Nutrition in the Prevention and Treatment of Disease

Chapter 1

Dietary Assessment Methodology

Frances E. Thompson and Amy F. Subar

National Cancer Institute, Bethesda, MD, United States

I INTRODUCTION

This chapter is a revision of the similarly named chapter

in the earlier editions [13] of this book, which itself was

based on the “Dietary Assessment Resource Manual” [4]

by Frances E. Thompson and Tim Byers, adapted with

permission from the Journal of Nutrition. Dietary assess-

ment encompasses food supply and production at the

national level, food purchases at the household level, and

food consumption at the individual level. Th is review

focuses only on individual-level food intake. It is intended

to serve as a resource for those who wish to assess diet in

a research study, for example, to describe the intakes of a

population, using individual measurements for group-

level analysis. This chapter does not address clinical

assessment of individuals for individual counseling. The

first section reviews major dietary asse ssment methods,

their advantages and disadvantages, and validity. The

next sections describe which dietary assessment methods

are most appropriate for different types of studies and for

various types of populations. Finally, specific issues that

relate to all methods are discussed.

II DIETARY ASSESSMENT METHODS

A Dietary Records

In the dietary record approach, the resp ondent records the

foods and beverages and the amounts of each consumed

over one or more days. Ideally, the recording is done at

the time of the eating occasion in order to avoid reliance

on memory. The amounts consumed may be measured,

using a scale or household measures (e.g., cups or table-

spoons), or estimated using mode ls, pictures, or no aid. If

multiple days are recorded, they are usually consecutive,

and no more than 7 days are included. Recording periods

of more than 4 conse cutive days are usually unsatisfac-

tory, as reported intakes decr ease [5] due to respondent

fatigue, and individuals who do comply may differ sys-

tematically from those who do not. Because the foods and

amounts consumed on consecutive days of reporting may

be related (e.g., leftovers and eating more one day and

less the next day), it may be advantageous to collect non-

consecutive single-day records in order to increase repre-

sentativeness of the individual’s diet.

To complete a dietary record, each respondent must be

trained in the level of detail required to adequately describe

the foods and amounts consumed, including the name of

the food (brand name, if possible), preparation methods,

recipes for food mixtures, and portion sizes. In some stud-

ies, this is enhanced if the investigator contacts the respon-

dent and reviews the report after 1 day of recording. At the

end of the recording period, a trained interviewer should

review the records with the respondent to clarify entries

and to probe for forgotten foods [6]. Dietary records also

can be recorded by someone other than the subject, such as

parents reporting for their children.

The dietary record method has the potential for pro-

viding quantitatively accurate information on food con-

sumed during the recording period [7]. By recording

foods as they are consumed, the problem of omission may

be lessened and the foods more fully described.

Furthermore, reporting amounts of food as they are con-

sumed should provide more accurate portion size informa-

tion than if the respondents were recalling portion sizes of

foods previously eaten.

Although intake data using dietary records are typi-

cally collected in an open-ended form, close-ended forms

have also been developed [810]. These forms consist of

listings of food groups; the respondent indicates whether

that food group has been consumed. In format, these

“checklist” forms resemble food frequency questionnaires

Nutrition in the Prevention and Treatment of Disease. DOI: http://dx.doi.org/10.1016/B978-0-12-802928-2.00001-1

2017, Published by Elsevier Inc.

(FFQs) (see Section II.C). Unlike FFQs, which generally

query about intake over a specified time period such as

the past year or month, checklists are intended to be filled

out concurrently with actual intake or at the end of a day

for that day’s intake. A checklist can be developed to

assess particular “core foods” that contribute substantially

to intakes of some nutrients [11], and it also has been

used to track food contaminants [12]. Portion size can

also be asked, either in an open-ended manner or in

categories.

A potential disadvantage of the dietary record method

is that it is subject to bias both in the selection of the sam-

ple and in the sample’s completion of the number of days

recorded. Dietary record keeping requires that respondents

or respondent proxies be both motivated and literate

(except for photograph-based methods), which can poten-

tially limit the method’s use in some population groups

(e.g., low literacy, recent immigrants, childr en, and some

elderly). The requirements for cooperation in keepi ng

records can limit who will respond, compromising the

generalizability of the findings from the dietary records to

the broader population from which the study sample was

drawn. Research indicates that incomplete records

increase significantly as more days of records are kept,

and the validity of the collected information decreases in

the later days of a 7-day recording period, in contrast to

information collected in the earlier days [5]. Part of this

decrease may occur because many respondents develop

the practice of filling out the record retrospectively rather

than concurrently. When respondents record only once

per day, the reco rd method becomes simi lar to the 24-

hour dietary recall in terms of relying on memory rather

than concurrent recording.

An important disadvantage of this method is that

recording foods as they are being eaten can affect both

the types of food chosen and the quantities consumed

[1315]. The knowledge that foods and amounts must be

recorded and the demanding task of doing it may alter the

dietary behaviors the tool is intended to measure [16],

creating “reactivity bias.” This effect is a weaknes s when

the aim is to measure typical dietary behaviors. However,

when the aim is to enhance awareness of dietary beha-

viors and change them, as in some intervention studies,

this effect can be seen as an advantage [17]. Recording,

by itself, is an effect ive weight loss technique [18,19].

Recent interest in “real-time” assessment has led to the

development of numerous mobile “apps” for self-

monitoring that enable concurrent recording and immedi-

ate, automated feedback. This approach generally has

been found to improve self-monitoring and adherence to

dietary goals compared with traditional paper-and-pencil

dietary records [20,21].

A third disadvantage is that unless dietary records are

collected electronically, the data can be burdensome to

code and can lead to high personnel costs. Dietary assess-

ment software that allows for easier data entry using com-

mon spellings of foods can save considerable time in data

coding. Even with high-quality data entry, maintaining

overall quality control for dietary records can be difficult

because information often is not recorded consistently

among different respondents, nor is the information coded

consistently among different coders. This highlights the

need for training of both the respondents and the coders.

Several approaches using a variety of technological

advances have been used to allow easier data capture and

less respondent burden; some may be particularly benefi-

cial among low-literacy groups. For example, a computer-

administered instrument allows the respondent to select

the food consum ed and the appropriate portion size from

photographs on a screen

[22,23]; this can be done using

touch-screen technology [24]. The proliferation of mobile

devices with cameras allows simultaneous photographic

records of the foods selected [25]. However, for this

approach to be quantifiable, before and after pictures of a

consumption event and training of the participant in how

to consistently take pictures using a standard reference

object are required. Wearable cameras which can continu-

ously take pictures or videos have been developed

[26,27], lessening the burden on the respondent and

potentially allaying some reactivity (i.e., changes in the

respondent’s behavior that are caused by the instrument).

These methods have great potential to improve portion

size accuracy.

Automated processing of the image information for

these methods is not yet fully developed. The images that

illustrate the beginning of the consumption event and its

completion must be selected, the food has to be identified

[28], and the mathematical properties of the food image

need to be quantified [29] in order to develop an accurate

estimate of the food’s volume. However, if these pro-

blems can be solved, the foods can be linked to appropri-

ate databases (see Section V.E), dramatically reducing the

burden of coding [30]. In the meantime, the images could

be identified manually by staff or the respondent in an

accompanying application, and later coded.

Respondent burden and reactivity bias may be less

pronounced for the “checklist” [31], because checking off

a food item is easier than recording a complete descrip-

tion of the food [32], and the costs of data processing can

be minimal, for example, paper forms that are machine

scannable, or electronic forms on a computer or mobile

device. Checklists are often developed to assess particular

foods that contribute substantially to intakes of some

nutrients. As the comprehensiveness of the nutrients to be

assessed increases, the length of the form also increas es,

and it becomes more burdensome to complete at each eat-

ing occasion and may increase reactivity. Nonetheless,

precoded food diaries to assess diet have been developed,

6 PART | A Assessment Methods for Research and Practice

evaluated, and used: the precoded food diary used in the

200508 Danish National Survey of Diet and Physical

Activity contained about 400 items and portion size

choices [33]; a precoded food diary used in Norway con-

tained 277 items [34]. However, checklists are limited in

their ability to assess the diet, because of lack of details

on the particular food consumed, food preparation, por-

tion sizes, and other relevant information.

Food records have been evaluated most frequently

through comparison to another instrument, often 24-hour

recalls. However, no self-report instrument is without

reporting error, and thus relative validation is not neces-

sarily useful. Instead, when possible, validation studies

should consider using “recovery” biomarkers that are

unbiased reference instruments. Only a few are currently

available. These are total energy expenditure from doubly

labeled water for energy [35], and protein (nitrogen) [36],

potassium [37], and sodium based on 24-hour urine col-

lections [38]. Many studies in selected small sample s of

adults indicate that reported energy and protein intakes on

dietary records are underestimated in the range of 437%

compared to energy expenditure as measured by doubly

labeled water and protein intake as measured by urinary

nitrogen [18,3953]. In the largest doubly labeled water

study using food records, with about 450 postmenopausal

women in the Women’s Health Initiative, energy and pro-

tein intakes reported on food records were underestimated

by about 20% and 4%, respectively, and protein density

(kcal of protein as a percentage of total kcal) was overes-

timated by about 17% [54]. Underreporting on dietary

records is probably a result of the combined effects of

incomplete and inaccurate recording and the impact of the

recording process on dietary choices leading to undereat-

ing, and thus not typical of usual intake [18,48,55,56].

The highest levels of underreporting on dietary records

have been found among individuals with greater body

mass index (BMI) [41,43,44,54,57,58], particularly

women [41,43,44,52,5961] . This effect, however, may

be due, in part, to the fact that overweight individuals are

more likely to be dieting on any given individual day

[62]. These relationships between underreporting and

BMI and sex have also been found among elderly indivi-

duals [63]. Other research shows that demographic or psy-

chological indices such as education, employment, social

desirability, body image, or dietary restraint also may be

important factors related to underreporting on diet records

[41,48,60,61,6467]. A few studies suggest that energy

underreporters compared to others have reported intakes

that are lower in absolute intake of most nutrients [58],

higher in percentage of energy from protein [58,61], and

lower in percentage of energy as carbohydrate

[58,61,68,69] and in percentage of energy from fat [69].

Correspondingly, energy underreporters may report lower

intakes of desserts, sweet baked goods, butter, and

alcoholic beverages [58,69], but more grains, meats, sal-

ads, and vegetables [58]. Some research has examined the

performance of food checklists relative to accelerometry

[70] or, more commonly, complete dietary records

[8,9,32], 24-hour dietary recalls [11], dietary history [71],

and biological markers [71]. An evaluation study of the

7-day precoded food diary used in the Danish National

Survey of Dietary Habits and Physical Activity 200002

reported that energy intake was underestimated by 12%

compared to accelerometer [70].

Some appro aches have been suggested to overcome

underreporting in the dietary record. These include

enhanced training of respondents and incorporating psy-

chosocial questions known to be related to under reporting

in order to control for the effect of underreporting [56].

Another approach is to calibrate dietary records to doubly

labeled water or urinary nitrogen, biological indicators of

energy expenditure and protein intake, resp ectively,

including covariates of sex, weight, and height, to more

accurately predict individuals’ energy and protein intake

[72]. This approach was applied to a subcohort of the

Women’s Health Initiative. Calibration equations that

included BMI, age, and ethn icity explained much more of

the variation in the energy and protein biomarkers than

did calibration without the covariates, for example, 45%

versus 8% for energy [54]. Further research is needed to

test this approach in other populations and to develop and

test other modeling approaches.

B 24-Hour Dietary Recall

In the 24-hour dietary recall, the respondent is asked to

remember and report all the foods and beverages consumed

in the preceding 24 hours or on the preceding day. The

recall typically is conducted by interview, in person or by

telephone [73,74], either computer-assisted [75] or using

a paper-and-pencil form, although self-administered com-

puter administration is becoming more prevalent [7680].

