George Beaton,
Gladys Block, Tim Byers, Imogen Cowin, Pauline Emmett,
Darren Greenwood, Allan Hackett, Rudolf Kaaks, Sara
Kirk, Christel Larsson, Jenny Matthew, Jane Pryer, Sian
Robinson, Chris Sempos, Margaret Thorogood, Ailsa
Welch and Walter Willett.
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