When interviewer-administered, well-trained interviewers

are crucial because much of the dietary information is

collected by asking probing questions. Interviewers should

be knowledgeable about foods available in the marketplace

and about preparation practices, including prevalent regional

or ethnic foods.

The interview is often structured, usually with specific

probes, to help the respondent remember all foods con-

sumed throughout the day. An early study found that

respondents with interviewer probing reported 25% higher

dietary intakes than did respondents without interviewer

probing [81]. Probing is especially useful in collecting

necessary details, such as how foods were prepared. It is

also useful in recovering many items not originally

reported, such as common additions to foods (e.g., butter

on toast) and eating occasions not originally reported

Dietary Assessment Methodology Chapter | 1 7

(e.g., snack s and beverage breaks). However, interviewers

should be provided with standardized neutral probing

questions so as to avoid leading the respondent to specific

answers when the respondent really does not know or

remember.

The current state-of-the-art 24-hour dietary recall pro-

tocol in the United States is the U.S. Department of

Agriculture’s (USDA) Automated Multiple-Pass Method

(AMPM) [82,83], which is used in the U.S. National

Health and Nutrition Examination Survey (NHANES).

The AMPM five-pass method consists of (1) an initial

“quick list,” in which the respondent reports all the foods

and beverages consumed, without interruption from the

interviewer; (2) a forgotten foods list of nine food catego-

ries commonly omitted in 24-hour recall reporting; (3)

time and occasion, in which the time each eating occasion

began and what the respondent would call it are reported;

(4) a detail pass, in which probing questions ask for

more detailed information about the food and the portion

size, in addition to review of the eat ing occasions

and times between the eating occasions; and (5) final

review, in which any other item not already reported

is asked [82,83]. In addition, a two-dimensional Food

Model Booklet [84], developed from USDA research, is

used in the NHANES in order to facilitate more accurate

portion size estimation. A 24-hour recall interview usin g

the multiple-pass approach typically requires betwee n

30 and 45 minutes.

Data processing software systems are currently avail-

able in most developed countries, allowing direct coding

of most foods reported during the interview. This is

highly efficient with respect to processing dietary data,

minimizing missing data, and standardizing interviews

[85,86]. If direct coding of the interview is done, methods

for the interviewer to easily enter those foods not found

in the existing database should be available, and appropri-

ate use of these methods should be reinforced by inter-

viewer training and quality control procedures.

A huge technological advance in 24-hour dietary recall

methodology is the development of automated self-

administered data collection instruments [76,7880,

8791]. These systems vary in their design, inclusion of

probes regarding details of foods consumed and possible

additions and omissions, the approach to asking about

portion size, and the number of foods in their databases.

The Automated Self-Administered 24-hour dietary recall

(ASA24) developed at the National Cancer Institute

(NCI) [76,90,91] incorporates many elements of the

AMPM 24-hour interview developed by USDA [82].

Prompts used in the AMPM are asked in the program .

Portion sizes are reported using digital photographs depict-

ing up to eight sizes as portion size aids [91]. The system

uses the most current USDA survey database [92] to allow

automated coding and processing and ultimately estimation

of nutrient and food group intakes. The ASA24 system is

freely available for web or mobile phone administration

[76]. Such automated tools make feasible the collection of

high-quality dietary data in large-scale population research.

Automated self-administered recalls have been compared

to interviewer-administered recalls. One study in adoles-

cents found that differences between interviewer- and self-

administered recalls were minimal [80]. A feeding study of

86 adults found that the AMPM and the ASA24 were com-

parable in their agreement with observed intake [93].

Additionally, a large field study in 1083 adults found

that nutrient and food group intakes estimated from

AMPM and ASA24 recalls were comparable, and that

the ASA24 was preferred over the AMPM by 70% of the

participants [94].

There are many advantages to the 24-hour recall.

When an interviewer administers the tool and records the

responses, literacy of the respondent is not required. For

self-administered versions, literacy can be a constraint.

Because of the immediacy of the recall period, respon-

dents are generally able to recall most of their dietary

intake. Because there is relatively little burden on the

respondents, those who agree to do 24-hour dietary recalls

are more likely to be representative of the population than

are those who agree to keep food records. Thus, the 24-

hour recall method is useful across a wide range of popu-

lations. In addition, interviewers can be trained to capture

the detail necessary so that new foods reported can be

researched later by the coding staff and coded appropri-

ately. Finally, in cont rast to record methods , dietary

recalls occur after the food has been consumed, and if

unscheduled, reactivity is not a problem.

The main weakness of the 24-hour recall approach is

that individuals may not report their food consumption

accurately for various reasons related to knowledge,

memory, and the interview situation. These cognitive

influences are discussed in more detail in Section V.A.

A potenti al limitation, as is true for f ood records, is that

multiple days of recalls may be needed for the study

objective. Whereas a single 24-hour recall can be used

to describe the average dietary intake of a population,

multiple days of recalls are needed to model estimates

of the population’s usual intake distributions. Multiple

administrations of 24-hour recalls also allow more

precise estimation of relationships with other factors

(see Section V.G).

As with other self-report instruments, relative valida-

tion, for example, comparing 24-hour recalls with food

records, is not particularly useful. The validity of the 24-

hour dietary recall has been studied by comparing respon-

dents’ reports of intake either with intakes unobtrusively

recorded/weighed by trained observers or with recovery

biomarkers. Numerous observat ional studies of the perfor-

mance of the 24-hour recall have been conducted with

8 PART | A Assessment Methods for Research and Practice

children (see Section IV.C). In studies of adults, group

mean nutrient estimates from 24-hour recalls have been

found to be similar to observed intakes [5,95], although

respondents with lower observed intakes have tended to

overreport energy and those with higher observed intakes

have tended to underreport energy [95]. One observationa l

study found energy underreporting during a self-selected

eating period in both men and women, similar underre-

porting during a controlled diet period in men, and accu-

rate reporting during a controlled diet period in women;

underestimates of portion sizes accounted for much of the

underreporting [96]. A study of adults comparing AMPM

and ASA24 to observed intake found that both protocols

captured about 80% of the foods and drinks actually con-

sumed; there were few differences in nutrient and food

group intakes between observed and reported for both

protocols [93]. Studies with the recovery biomarkers of

doubly labeled water and urinary nitrogen generally have

found underreporting using 24-hour dietary recalls for

energy in the range of 334% [22,42,79,83,97103],

with the largest two studies in adults using a multiple-

pass method showing average underreporting to be

between 12% and 23% [83,100]. For protei n, under-

reporting tends to be in the range of 1128%

[97,100,101,103107]. An analysis of data pooled from

five of the larger recovery biomarker studies found an

average rate of underreporting of 15% for energy and 5%

for protein [108]. However, underreporting is not always

found. Some studies found overreporting of energy from

24-hour dietary recalls compared to doubly labeled water

in proxy reports for young children and adolescents

[109,110]. In addition, it is likely that the commonly

reported phenomenon of underreporting in Western coun-

tries may not occur in all cultures; for example, Harrison

et al. [111] reported that 24-hour recalls collected from

Egyptian women were well within expected amounts.

Finally, in many studies, energy adjustment has been

found to reduce error. For example, for protein density

(i.e., percentage energy from protein), 24-hour dietary

recalls conducted in the large biomarker studies were in

close agreement or somewhat higher compared to a

biomarker-based measure [54,100,101].

In past national dietary surveys using multiple-pass

methods, findings suggest that energy underreporting may

affect up to 15% of all 24-hour recalls [112,113].

Underreporters compared to nonunderreporters tended to

report fewer numbers of foods, fewer mentions of foods

consumed, and smaller portion sizes across a wide range of

food groups and tended to report more frequent intakes of

low-fat/diet foods and less frequent intakes of fat added to

foods [112]. As was found for records, factors such as BMI,

sex, social desirability, restrained eating, education, literacy,

perceived health status, and race/ethnicity have been shown

in various studies to be related to underreporting in recalls

[48,54,62,64,83,98,106,108,112116]. The 24-hour dietary

recall is considered the least biased self-report instrument,

and thus is useful for most research purposes. The NCI

Dietary Assessment Primer gives extensive guidance as to

its use in research studies [117].

C Food Frequency

The food frequency approach asks respondents to report

their usual frequency of consumption of each food from a

list of foods for a specific period. Information is collected

on frequency, but little detail is collected on other charac-

teristics of the foods as eaten, such as the methods of

cooking, or the combinations of foods in meals. Many

FFQs also incorporate usual portion size questio ns or

specify portion sizes as part of each question. Overall

nutrient intake estimates are derived by summing, over all

foods, the products of the reported frequency of each food

by the amount of nutrient in a specified (or assumed)

serving of that food to produce an estimated daily intake

of nutrients, dietary const ituents, and food groups. In

most cases, the purpose of an FFQ is to obtain a crude

estimate of usual total daily intakes over a designated

time period.

There are many FFQ instruments, and many continue

to be adapted and developed for different populations and

purposes. Among those evaluated and commonly used are

the Block Questionnaires [118], the Harvard University

Food Frequency Questionnaires or Willett Questionnaires

[119], the Fred Hutchinson Cancer Research Center Food

Frequency Questionnaire [120,121], and the NCI’s Diet

History Questionnaire [122], which was designed with an

emphasis on cognitive ease for respondents [123,124].

FFQs have been developed for use with specific popula-

tions in the United States (e.g., African Americans,

Hispanics) and throughout the world. Because of the num-

ber of FFQs available, investigators planning to use an

FFQ need to carefully consider which best suits their

research needs. “Brief” FFQs that assess a limited number

of dietary exposures are discussed in the next section.

The appropriateness of the food list is crucial in the

food frequency method. The entire breadth of an indivi-

dual’s diet, which includes many different foods, brands,

and preparation practices, cannot be fully captured with a

finite food list. Obtaining accurat e reports for foods eaten

both as sing le items and in mixtures is particularly prob-

lematic. FFQs can ask the respondent either to report a

combined frequency for a particular food eaten both alone

and in mixtures or to report separate frequencies for

each food u se. (For example, one could ask about beans

eaten alone and in mixtures, or one could ask separate

questions about refried beans, bean soups, beans in burri-

tos, etc.) The first approach is cognitively complex for the

respondent, but the second approach may lead to double

Dietary Assessment Methodology Chapter | 1 9

counting (e.g., burritos with beans may be reported as

both beans and as a Mexican mixture). Often, FFQs will

include similar foods in a single question (e.g., beef, pork,

or lamb). However, such grouping can create a cogni-

tively complex question (e.g., for someone who often eats

beef and occasionally eats pork and lamb). Differences in

definitions of the food items asked may also be problem-

atic; for example, rice is judged to be a vegetable by

many nonacculturated Hispanics living in the United

States, a judgment not shared in other race/ethnic groups

[125]. Finally, when a group of foods is asked as a single

question, assumptions about the relative frequencies of

intake of the foods constituting the group are made in the

assignment of values in the nutrient database. These

assumptions are generally based on information from an

external study popul ation (such as from a national survey

sample) even though true eating patterns may differ con-

siderably across population subgroups and over time.

Each quantitative FFQ must be associated with a data-

base to allow estimation of nutrient intakes for an

assumed or reported portion size of each food queried

[126]. For example, the FFQ item of macaroni and cheese

encompasses a wide variety of different recipes with dif-

ferent nutrient com position, yet the FFQ database must

have a single nutrient composition profile. There are sev-

eral appro aches to constructing such a database. One

approach uses quantitative dietary intake information

from the target popul ation to define the typical nutrient

density of a particular food group category. For example,

for the food group macaroni and cheese, all reports of the

individual food codes reported in a population survey can

be collected, and a mean or median nutrient composition

(by po rtion size if necessary) can be estimated. Values

can also be calcul ated by sex and age. Dietary analysis

software, specific to each FFQ, is then used to compute

nutrient intakes for individual respondents. These analyses

are available commercially for the Block, Willett, and

Fred Hutchinson FFQs, and are publicly available for the

NCI Diet History Questionnaire.

In pursuit of improving the validity of the FFQ, investi-

gators have addressed a variety of frequency questionnaire

design issues, such as length, closed- versus open-ended

response categories, portion size, seasonality, and time

frame. Frequency instruments designed to assess total diet

generally list more than 100 individual line items, many

with additional portion size questions, requiring

3060 minutes to complete. In fact, some research sug-

gests that FFQs with even longer food lists (e.g., 200

items) may perform better than those with shorter food lists

(e.g., 100 items) [127]. This raises concern about the length

and its effect on response rates. Although respondent bur-

den is a factor in obtaining reasonable response rates for

studies in general, a few studies have shown that respon-

dent burden does not seem to be a decisive factor for FFQs

[124,128,129]. This tension between length and specificity

highlights the difficult issue of how to define a closed-

ended list of foods for a food frequency instrument. Using

food record intake information, a recently described mathe-

matical approach considers the length, coverage, and

explained variance to derive an optimized food list [130].

It is suggested that this tool be used in conjunction with

expert judgment from a research nutritionist.

Although the amounts consumed by individuals are

considered an important component in estimating dietary

intakes, it is controversial as to whether or not portion size

questions should be included on FFQs [127].Frequency

has been found to be a greater contributor than serving size

to the variance in intake of most foods [131,132],suggest-

ing that the additional respondent burden of reporting serv-

ing sizes is not worthwhile. Others cite small

improvements in the performance of FFQs that ask the

respondents to report a usual serving size for each food

[133,134]. Some incorporate portion size and frequency

into one question, asking how often a particular portion of

the food is consumed [135].Althoughsomeresearchhas

been conducted to determine the best ways to ask about

portion size on FFQs [123], the marginal benefit of such

information in a particular study may depend on the study

objective and population characteristics [136]. The ramifi-

cations of using self-reported versus standard portion sizes

were illustrated in a casecontrol study that found differ-

ent odds ratios depending on which metric was used [137].

Another design issue is the time frame about which

intake is queried. Most instruments inquire about usual

intakes during the past year, but others ask about the past

week or month [138], depending on specific research

situations. Even when intake during the past year is asked,

some studies have indicated that the season in which the

questionnaire is administered has an influence on report-

ing for the entire year [139 141].



Finally, analytical decisions are required in how food

frequenc

y data are processed. In research applications in

which there are no automated quality checks to ensure

that all questions are asked, decisions about how to han-

dle missing data are needed. In particular, in self-

administered situations, there are usually many initial

frequency questions that are not answered. One approach

is to assign null values because some research indicates

that respondents selectively omit answering questions

about foods they seldom or never eat [142,143]. Another

approach is the imputation of frequency values for

those not providing valid answers. Only a few studies

have addressed this issue [144,145], and it is currently

unclear whether imputation is an advance in FFQ analy-

ses. Recently, however, paper and pencil administration

has declined and has been replaced by electronic adminis-

tration which, because of programmable skip patterns,

greatly reduces missing data.

10 PART | A Assessment Methods for Research and Practice

Strengths of the FFQ approach are that it is inexpen-

sive to administ er and process and it asks about the

respondent’s usual intake of foods over an extended

period of time. Unlike other methods, the FFQ can be

used to circumvent recent changes in diet (e.g., changes

due to dise ase) by obtaining information about indivi-

duals’ diets as recalled about a prior time period.

Retrospective reports about diet nearly always use a

food frequency approach. Food frequency responses are

used to rank individuals according to their usual

consumption of nutrients, foods, or groups of foods.

Nearly all food frequenc y instruments are designed to be

self-administered, and most are either optical ly scanned

paper versions or administered electronically [118,120,

122,146148]. Be cause the costs of data collection and

processing and the respondent burden have traditionally

been much lower for FFQs than for multiple diet records

or recalls, FFQs have been a common way to estimate

usual dietary intake in large epidemiological studies.

The major limitation of the food frequency method is

that it contains a substantial amount of measurement error

[54,100103,149]. Many details of dietary intake are not

measured, and the quantification of intake is not as accu-

rate as with recalls or records. Inaccuracies result from an

incomplete listing of all possible foods and from errors in

frequency and usual serving size estimations. The estima-

tion tasks required for an FFQ are complex and difficult

[150]. As a result, the scale for nutrient intake estimates

from an FFQ may be shifted considerably, yielding inac-

curate estimates of the average intake for the group.

Research suggests that longer food frequency lists may

overestimate whereas shorter lists may underestimate

intake of fruits and vegetables [151], but it is unclear

whether or how this applies to nutrients and other food

groups.

Portion size of foods consumed is difficult for respon-

dents to evaluate and is thus problematic for all assess-

ment instruments (see Section V.D). However, the

inaccuracies involved in respondents attempting to esti-

mate usual portion size in FFQs may be even greater

because a respondent is asked to estimate an average for

foods that may have highly variable portion sizes across

eating occasions and time periods [152].

Because of the error inherent in the food frequency

approach, it is generally considered inappropriate to use

FFQ data to estimate quantitative parameters, such as the

mean and variance, of a population’s usual dietary intake

[153158]. Altho ugh some FFQs seem to produce esti-

mates of population average intakes that are reasonable

[153,159,160], different FFQs will perform in often

unpredictable ways in different populations, so the levels

of nutrient intakes estimated by FFQs should best be

regarded as only approximations [154]. FFQ data are usu-

ally energy adjusted and then used for ranking subjects

according to food or nutrient intake rather than for esti-

mating absolute levels of intake, and they are used widely

in casecontrol or cohort studies to assess the association

between dietary intake and disease risk [161163]. For

estimating relative risks, the degree of misclassification of

subjects is more important than is the quantitative scale

on which the ranking is made [164].

The definitive validity study for a food frequency

based estimate of long-term usual diet would require

nonintrusive observation of the respondent’s total diet

over a long time. Such studies are not possible in free-

living populations. One early feeding study, with three

defined 6-week feeding cycles (in which all intakes were

known), showed some significant differences in known

absolute nutrien t intakes compared to the Willett FFQ for

several fat components, mostly in the direction of under-

estimation by the FFQ [165]. Many studies have com-

pared food frequency estimates with those from multiple

food recalls or records over a period of time (see [166]

for a register of such studies). However, recalls and

records cannot be considered as accurate reference instru-

ments because they themselves have err or. Validation

studies of various FFQs using recovery biomarkers have

found that FFQs underestimate energy intake by 11%

35% [42,48,51,54,79,97,99103] and protein intake by

up to 30% [46,47,54,97,100,101,103,167171].Ina

pooled analysis of five larger U.S. biomarker studies,

FFQs underestimated energy by 28% and protein by 10%

[108]. A few studies show that correlations between a bio-

marker for protein density constructed from both urinary

nitrogen and doubly labeled water and self-reported pro-

tein density on an FFQ (kcal of protein as a percentage of

total kcal) are higher than correlations between urinary

nitrogen and FFQ-reported absolute protein intake

[101,103,149], indicating that energy adjustment may

alleviate some of the error inherent in food frequency

instruments. Various statistical methods employing mea-

surement error models and energy adjustment are used

not only to assess the validity of FFQs but also to adjust

estimates of relative risks for disease outcomes

[54,172182]. However, analyses indicate that correla-

tions between an FFQ and a reference instrument, such as

the 24-hour recall, may be overestimated because of cor-

related errors [54,101,149]. Furthermore, a few analyses

comparing relative risk estimation from FFQs to dietary

records [183,184] in prosp ective cohort studies indicate

that observed relationships are attenuated with FFQs,

thereby obscuring associations that might exist; however,

not all analyses have found this result [185]. Some epide-

miologists have suggested that the error in FFQs is a

serious enough problem that more accurate methods

(e.g., food records or 24-hour recalls) of assessing dietary

intake in large-scale prospective studies should be consid-

ered [186188].

Dietary Assessment Methodology Chapter | 1 11

Because of relatively large measurement error and

bias found with FFQs, the NCI Dietary Assessment

Primer suggests they be used sparingly, especially when

other instruments such as 24-hour dietary recalls could be

used. When FFQs are used as the main instrument, a con-

current calibration study on a subsample of the population

using more accurate instruments should be included in the

design [117]. See Section V.C for more discussion of cali-

bration. Because FFQ data might be combined with recall

or record data to improve estimates of intake and relative

risks [188190], the use of both instruments may be opti-

mal [117].

D Brief Dietary Assessment Instruments

Many brief dietary assessment instruments, also known as

“screeners,” have been developed. These instruments can

be useful in situations that do not require either assess-

ment of the total diet or quantitat ive accuracy in dietary

estimates. For example, a brief diet assessment of some

specific dietary components may be used to triage large

numbers of individuals into groups to allow mor e focused

attention on those at greatest need for intervention or edu-

cation. Measurement of dietary intake, even if imprecise,

can also serve to activate interest in the respondent, which

in turn can facilitate nutrition education. Brief instruments

may therefore have utility in clinical settings or in situa-

tions in which health promotion and health education is

the goal. In the intervention setting, brief instruments

focused on specific aspects of a dietary intervention have

been used to track changes in diet. However, because of

concern that responses to questions of intake that directly

evolve from intervention messages may be biased [191]

and that these instruments lack sensitivity to detect die-

tary change [192], this use is not recommended. Brief

instruments of specific dietary components such as fruits

and vegetables have been used for population surveillance

at the state or loca l level, for example, in the Centers for

Disease Control and Prevention’s (CDC) Behavioral Risk

Factor Surveillance System (BRFSS) [193,194] and the

California Health Interview Survey (CHIS) [195] (see

Section III.A). Brief instruments have also been used to

examine relationships between some specific aspects of

diet and other exposures, such as in the National Health

Interview Survey (NHIS) [196]. Finally, some suggest the

use of brief instruments to evaluate the effectiveness of

policy initiatives [195,197,198], although others question

the ability of short measures to adequately evaluate die-

tary changes [199].

Brief instruments can be simplified or targeted FFQs,

questionnaires that focus on specific eating behaviors other

than the frequency of intake of specific foods, or daily

checklists. Complete FFQs typically contain 100 or more

food items to capture the range of foods contributing to

the many different nutrients in the diet. If an investigator

is interested only in estimating the intake of a single

nutrient or food group, however, then far fewer foods

need to be assessed. Often, only 1530 foods might be

required to account for most of the intake of a particular

food component [200,201].

Numerous short questionnaires using a food frequency

approach have been developed and compared with multi-

ple d ays of dietary records, 24-hour recalls, complete

FFQs, and/or biological indicators of diet. The NCI has

developed a Register of Validated Short Dietary

Assessment Instruments [202], which contains descriptive

information about short instruments and their validation

studies and publications, as well as copies of the instru-

ments when available. To be included, publications are

required to be in English language peer-reviewed journals

and published since January 1998. Currently, the register

includes nearly 140 instruments assessing more than 30

dietary factors from 31 different countries. Instruments in

the register may be searched by dietary factors, question-

naire format, and number of questions. Descriptive infor-

mation about the validation study includes the reference

tool, the study population (age, sex, and race/ethnicity),

and the geographical location.

Much of the focus in brief instrument development

has been on fruits and vegetables and on fats. Some work

has addressed other food components that are found in

relatively few foods, such as calcium, added sugars, soy,

phytoestrogens, and heterocyclic amines [202].

1 Brief Instruments Assessing Fruit and

Vegetable Intake

Food frequency-type instruments to measure fruit and

vegetable consumption range from a single overall ques-

tion to 45 or more individual questions [203207].An

early 7-item tool developed by the NCI and private g ran-

tees for NCI’s 5 A Day for Better Health Program effort

was used widely in the United States [208210].This

tool was similar to one used in CDC’s BRFSS

[193,211,212]. Validation studies of the BRFSS and

5 A Day brief instruments to assess fruit and

vegetable intake suggested that without portion size

adjustments, they often underestimated actual intake

[203,208,212214]. Using cognitive interviewing find-

ings (see Section V.A), NCI revised the tool, including

adding portion size questions; some studies indicate

improved performance [215] and utility in surveillance

studies. However, its performance in community interven-

tions was mixed. In six of eigh t site/sex comparisons, fruit

and vegetable consumption was significantly overesti-

mated in relation to results from multiple 24-hour recalls

[216]. More important, the screener indicated change in

consumption in both men and women when none was

12 PART | A Assessment Methods for Research and Practice

seen with the 24-hour recalls [217]. The BRFSS fruit and

vegetable screener used in 201115 in odd years [193]

assessed intake of solid fruit and 100% fruit juice and

subgroups of vegetables that were particularly relevant to

2010 Dietary Guidelines for Americans [218]. Intake esti-

mates from the 2011 and 2013 assessments with the new

tool have been reported [194,219]. The instrument is

being redesigned, using questions developed at NCI.

2 Brief Instruments Assessing Fat Intake

The MEDFICTS (meats, eggs, dairy, fried foods, fat in

baked goods, convenience foods, fats added at the table,

and snacks) questionnaire, initially developed to assess

adherence to low total fat (,30% energy from fat) and

saturated fat diets [219], asks about frequency of intake

and portion size of 20 individual foods that are major

food sources of fat and saturated fat in the U.S. diet. Its

initial evaluation showed high correlations with dietary

records [219]. In addition to the cross-sectional studies,

the MEDFICTS underestimated percentage calories from

fat; it was effective in identifying very high-fat intakes

but was not effective in iden tifying moderately high-fat

diets [220] or correctly identifying low-fat diets [221].

The number of mixtures reported on an FFQ (e.g., pizza

and macaroni and cheese), which were not specifically

included in the MEDFICTS tool, was negatively related

to its predictive ability [221]. In a longitudinal setting,

positive changes in the MEDFICTS score have been cor-

related with improvements in serum lipids and waist cir-

cumference among cardiac rehabilitation patients [222] .

The instrument has been adapted for other populations

with varying success [221,223]. Other fat screeners have

been developed to preserve the between-person variabil-

ity of intake [224226]—that is, to focus on the fat

sources that most distinguish differences in fat intake

among individuals or groups. A 20-item screener was

developed and tested at the German site of European

Prospective Investigation into Cancer and Nutrition

correlated with 7-day dietary records (r 5 0.84) and a

complete FFQ (r 5 0.82) [224,225]. A 16-item percent-

age energy from fat screener had a correlation of 0.6

with 24-hour recalls in an older U.S. population [226].

However, its performance in overweight African-

American women was poorer (mean of 33.0% vs 35.5%

energy from fat for screener vs 24-hour recall) [227].

Its performance in an intervention study of adults varied

by site [228].

Often, dietary fat reduction interventions are designed

to target specific food preparation or consumption beha-

viors rather than frequency of consuming specific foods.

Such behaviors might include trimming the fat from red

meats, removing the skin from chicken, or choosing low-

fat dairy products. Many questionnaires have been

developed in various populations to measure these types

of dietary behaviors [229238], and many have been

found to correlate with fat intake estimated from other

more detailed dietary instruments [239,240] or with

blood lipids [233,241,242]. In addition, some studies

have found that changes in dietary behavior scores have

correlated with changes in blood lipids [234,241,243].

The instrument has been updated and modified for use in

different settings and populations [242,244,245]. A modi-

fication tested in African-American adolescent girls had a

relatively low correlation (r 5 0.31) with multiple 24-hour

recalls [246] . In another modification developed for

African-American women [247], a subset of 30 items

from the SisterTalk Food Habits Questionnaire correlated

with change in BMI (r 520.35) as strongly as did the

original 91 items (r 520.36) [248].

3 Brief Multifactor Instruments

Recognizing the utility of assessing a few dimensions of

diet simultaneously, several multifactor short instruments

have been developed and evaluated. For example, Prime-

Screen is composed of 18 FFQ items asking about con-

sumption of fruits and vegetables, whole and low-fat dairy

products, whole grains, fish and red meat, and sources of

saturated and trans-fatty acids. The average correlation

with estimates from a full FFQ over 18 food groups was

0.6 and over 13 nutrients was also 0.6 [249]. The NCI

developed a dietary screener administered in the

200910 NHANES that included 28 items addressing

consumption of fruits and vegetables, whole grains, added

sugars, dairy, fiber, calcium, red meats, and processed

meats [250]. This screener was also used in the 2010 and

2015 NHIS Cancer Control Supplement.

Some multicomponent behavioral questionnaires

have also been developed . For example, Schlundt et al.

[251] developed a 51-item Eating Behavior Patterns

Questionnaire targeted at assessing fat and fiber consump-

tion among African-American women. Newly incorpo-

rated in this questionnaire were questions to reflect

emotional eating and impulsive snacking.

Some instruments combine aspects of food frequency

and behavioral questions to assess multiple dietary patterns.

For example, the Rapid Eating and Activity Assessment

for Patients is composed of 27 items assessing consump-

tion of whole grains, calcium-rich foods, fruits and

vegetables, fats, sugary beverages and foods, sodium,

and alcohol. When compared to dietary records, correla-

tions were 0.49 with the original Healthy Eating Index

(HEI) [252], a measure of overall diet quality, and moder-

ately high (range of r 5 0.330.55) for HEI subscores of

fat, saturated fat, cholesterol, fruit, and meat. Correlations

for other HEI subscores for sodium, grains, vegetables, and

dairy were low (range of r 5 0.030.27) [253].

Dietary Assessment Methodology Chapter | 1 13

Because the cognitive processes for answering food

frequency-type questions can be complex, some attempts

have been made to reduce respondent burden by creating

brief instru ments with questions that require only

“yesno” answers. This approach has been applied as a

modification of the 24-hour recall [254]. These “targeted”

24-hour recall instruments aim to assess particular foods,

not the whole diet [71,255257]. They present a pre-

coded close-ended food list and ask whether the respon-

dent ate each food on the previous day; portion size

questions may also be asked. For example, a web-

administered checklist has been developed to measure the

Dietary Approaches to Stop Hypertension diet. It includes

a listing of foods grouped into 11 categories, and it

includes serving size information [258].

4 Limitations of Brief Instruments

The brevity of these instruments and their correspondence

with dietary intake as estimated by more extensive meth-

ods create a seductive option for investigators who would

like to measure dietary intake at a low cost. Although

brief instruments have many applications, they have sev-

eral limit ations. First, they do not capture information

about the entire diet. Most measures are not quantitatively

meaningful and, therefore, estimates of dietary intake for

the population usually cannot be made. Even when

measures aim to provide estimates of total intake, the esti-

mates are approximations and have large measurement

error. Finally, the specific dietary behaviors found to cor-

relate with dietary intake in a particular population may

not correlate similarly in another population or even in

the same population at another time period. For example,

a brief instrument developed to assess fast-food and

beverage consumption in a pri marily white, adolescent

population

[259] was not useful in an overweight Latina

adolescent population [260]. Investigators should care-

fully consider the needs of their study and their own

population’s dietary patterns before choosing an “off-the-

shelf” instrument designed to briefly measure either food

frequency or specific dietary behaviors. Because of these

limitations, the NCI Dietary Assessment Primer recom-

mends that short instruments be used sparingly and when

used, to be calibrated to a more accurate instrument such

as 24-hour dietary recalls [117]. See Section V.C for

more discussion on calibration.

E Diet History

The term diet history is used in many ways. In the most

general sense, a dietary history is any dietary assessment

that asks the respondent to report about past diet.

Originally, as coined by Burke, the term dietary history

referred to the collection of information not only about

the frequency of intake of various foods but also about

the typical makeup of meals [261,262]. Many now impre-

cisely use the term dietary history to refer to the food fre-

quency method of dietar y assessment. However, several

investigators have developed diet history instruments that

provide information about usual food int ake patterns

beyond simply food frequency data [263266].Someof

these instruments characterize foods in much more detail

than is allowed in food frequency lists (e.g., preparation

methods and foods eaten in combination), and some of

these instruments ask about foods consumed at every

meal [265,267]. The term diet history is therefore proba-

bly best reserved for dietary assessment methods that are

designed to ascertain a person’s usual food intake in

which many details about characteristics of foods as usu-

ally consumed are assessed in addition to the frequency

and amount of food intake.

The Burke d iet history included three elements: a

detailed interview about usual pattern of eating, a food

list asking for amount and frequency usually eaten, and a

3-day dietary record [261,262]. The detailed interview

(which sometimes include s a 24-hour recall) is the central

feature of the Burke dietary history, with the food fre-

quency checklist and the 3-day diet record used as cross-

checks of the history. The original Burke diet history,

which requires administration by an interviewer, has not

often been exactly reproduced because of the effort and

expertise involved in capturing and coding the informa-

tion. However, many variations of the Burke method have

been developed and used in a variety of settings

[263266,268272]. These variations attempt to ascer-

tain the usual eating patterns for an extended period of

time, including type, frequency, and amount of foods con-

sumed; many include a cross-check feature [273,274].

Some diet history instruments have been automated

and adapted for self-administration, sometimes with

audio, thus eliminating the need for an interviewer to ask

the questions [24,265,275]. Other diet histories have been

automated but still continue to be administered by an

interviewer [276,277]. Short-term recalls or records are

often used for validation or calibration rather than as a

part of the tool.

The major strength of the diet history method is its

assessment of meal patterns and details of food intake

rather than intakes for a short period of time (as in records

or recalls) or only frequency of food consumption. Details

of the means of preparation of foods can be helpful

in better characterizing nutrient intake (e.g., frying vs

baking), as well as exposure to other factors in foods

(e.g., charcoal broiling). When the information is col-

lected separately for each meal, analyses of the joint

effects of foods eaten together are possible (e.g., effects

on iron absorption of concurrent intake of tea or foods

containing vitamin C). Although a meal-based approach

14 PART | A Assessment Methods for Research and Practice

often requires more time from the respondent than does a

food-based approach, it may provide more cognitive sup-

port for the recall process. For example, the respondent

may be better able to report total bread consumption by

reporting bread as consumed at each meal.

A weakness of the approach is that respondents are

asked to make many judgments about both the usual

foods consumed and the amounts of those foods eaten.

These subjective tasks may be difficult for many respon-

dents. Burke cautioned that nutrient intakes estimated

from these data should be interpreted as relative rather

than absolute. All of thes e limitations are also shared with

the food frequency method. The meal-based approach is

not useful for individuals who have no particular eating

pattern and may be of limited use for individuals who

“graze” (i.e., eat throughout the day rather than at defined

mealtimes). The approach, when conduc ted by inter-

viewers, requires trained nutrition professionals and is

thus costly. Finally, the diet history as a method is not

well standardized, and thus methods differ from each

other and are difficult to reproduce, making comparisons

across studies difficult.

Relative to other assessment approaches, few valida-

tion studies of diet history questionnaires using biological

markers as a basis of comparison have been conducted.

The studies found that reported mean energy intakes using

the diet history approach in selected small samples of

adults were underestimated in the range of 223% com-

pared to energy expenditure as measured by doubly

labeled water [278281]. Generally, underreporting of

protein, compared to urinary nitrogen, was less than that

for energy and only sometimes significantly different

[279,281283]. These results have also been seen in chil-

dren [284], adolescents [285,286], and the elderly [264].

Because of small sample sizes in these studies, few were

able to examine characteristics related to underreporting,

and their results were mixed, with some finding more

underreporting with higher BMI [283,284] and others

finding no relationship [264,280,287]. Although the diet

history approach was extensively used as the main study

instrument in European cohorts initiated in the 1990s, the

approach is seldom used now in new cohort studies as

other approaches have evolved. The approach is some-

times used as a reference instrument [288 290].



F Blended Instruments/Combined

Instruments

Better understanding of various instrume nts’ strengths

and weaknesses has led to creative blending of instru-

ments with the goal of maximizing the strengths of each

instrument. For example, a record-assisted 24-hour recall

has been used in several studies with children [291,292].

The child keeps notes of what he or she has eaten and

then uses these notes as memory prompts in a later 24-

hour recall. A mobile phone food record app that includes

before and after meal photographs with text entry has

been tested in adolescents [293].

Analytical methods for using information from two

different instruments are available. For example,

Thompson et al. [294] combine d information from a

series of daily checklists (i.e., precoded record) with fre-

quency reports from an FFQ to form checklist-adjusted

estimates of intake. In an evaluation of this approach,

agreement with 24-hour recalls improved for energy and

protein but was unchanged for protein density [294].

A two-part statistical model developed by NCI uses infor-

mation from two or more 24-hour recalls, allowing for the

inclusion of daily frequency estimates derived from a

food propensity questionnaire (a frequency questionnaire

that does not ask about portion size), as well as other

potentially contributing characteristics (e.g., age and race/

ethnicity), as covariates [295]. Frequency information

contributes to the model by providing additional informa-

tion about an individual’s propensity to consume a food,

and is particularly useful for episodically consumed foods

and nutrients [296]. The recalls, however, provide infor-

mation about the nature and amount of the food con-

sumed. Such methods are used to better measure usual

intakes (see Section V.G). Several approaches consisting

of multiple dietary assessment instruments are available

to estimate associations between diet and disease.

A prominent use is to calibrate a frequency questionnaire

completed by all study subjects wi th information from a

more accurate instrument, such as a 24-hour recall, com-

pleted by a subset. See Section V.C for more discussi on

of calibration. Carroll et al. [188] explored the number of

days of 24-hour recall required to estimate associations

between diet and disease in a cohort study and whether an

FFQ, in addition, is beneficial. They concluded that for

most nutrients and foods, 46 nonconsecutive days of

24-hour recall and an FFQ are optimal. The combination

of FFQ and multiple 24-hour recalls was superior in

estimating some nutrients and foods, especially for episodi-

cally consumed foods. Finally, the addition of biomarker

information to self-reported dietary information has been

shown to increase accuracy and statistical power to estimate

associations between diet and disease [297,298].

Table 1.1 summarizes the important characteristics of

the main self-report dietary assessment methods.

III DIETARY ASSESSMENT IN DIFFERENT

STUDY DESIGNS

The choice of the most appropriate dietary assessment

method for a specific research question requires careful

Dietary Assessment Methodology Chapter | 1 15

consideration. The primary research question must be

clearly formed, and questions of secondary interest should

be recognized as such. Projects can fail to achieve their

primary goal becau se of too much attention to secondary

goals. The choice of the most appropriate dietary assess-

ment tool depends on many factors. Questions that must

be answered in evaluating which dietary assessment tool

is most appropriate for a particular research need include

the following [162]: (1) Is information needed about

foods, nutrients, other food components, or specific die-

tary behaviors? (2) Is the focus of the research question

on describing intakes using estimates of average intake,

and does it also require distributional information? (3) Is

the focus of the research question on describing relation-

ships between diet and health outcomes? (4) What level

of accuracy and precision is needed? (5) What time period

is of interest? (6) What are the research constraints in

terms of money, interview time, staff, and respondent

characteristics?

The NCI Dietary Assessment Primer conceptualizes

research questions into four categories: to describe a

population’s dietary intake; to examine associations

between diet as an independent variable and another vari-

able; to examine associations between an independent

TABLE 1.1 Comparison of Self-Report Dietary Assessment Methods by Important Characteristics

Dietary 24-Hour FFQ Diet Screener

Record Recall History

Type of Information Attainable

Detailed information about foods consumed X X

General information about food groups consumed X X

Meal-specific details X X

Scope of Information Sought

Total diet X X X X

Specific components X

Time Frame Asked

Short term (e.g., yesterday, today) X X

Long term (e.g., last month, last year) X X X

Adaptable for Diet in Distant Past

Yes

XX X

No X X

Cognitive Requirements

Measurement or estimated recording of foods and drinks as X

they are consumed

Memory of recent consumption X X

Ability to make judgments of long-term diet X X X

Potential for Reactivity

High X

Low XXX

Time Required to Complete

,15 minutes

.20 minutes X X X X

Suitable for Cross-Cultural Comparisons Without Instrument Adaptation

Yes X X

No XX X

16 PART | A Assessment Methods for Research and Practice

variable and diet as a dependent variable; and to evaluate

the effect of an intervention on dietary intake. The role of

measurement error in tool selection for each research

objective is discussed in depth [117].

A Cross-Sectional Surveys

One of the most common types of population studies is

the cross-sectional survey, a set of measurements of a

population at a particular point in time. Such data can be

collected solely to describe a particular population’s

intake. Alternatively, data can be used for surveillance at

the national, state, and local levels as the basis for asses-

sing risk of deficiency, toxicity, and overconsumption; to

evaluate adherence to dietary guidelines and p ublic health

programs; and to develop food and nutrition policy.

Cross-sectional data also may be used for examining asso-

ciations betwee n current diet and other factors including

health. However, caution must be applied in examining

many chronic diseases believed to be associated with past

diet because the currently measured diet is not necessarily

related to past diet. If the study objective requires quanti-

tative estimates of intake, the 24-hour recall and possibly

the food record instruments are recommended [117]. Less

detailed instruments, such as FFQs or behavioral indica-

tors, may be appropriate when qualitative estimates on

limited exposures are sufficient—for example, frequency

of consuming sugar-sweetened beverages and frequency

of eating from fast-food restaurants.

1 Surveillance/Monitoring

When measurements are collected on a sample at two or

more times, the data can be used for purposes of monitor-

ing dietary trends. To assess trends in intakes over time, it

would be ideal for the dietary surveillance data collection

methods, sampling procedures, and food composition

databases to be similar from survey to survey. As a practi-

cal matter, however, this is difficult, and the benefits of

trend analysis may not outweigh the benefits of improving

the methods over time. The dietary assessment method

used consistently throughout the years in U.S. national die-

tary surveillance is the interviewer-administered 24-hour

recall. However, recall methodology has improved over

time based on cognitive research, the addition of multiple

interviewing passes, standardization of probes, automation

of the interview, and automation of the coding. The avail-

ability of automated self-administered 24-hour recall instru-

ments may lead to further changes in methodology.

Another issue that affects the assessment of trends over

time is changes in the nutrient or food grouping databases

and specification of default foods. Changes in the food sup-

ply are reflected in additions or subtractions to food com-

position databases, whereas changes in consumption trends

may lead to subsequent reassignment of default codes for

foods not fully specified in 24-hour recalls or records (e.g.,

when type of milk is not specified, the default code is now

2% milk as opposed to whole milk in the past). Food com-

position databases, too, are modified over time because of

true changes in food composition, improved analytic meth-

ods for particular nutrients, or inclusion of information for

new dietary components. Since 1999, the major cross-

sectional surveillance survey in the United States has been

the NHANES [299]. This survey is conducted by the

National Center for Health Statistics. The dietary compo-

nent of the survey, called “What We Eat in America” [75],

consists of 24-hour recalls collected using the USDA’s

AMPM (see Section II.B). The USDA also processes and

analyzes the data. The 24-hour recalls in NHANES query

the intake of dietary supplements as well as foods and

beverages. Since 200304, NHANES has conducted two

24-hour dietary recalls on each respondent, allowing for

estimation not only of average usual intake but also of the

distributions of usual intake of the dietary components (see

Section V.G).

NHANES provides high-quality dietary intake data at

the national level, but these data are of limited use for

state and local researchers planning and evaluating their

programs and policies [300]. Collection of state and local

data is often constrained by lack of resources or interview

time, leading to the frequent use of less expensive brief

instruments. For example, the CDC has used telephone-

administered brief instruments to periodically assess fruit

and vegetable intake within the BRFSS [193]. The

California Department of Public Health, in its California

Dietary Practices Survey, has assessed dietary practices

among adults biennially since 1989 [301]. The CHIS used

telephone-administered brief instruments to assess fruit

and vegetable intake in 2001, 2005, and 2009 [195].

B Case Control (Retrospective) Studies

A casecontrol study design classifies individuals with

regard to current disease status (as cases or controls) and

relates this to past (retrospective) exposures. In etiologic

research, information about diet before onset of disease is

needed. Dietary assessment methods that focus on current

behavior, such as the 24-hour reca ll, are obviously not

useful in retrospective studies of long past diet. The food

frequency and diet history methods are the only viable

choices for case control (retrospective) studies.

In any food frequency or diet history interview, the

respondent is not asked to recall specific memories of

each eating occasion but, rather, to respond on the basis

of general perceptions of how frequently he or she ate a

food. In casecontrol studies, the relevant period is often

the year before diagnosis of disease or o nset of symptoms

or at particular life stages, such as adolescence and

Dietary Assessment Methodology Chapter | 1 17

childhood. Thus, in assessing past diet, an additional

requirement is to orient the respondent to the appropriate

time period.

The validity of recalled diet from the distant past is

difficult to assess because definitive recovery biomarker

information (e.g., doubly labeled water, urinary nitrogen)

is not available for large samples from long ago. Instead,

relative validity and long-term reproducibility of various

FFQs have been assessed in various populations by asking

participants from past dietary studies to recall their diet

from that earlier time [302304]. These studies have

found that correlations between past and current reports

about the past vary by nutrient and by food group

[135,305], with higher correspondence for very frequently

consumed and rarely consumed foods compared to that

for foods consumed moderately often [305,306]. Evidence

suggests that correspondence between past and recalled

past decreases with the length of time between reports

[302,307]. In particular, retros pective reports of diet in

adolescence after long recall periods (i.e., .30 years)

have shown little correspondence with the original reports

[308310]. Maternal reports about diets of their children

in early childhood or adolescence and siblings reports of

each other’s diets in adolescence have also shown low

correspondence with the original reports [310,311].

Correspondence of retrospective diet reports with the

diet as measured in the original study usually has been

greater than the correspondence of current diet with past

diet. This obser vation implies that if diet from years in the

past is of interest, it is usually preferable to ask respon-

dents to recall it than to consider current diet as a proxy

for past diet. Nonetheless, the current diets of respondents

may affect their retrospective reports about past diets. In

particular, retrospectiv e diet reports from seriously ill indi-

viduals may be biased by recent dietary change s

[302,312]. Some studies of groups in whom diet was pre-

viously measured indicat e no consistent differences in the

accuracy of retrospective reporting between those who

recently became ill and others [313,314]. However, in two

of three studies that have compared baseline prospective

dietary information to later retrospective recall of the ear-

lier diet, the correspondence of the information differed

between those who later became cases and control s, intro-

ducing attenuation into risk estimates [310,315,316].

C Cohort (Prospective) Studies

In a cohort study design, exposures of interest are

assessed at baseline and possibly at later times in a group

(cohort) of people and disease outcomes occurring over

time (prospectively) are then related to the baseline expo-

sure levels. For many chronic diseases, large numbers of

individuals need to be followed for years before enough

new cases with that disease accrue to have adequate

power for statistical analyses. A broad assessment of diet

is usually desirable in prospective studies because many

dietary exposures and many disease end points will ulti-

mately be inve stigated, and areas of interest may not even

be recognized at the beginning of a cohort study.

In order to relate diet at baseline prior to disease to the

eventual occurrence of disease, a measure of the usual

intake of foods (see Section V.G) by study subjects is

needed. Multiple dietary recalls, multiple records, diet histo-

ries, and food frequency methods have all been used effec-

tively in prospective studies. Cost and logistic issues have

favored food frequency methods because many prospective

studies require thousands of respondents. However, because

of concern about significant measurement error and attenua-

tion attributed to the FFQ [183,186,187,317320],other

approaches are being considered. One approach is the use of

multiple automated self-administered 24-hour recall instru-

ments (see Section II.B). Another approach is collecting

multiple days of dietary records at baseline, with later cod-

ing and analysis of records for those respondents selected

for analysis, using a nested casecontrol design [321,322].

The incorporation of emerging technological advances, such

as mobile phones, in obtaining dietary records increases the

feasibility of such approaches in prospective studies.

If using an FFQ as the main instrument in the cohort, it

is desirable to include multiple recalls or records in repre-

sentative subsamples of the population (preferably before

beginning the study) to construct or modify the food fre-

quency instrument and to calibrate it (see Section V.C).

Information on the foods consumed could be used to

ensure that the FFQ includes the major food sources of key

nutrients, with appropriate portion size categories. Because

the diets of individuals change over time, it is desirable to

measure diet throughout the follow-up period rather than

just at baseline. If diet is measured repeatedly over years,

repeated calibration is also desirable. Information from cal-

ibration studies can be used for three purposes: to assist in

study design, such as the sample size needed [164];tocali-

brate values from the food frequency tool to values from

the recalls/records [180]; and to determine the degree of

attenuation/measurement error in the estimates of associa-

tion observed in the study (e.g., between diet and disease)

[175,178,180,182,323327] (see Section V.C). Some

research indicates that an optimal approach to dietary

assessment in prospective studies may be the use of both

multiple recalls or records and FFQs [188].TheFFQcan

be particularly useful in contributing information about epi-

sodically consumed foods.

D Intervention Studies

Dietary intervention study designs usually consist of mea-

sures of interest for at least two time periods (typically,

before and after intervention), and for at least two groups

18 PART | A Assessment Methods for Research and Practice

of participants, those receiving the intervention and those

not (i.e., controls). Intervention studies range from rela-

tively small, highly controlled, clinical studies of targeted

participants to large trials of population groups.

The need for careful planning and formative research

in designing useful community dietar y intervention trials

has been described [328]. A critical element is the exis-

tence of evidence that a particular intervention would cre-

ate a measurable change in a particular group and setting.

Intentional behavior change is a complex and sequential

phenomenon, as has been shown for tobacco cessation

[329], and this is also true for dietary change [330].

Interventions that aim to change the existing diet may

use dietary assessment for two purposes: (1) initial

screening for inclusion (or exclusion) into the study and

(2) baseline measurement agai nst which dietary changes

resulting from the intervention are assessed. Not all inter-

vention trials require initial screening. For those that do,

screening can be performed using very detailed instru-

ments or less burdensome instruments. For example, food

frequency instruments were used in the Women’s Health

Trial [331] and in the Women’s Health Initiative Dietary

Modification Trial [332] to identify groups with high fat

intake and thus determine eligibility.

Measurement of the effects of a dietary intervention

requires a valid measure of change from baseline to the

conclusion of the intervention period, and often, postinter-

vention to assess the durability of any change. Dietary

interventions that are expected to change an objective

marker, for example, weight or blood lipids, are relatively

straightforward to measure and analyze. However, if eval-

uation of the intervention requires measurement of change

in self-reported diets, the task is complex, due to many

possible biases.

Although not intending to be deceptive, some respon-

dents may tend to report what they think investigators

want to hear, leading to social desirability [333] and social

approval [334] biases. Because of their greater subjectiv-

ity, behavioral questions, short instruments, and the food

frequency method may be more susceptible to social desir-

ability biases than the 24-hour recall method [73,191].On

the other hand, repeated measurement may lead to greater

awareness of diet and enhanced reporting skills and thus

may enhance accuracy [335]. Dietary records and sched-

uled 24-hour recalls are vulnerable to reactivity bias. If

assessment is by 24-hou r recalls, unannounced administra-

tion would avoid reactivity but possibly at the expense of

participation as successful contact may be more difficult

(and expensive). Most importantly, the potential for differ-

ential misreporting of diet between study groups (whether

the misreporting in each group is similar or different) can

affect the integrity of the results. Repeated measures of

diet among study subjects can reflect reporting bias in the

direction of the change being promoted [336].

Some work has been done to evaluate the use of self-

report dietary assessment methods to measure dietary

changes [245,336]. Researchers have found that dietary

records and scheduled 24-hour recalls are associated with

changed eating behavior during the record days and less

correspondence with biological measures [337] and

expected weight change [338], and increased underreport-

ing [339]. One study using dietary screeners and a

reference measure of multiple nonconsecutive unan-

nounced 24-hour recalls found that change in fruit and

vegetable intake in the intervention group was overesti-

mated relative to the control group [217]. However, in the

same study, a fat screener and the 24-hour recalls were

consistent in finding no change in percentage energy

fromfatinthetwogroups[340]. Because of resource

constraints and respondent burden, large intervention

studies have often relied on less precise measures of

diet, including FFQs and brief instruments. However,

resource constraints may be less relevant with the avail-

ability of automated self-administered 24-hour dietary

recall instruments and less burdensome dietary records.

Because self-reports of diet are subject to differential

response bias in the context of an intervention study

[335,336], an independent objective assessment of dietary

change should be considered. For example, food availabil-

ity and/or sales in worksite cafeterias, school cafeterias, or

vending machines could be monitored. One such method

useful in community-wide interventions is monitoring food

sales [341]. Often, cooperation can be obtained from

food retailers [342]. However, because the number of food

items may be large, it may be possible to monitor only

a small number, and the large effects on sales of day-to-

day pricing fluctuations should be carefully considered.

Another method to consider is measuring changes in

biomarkers of diet, such as serum carotenoids [335,343]

or serum cholesterol [344]. Consistency of changes in self-

reported diet and appropriate biomarkers provides further

evidence for real changes in the diet. Finally, social desir-

ability biases could be measured and the resulting scales

incorporated into intervention analyses. See Chapter 10,

Nutritional Intervention: Lessons from Clinical Trials,

and Chapter 11, Biomarkers and Their Use in Nutrition

Intervention, for more in-depth discussions of the evalua-

tion of diet in nutrition interventions and use of biomarkers

in intervention studies, respectively.

IV DIETARY ASSESSMENT IN SPECIAL

POPULATIONS

A Respondents Unable to Self-Report

In many situations, respondents are unavailable or unable

to report about their diets. Dietary assessment in young

children relies on surrogate reports. In casecontrol

Dietary Assessment Methodology Chapter | 1 19

studies, surrogate reports may be obtained for cases who

have died or who are too ill to interview. Although the

accuracy of surrogate reports has not been examined

using the recovery biomarkers of doubly labeled water or

urinary nitrogen, comparability of reports by surrogates

and subjects has been studied with the goal that surrogate

information might be used interchangeably with informa-

tion provided by subjects [345]. Common sense indicates

that individuals who know most about a subject’s lifestyle

would make the best surrogate reporters [346]. Adult sib-

lings provide the best information about a subject’s early

life, and spouses or children provide the best information

about a subject’s adult life. When food frequenc y instru-

ments are used, the level of agreement between subject

and surrogate reports of diet varies with the food and pos-

sibly with other variables, such as number of shared

meals, interview situation, case status, and sex of the sur-

rogate reporter. Mean frequencies of use computed for

individual foods and food groups between surrogate

reporters and subject reporters tend to be similar

[347349], but agreement is much lower when detailed

categories of frequency are compared. Several studies

have shown that agreement is better for alcoholic bev-

erages, coffee, and tea than for foods.

When subjects themselves report intakes in the

extremes of a distribution, their surrogates seldom report

intakes in the opposite extreme, although the surrogates

tend to report intakes in the middle of the distribution

[350]. This may limit the usefulness of surrogate infor-

mation for analyses that rely on accurate ranking.

Furthermore, the quality of surrogate reports between

spouses of deceased subjects and spouses of surviving

subjects may dif fer substantiall y [351]. Thus far, however,

little evidence suggests that dietary intakes are systemati-

cally overreported or underreported depending on the case

status of the subject [352 354]. Nonetheless, use of

surrogate respondents should be minimized for obtaining

dietary information in analytical studies. When used, anal-

yses excluding the surrogate reports should be done to

examine the sensitivity of the reported associations to

possible errors or biases in the surrogate reports. If plan-

ning a study using surrogate reports, sample size should

be inflated to account for higher incidence of missing

data, inability to recruit surrogates for some number of

cases, and reduced precision of dietary estimates.

B Minority Populations

The widespread use of many “ethnic” foods in the Unite d

States throughout the popul ation and the increasing diver-

sity of the population have broadened the food composi-

tion databases and food lists used for the general

population. Nonetheless, special modifications may be

needed in dietary assessment methods when the study

population is com posed of individuals whose cuisine or

cooking practices are not adequately represented in the

instrument and/or database [355]. If the method requires

an interview, interviewers of the same ethn ic or cultural

background are preferable so that dietary information can

be more effectively communicated. If dietar y information

is to be quantified into nutrient estimates, examination of

the nutrient composition database is necessary to ascertain

whether ethnic foods are included and whether those foods

and their various preparation methods represent those con-

sumed by the target population [356]. It is also necessary

to examine the recipes and assumptions underlying the

nutrient composition of certain ethnic foods. Some very

different foods may be called the same name, or iden tical

foods may be called by different names [357,358]. For

these reasons, it may be necessary to obtain detailed recipe

information for all ethnic mix tures reported.

To examine the suitability of the initial database, pre-

liminary information about typical diets should be

collected from individuals in the minority groups. This

information could come from recalls or records with

accompanying interviews or from focus group interviews.

These interviews should focus on the foods eaten and the

ways in which foods are prepared in that culture. Recipes

and alternative names of the same food should be col-

lected, and field interviewers should be familiarized with

the results of these focus groups. Recipes and food names

that are relatively uniform should be included in the nutri-

ent composition database. Even with these modifications,

it may be preferable for the field interviewers to collect

detailed descriptions of ethnic foods reported rather than

to directly code these foods using preselected lists most

common in computer-assisted methods. This would pre-

vent the detail of food choice and preparation from being

lost by a priori codi ng.

USDA continues to incorporate new foods into the

National Nutrient Database for Standard Reference (SR)

as does the University of Minnesota Nutrient Database

System (see Section V.F). If a newly reported food is not

available in the food composition database being used, a

default code that is thought to closely mirror the nutrient

composition of the new food can be used.

Use of FFQs developed for the majority population

may be suboptimal for many individuals with different

eating patterns. Many individuals consume both foods

common in the mainstream culture and foods that are spe-

cific to their own culture. Modification of the existing

food list can be accomplished through expert judgment,

qualitative interv iews with the target population [359],

and/or examination of the frequency of reported foods in

the population from a set of dietary records or recalls. For

example, FFQs for Alaska Natives [360], Hispanics

[361,362], and African Americans in the southern United

States [363] have been developed using these approaches.

20 PART | A Assessment Methods for Research and Practice

In addition to the food list, however, there are other

important issues to consider when adapting existing FFQs

for use in other populations. The relat ive intake of differ-

ent foods within a food group line item may differ , thus

requiring a change in the nutrient database associated

with each line item. For example, Latino populations may

consume more tropical fruit nectars and less apple and

grape juice than the general U.S. population and therefore

would require a different nutrient composition standard

for juices. In addition, the portion sizes generally used

may differ [364]. For example, rice may be consumed in

larger quantities in Latino and Asian populations; the

amount attributed to a large portion for the general popu-

lation may be substantially lower than the amount typi-

cally consumed by Latino and Asian populations.

Adaptation of an existing FFQ considering all of these

factors has been done for an elderly Puerto Rican popula-

tion [365], for white and African-American adults in the

Lower Mississippi Delta [366], and for the Hawai i Los

Angeles Multiethnic Cohort Study [367]. The Southern

Community Cohort Study incorpor ated both race/ethnicity

and geographic region into its FFQ database [368].

With some populations, it may be preferable to adminis-

ter an FFQ using an interviewer rather than self-

administration because literacy and language barriers may

limit participation in the study as well as quality of

response. In addition, portion size models, which inter-

viewers can bring to a home interview, may be preferable

to portion size pictures available in a self-administered

instrument [360].

The NCI Dietary Calibration/Validation Studies

in specific race/ethnicity groups. Questionnaires aimed at

allowing comparison of intakes across multiple cultures

have been developed. Although some studies have found no

appreciable performance differences across various race/

ethnicity groups [369], most have found differences

[365,367,370374]. Understanding these differences is

crucial to the appropriate interpretation of study results.

C Children

Assessing the diets of children is considered to be even

more challenging than assessing the diets of adults.

Children tend to have diets that are highly variable from

day to day, and their food habits can change rapidly over

time. Younger children are less able to recall, estimate,

and cooperate in usual dietary assessment procedures than

older children [375], so much information by necessity

has to be obtained by surrogate reporters. Although they

are more able to report, adolescents may be less moti-

vated to give accurate reports. Baranowski and Domel

[376] have posited a cognitive model of how children

report dietary information.

Dietary assessment in children and adolescents

has been discussed and reviewed [375,377382].

The 24-hour recall, dietary records (including precoded

checklists [8]), dietary histories, FFQs, brief instruments

[383385], and blended instruments such as a dietary

record-assisted 24-hour recall [291] have all been used to

assess children’s intakes. The use of direct observation of

children’s diets has also been used extensivel y, most often

as a reference method to compare with self-reported

instruments [386,387]. As predicted from Barano wski and

Domel’s model, it has been found that children’s esti-

mates of portion size have large error [388], and they are

less able than adults to estimate portion sizes [389] (see

Section V.D). Overall, the consensus seems to be that the

characteristics of different age groups call for the use of

different assessment approaches [380].

For pres chool-aged children, information is obtained

from surrogates, usually the primary caretaker(s), typi-

cally a parent or external caregiver. If information is

obtained only from one surrogate reporter, the reports are

likely to be less complete. Even for periods when the

caregiver and child are together, foods tend to be underes-

timated [390]. A “consensus ” recall method, in which the

child and parents report as a group on a 24-hour recall,

has been shown to give more accurate information than a

recall from either parent or child alone [391]. Sobo and

Rock [392] describe such interviews and suggest tips for

interviewers to maximize data accuracy. Food records

have been used in many European population studies

[393]. This approach may be acceptable, but is likely to

be inappropriate for some populations. The U.S.

NHANES administers 24-hour recalls to proxy reporters

for children under 6 [394].

For older children, extensive research has been con-

ducted on the self-reported 24-hour recall [395]. Baxter

et al. [396] found that among fourth graders, accuracy of

the 24-hour recall improves as the time between reporting

and eating decreases, and meal-specific intrusions (i.e.,

reports of foods not consumed) are fewer in an open for-

mat interview than in a time-forward format interview

(i.e., beginning at the earliest meal in the time period and

working forward to the next meal). These intrusions are

often associated with additional intrusions at the same

meal [396]. Becaus e accuracy of recall is greater when

the time between eating and reporting is shorter, there

will be differential error by meal; meals further away

(e.g., at the beginning of the 24-hour recall period) will

have substantially more error [397,398].

To make 24-hour recalls more feasible, self-

administered automated 24-hour recall tools have been

developed and tested for children [88]. An interviewer-

administered 24-hour recall and a self-administered 24-

hour recall using the Food Intake Recording Software

System (FIRSSt) were compared to unobtrusive

Dietary Assessment Methodology Chapter | 1 21

observations in fourth graders. Compared to observed

intake, the interviewer-administered 24-hour recall was

associated with a 59% match, 17% intrusion, and 24%

omission rates, whereas the automated recall was associ-

ated with a 46% match, 24% intrusion, and 30% omission

rates [88]. The most recent version, FIRSSt4, is an adap-

tation of the ASA24, simplified for children [399,400]

and is available as ASA24-Kids [76]. Particular chal-

lenges of self-administered 24-hour recalls in this age

group include instigating and maintaining motivation to

complete the task, and, because of difficulty in estimating

portion size incorporating training for portion size estima-

tion within the application [401]. Other web-based

24-hour recall systems have been developed especially for

children and adolescents, for example, SCRAN24 in

Great Britain [402], Web DASC in Denmark [403], and

CANAA-W in Belgium [404]. The Synchronized

Nutrition and Activity Program (SNAP), a partial recall,

directs children to report the previous day’s food intake

by ticking the number of times they consumed each of

40 foods and 9 drinks [405]. Another approach that has

been taken with school-age children is a blended instru-

ment, the record-assisted 24-hour recall, in which the

children record only the names of foods and beverages

consumed throughout a 24-hour period. This information

serves as a cue for the later 24-hour recall interview.

The European Food Consumption V alidation Project, a

consortium of 13 institutes from 11 European countries,

provisionally recommended a similar approach—a food

recording booklet for foods eaten away from home—for

school children 714 years old. Studies examining the

validity of this approach have had mixed results

[291,292,406]. For children ages 611, the U.S.

NHANES administers 24-hour recalls to the child

assisted by an adult household member. Children 12

years old and older report for themselves and may have

a proxy reporter if necessary [394].

Food frequency appro aches are even more challenging

for children and adol escents as they are for adults.

Children’s diets change more quickly over time, and may

also be more variable from day to day than adults. In

addition, children are less able to conceptualize intake

over a long period of time. The instrument itself requires

adaptation of the food list, question wording and format,

and portion size categories, and consequently the database

for converting responses to nutrient intakes. Food

frequency instruments, some web administered, have been

developed and tested for use in child and adolescent

populations [146,407410]. A web -based food behavioral

questionnaire underestimated the intake of middle-school

children compared to a multiple-pass 24-hour recall

[411]. Generally, correlations between food frequency

type instruments and more precise reference instruments

have been lower in child and adolescent populations than

in adult populations. For these reasons, the food fre-

quency approach is not recommended for children and

adolescents.

New technology has been incorporated into some die-

tary assessment approaches. Williamson et al. [412]

developed and tes ted an observational method using digi-

tal photography in school cafeterias. The method consists

of standardized photography of the food selected before

the meal and the plate waste following the meal. Using

reference portions of measured quantities of the foods,

expert judgment is used to estimate the amount of each

food consumed [413]. Technology-based methods, such

as disposable cameras, mobile phones with cameras

[414], and smart phones, are being developed for collect-

ing records and may be particularly useful among adoles-

cents, who prefer these methods to traditional methods

[415]. Examples of these new methods are the Remote

Food Photography Method [416] and Technology

Assisted Dietary Ass essment [417]. Generally, these

methods require more development, and eventual large-

scale evaluation.

In addition to performance considerations, the choice

of which dietary assessment approach instrument to use in

a given study may depend on the study objectives and

study design factors, all of which will influence the

appropriateness and feasibility of different approaches

[418].

D Elderly

Measuring diets among the elderly can, but does not nec-

essarily, present special challenges [419422]. Both

recall and food frequency techniques are inappropriate if

memory or cognitive functioning is impaired. Similarly,

self-administered tools may be inappropriate if physical

disabilities such as poor vision are present. Interviewer

administration is difficult when hearing problems are

present [421]. Direc t observation in institutional care

facilities [419] or shelf inventories for elders who live at

home can be useful. Even when cognitive integrity is not

impaired, several factors can affect the assessment of diet

among the elderly. Because of the frequency of chronic

illness in this age group, it is more probable that special

diets (e.g., low sodium, low fat) would have been recom-

mended. Such recommendations could not only affect

actual dietary intake but also bias reporting because indi-

viduals may report what they should eat rat her than what

they do eat. Alternatively, respon dents on special diets

may be more aware of their diets and may more accu-

rately report them. When dentition is poor, the interviewer

should probe regarding foods that are prepared or con-

sumed in different ways. Relative to other age groups, the

elderly are more apt to take multiple types of nutritional

supplements [423425], which present special problems

22 PART | A Assessment Methods for Research and Practice

in dietary assessment (see Chapter 2: Assessment of

Dietary Supplement Use). Because of the concern of mal-

nutrition among the elderly, specific instruments to detect

risk of malnutrition [426], such as the Mini Nutritional

Assessment [427] and the Mini Nutritional Assessment

Short Form [428,429], the Geriatric Nutritiona l Risk

Index [430432], the Subjective Global Assessment

[426,428], and the Scored Patient-Generated Subjective

Global Assessment [433] have been developed. While all

of these tools focus on the elderly, they vary by setting,

purpose, and administration mode.

Some researchers have suggested that the short-term

memory required for the 24-hour recall may be more diffi-

cult for the elderly, who are more adept at long-term mem-

ory [419]. However, interviewers conducting an FFQ among

elderly respondents noted difficulty in maintaining interest

and concentration, whereas these issues were not found dur-

ing the more engaging 24-hour recall interview [420].

Validation studies using doubly labeled water and/or

urinary biomarkers among the elderly are limited

[42,434436]. Generally, energy underreporting has been

found to be positively related to elevated BMI and lower

education, similar to younger populations. However, in

the NIH-funded Health, Aging, and Body Composition

Study cohort, Shahar et al. [436] found that a substantial

portion of elderly reporters were undereaters, losing more

than 2% of their weight over a year. The distinction

between undereating and underreporting is particularly

relevant in the elderly.

Adaptations of standard dietary assessment methods

have been suggested and evaluated, including using mem-

ory strategies, notifying the respondent prior to the dietary

interview [437], combining methods [438], conducting

multiple interviews for long protocols [419], and adaptin g

existing instruments [439]. Specific adaptations that have

been made in elderly populations include use of house-

hold measures rather than pictures to portray portion size

for sight-impaired respondents [420] and tailoring the

food list and portion sizes to be characteristic of the

elderly rather than all adults in FFQs and their related

databases [440,441].

Some have suggested including measures of cognitive

function within a study to aid interpretation of results, but

one such study found no relationship between cognitive

functioning score and the validity of an FFQ [442].In

another study those showing cognitive dysfunction were

excluded, but this creates selection bias [443]. Another

approach is to solicit surrogate information for those con-

sidered cognitively unfit [444]. Mobile and web-based

methods may prove useful, but currently the acceptance,

feasibility, and validity of such methods in the elderly are

unknown [422].

The variability in functional status among the elderly

suggests the need for a flexible approach in assessing dietary

intake. Mixed mode design in survey research [445] has cer-

tain advantages with regard to enhancing coverage and

decreasing nonresponse, but it may cause other biases [446].

Table 1.2 summarizes special considerations for spe-

cific populations.

V SELECTED ISSUES IN DIETARY

ASSESSMENT METHODS

A Cognitive Testing Research Related to

Dietary Assessment

Nearly all studies using dietary information about subjects

rely on the subjects’ own reports of their diets. Because

such reports are based on complex cognitive processes, it

is important to under stand and take advantage of what is

TABLE 1.2 Optimal Strategies for Special Populations

Special Population Optimal Strategies

Respondents unable to Use best-informed surrogate

self-report

Analyze effect of potential bias on study results

Ethnic populations Use interviewers of same ethnic background

Use nutrient composition database reflective of foods consumed

For FFQs, use appropriate food list and nutrient composition database

Children For young children, use caretakers in conjunction with child

For older children and adolescents, blended instrument and other creative ways of engagement and

motivation may work best

For FFQs, use appropriate food list and portion size categories

Elderly Assess any special considerations, including memory, special diets, dentition, use of supplements, etc.,

and adapt methods accordingly

Dietary Assessment Methodology Chapter | 1 23

known about how respondents remember dietary informa-

tion and how that information is retrieved and reported to

the investigator. The need for and importance of such

considerations in the assessment of diet has been dis-

cussed by several investigators [302,376,447449], and

research using cognitive testing methods [10,90,123,197,

215,253,267,448,450454] and other qualitative research

techniques [400,402,404,455  458] has been reported.

A thorough descr iption of cognitive interviewing methods

is found in Willis [459,4 60].

Specific and generic memories of diet are distinctly

different. Specific memory relies on particular memories

about episodes of eating and drinking, whereas generic

memory relies on general knowledge about typical diet.

A 24-hour recall relies primarily on specific memory of

all actual events in the very recent past, whereas an FFQ

that directs a respondent to report the usual frequency of

eating a food during the previous year relies primarily on

generic memory. As the time between the behavior and

the report increases, respondents may rely more on

generic memory and less on specific memory [448].

Investigators can do several things to enhance retrieval

and improve reporting of diet. Research indicates that the

amount of dietary information retrieved from memory can

be enhanced by the context in which the instrument is

administered and by use of specific memory cues and

probes. For example, for a 24-hour recall, foods that were

not initially reported by the respondent can be recovered

by interviewer probes. The effectiveness of these probes is

well-established and is therefore part of the interviewing

protocols for all standardized high-quality 24-hour recalls,

including those administered in the NHANES. Probes can

be useful in improving generic memory, too, when subjects

are asked to report their usual diets from periods in the

past [302,449]. Such probes can feature questions about

past living situations and related eating habits.

The way in which questions are asked can affect

responses. Certain characteristics of the interviewing situa-

tion may affect particular responses for foods viewed as

“good” or “bad.” For example, the presence of other family

members during the dietary interview may increase bias

duetosocialapprovalorsocialdesirabilitytraits[333,334],

especially for certain items such as alcoholic beverages. An

interview in a health setting, such as a clinic, may also

increase social approval bias in reporting about foods that

were previously proscribed or recommended in that setting.

In all instances, interviewers should be trained to refrain

from either positive or negative feedback and should repeat-

edly encourage subjects to accurately report all foods.

B Validation Studies

Validation studies yield information about how well the

primary or main method used to collect dietary data is

measuring what it is intended to measure. It is important

and desirab le that the main dietary assessment method be

evaluated against a less-biased reference method

[179,180,182,461]. Furthermore, even if an instrument

has

been evaluated and shows satisfactory results, its pro-

posed use in a different population may warrant additional

validation research in that population. The purposes of

such studies are to better understand how the method

works in the particular research setting, to improve it if

possible, and to use that information to better interpret

results from the overall study.

There are two types of validation studies. The first

assesses the validity of

reported intakes for a specific

number of days or meals in comparison to reference mea-

sures that approximate truth such as direct observation,

feeding studies, or recovery biomarkers for a time period

exactly consistent with each self-reported intake day. The

results of this type of study provide estimates of differ-

ences in true versus reported intakes of nutrients and food

groups, proportion of foods and drinks accurately reported

and omitted, and correlation coefficients. This type of

study can only be used for short-t erm instruments such as

24-hour recalls or food records. For example, if the

24-hour recall or food record is the main instrument in a

study, available reference instruments include observa-

tional techniques, feeding studies, or recovery biomarkers

[115,390,462,463]. In observation or feeding studies,

accuracy

can be assessed by determining the matches,

intrusions and exclusions in the foods reported compared

to true intakes, and for matches differences between

actual and reported nutrient and food group intakes

and portion sizes [93,464,465]. Recovery biomarkers

are

unbiased reference instruments and include 24-hour

urine collections to measure protein, sodium, and

potassium intakes and doubly labeled water which

measures energy expenditure and is used as a measure

of energy intake when individuals are in energy balance

[4147,98,1

67,168,170,171,466,467]. In studies using

recovery biomarkers as the reference instruments, intakes

estimated from the biomarkers can be compared to

reported intakes from recalls or food records to assess

reporting error. However, the high cost and increased

respondent burden can make the collection of recovery

biomarkers impractical for many studies. Additionally,

known recovery biomarkers are limited in number.

The second type of validation study assesses how well

reported intakes match tru e usual intakes and collects ref-

erence measures such as recovery biomarkers or less-

biased self-report dietary assessment instruments for a

time period not exactly consistent with each self-reported

intake day. This type of validation study can be used

across all self-report dietary assessment instruments when

interest is in obtaining validation measures of usual

intake. For example, when an FFQ is used as the main

24 PART | A Assessment Methods for Research and Practice

study instrument, it can be evaluated in a study that com-

pares it to another less-biased dietary assessment method,

such as 24-hour recalls or dietary records and, preferably,

to recovery biomarkers. The results are summarized by

statistics such as correlation coefficients, bias, and attenu-

ation factors. Correlation coefficients are related to the

loss of power to detect relationships between diet and

health outcomes. They are also useful for estimating the

sample size required in a study because the less precise

the diet measure, the more individuals will be needed to

attain the desired statistical power [468]. Bias provides

information about the difference between average

reported intake and average true intake, at the group level.

Attenuation factors represent bias in the estimated effect

of self-reported dietary components on a health outcome.

Some of this “attenuation bias,” can be addressed through

the use of measurement error models that allow for

within-person error in the reference instrument, resulting

in estimates that more nearly reflect the correlation

between the diet measure and true diet [325,468].Itis

important to note that when an FFQ is being evaluated

using other biased and imperfect self-report reference

instruments such as dietary records or 24-hour recalls,

reporting errors between an FFQ and records/recalls are

correlated, therefore, the statistical measures that result,

such as correlation, bias, and attenuation, will be overly

optimistic compared to those determ ined from unbiased

reference instruments such as recovery biomarkers.

Validation and calibration studies (see below) are

challenging because of the dif ficulty and expense in

collecting reference dietary information. Because of this,

such studies are done frequently on subsamples of the

total study sample. If possible, the subsample should be

chosen randomly. In addition, it should be sufficiently

large to estimate the relationship between the study instru-

ment and a reference method with reasonable precision.

Increasing the numbers of individuals sampled and

decreasing the number of repeat measures per individual

(e.g., for an FFQ validation, collecting two nonconsecu-

tive 24-hour recalls on 100 people rather than four recalls

on 50 people) often can help to increase precision without

extra cost [469]. The subsequent analyses quantify the

relationship between the primary or main dietary intake

tool and the reference method, and the resulting statistics

can be used for a variety of purposes.

Too often, the term “validated” is used indiscrimi-

nately in research publications, to imply that the instru-

ment is “valid,” rather than that the instrument has been

evaluated [470]. Thus the existence of a validation study

is used by some to imply that the instrument is valid,

regardless of the validation study’s results. Often, valida-

tion coefficients in the range of 0.40.6 are presented as

evidence that an instrument is valid. In reality, however ,

such findings should not be used to answer a “yes” or

“no” question with respect to whether or not an instru-

ment is “valid.” Instead, readers should consider how the

instrument performed for the purpose of study planning or

instrument improvement. One should also consider

whether the validation study design used unbiased or

imperfect reference measures to evaluate the main instru-

ment. The identification of additional unbiased references

is needed to allow more extensive evaluation of self-

report dietary assessment instruments.

The NCI maintains a register of validation/calibration

studies and publications on the web [166].

C Calibration and Regression Calibration

The term “calibration” is used to refer to the rescaling of

dietary data obtained from a more biased, less accurate

instrument using information obtained from a less-bias ed,

more accurate instrument. A calibrated instrument can be

used to estimate population means and compare subpopu-

lation means more accurately than an instrument that has

not been calibrated. Calibration is distinct from “regres-

sion calibration,” a term used to describe a method that

uses calibration as part of a statistical procedure to better

estimate associations (e.g., relative risks) between diet

and other factors, such as health outcomes.

Calibration can be used to relate reported intakes on

an FFQ or screener to a more accurate reference instru-

ment administered in the same population. For example, a

study may administer an FFQ to all respondents and the

reference instrument (such as 24-hour dietary recalls) to a

subsample. Alternatively, external calibration using data

from a reference population different from the study pop-

ulation can be performed. In this case, the external popu-

lation should be similar to the study population. In both

situations, scoring algorithms are estimated and used to

rescale the dietary data from the screener. The use of

such scoring algorithms for screeners has been shown to

lead to estimates of mean intakes that are closer to means

estimated with 24-hour recall than those derived solely

from screeners.

Regression calibration is a method used to adjust esti-

mates of associations between diet and health outcomes

for measurement error. This requires a main dietary

assessment instrument collected among all study subjects

and a reference instrument collected in at least a subsam-

ple. This data to accomplish regression calibration often

come from a v alidation study (described above). In cohort

studies, the main instrument has most often been an FFQ,

although the use of multiple recalls or multiple-day food

records is now more feasible than in the past. The esti-

mated regression relationship between an FFQ and the

reference method is used to adjust the relationships

between diet and outcome (e.g., relative risk of disease

for subjects with high nutrient intake compared to those

Dietary Assessment Methodology Chapter | 1 25

with low intake) as assessed in the larger study

[164,175,176,325,471,472]. Many of these adjustments

require the assumption that the reference method is unbi-

ased [175,323]. However, as discussed above, at least for

most nutrients and food groups, the reported intakes from

reference instruments such as recalls and records are

biased in a manner correlated with FFQ [149], violating

this assumption, which leads to overestimates of validity.

For these reasons, researchers use recovery biomarkers

such as urinary nitrogen and doubly labeled water when

possible because they are unbiased measures of intake.

However, because these are available for only a few nutri-

ents, data from imperfect reference instruments such as

24-hour dietary recalls or food records are used. Such

data are assumed to be unbiased for true usual intake,

even though they fall short of this ideal. Although using

these imperfect reference instruments does not completely

adjust estimated diet-outcome associations for the bias

caused by dietary measurement error, on average, it may

produce less-biased results than an unadjusted standard

analysis based solely on FFQ data. Another area in need

of further study is the effect of measurement error in a

multivariate context because most research thus far has

been limited to the eff ect on univariate relationships

[178,182,473,474].

D Mode of Administration

Instruments may be interviewer-administered or self-

administered. Interviewer-administered questionnaires may

be in person or by telephone. A self-administered instru-

ment may be completed on paper or electronically. All of

these modes are currently used for dietary assessment.

For interviewer-administered instruments, telephone

administration is less costly than in-person adm inistration.

However, concern is increasing about response rates in

telephone surveys, given the public’s distaste for preva-

lent telemarketing, technology that allows for screening

of calls, the increase in the proportion of the population

(especially young adults [475]) who use only wireless

telephones, and the general resistance of the public to

engage in telephone interviews. For these reasons,

response rates obtained using random digit dialing techni-

ques have been dropping.

Despite these difficul ties, many surveys and studies do

collect dietary data over the telephone. For example,

BRFSS [193] and the CHIS [195], both, include dietary

screeners. NHANES [299] administers an initial 24-hour

recall at the examination site and a second 24-hour recall

later by telephone. For 24-hour recalls collected by tele-

phone, the difficulty of reporting serving sizes can be

eased by mailing picture booklets or other portion size

estimation aids to participants before the interview. Many

studies have evaluated the comparability of data from

telephone versus in-person 24-hour recall interviews.

Several have found substantial but imperfect agreement

between dietary data collected by telephone and that esti-

mated by other methods, including face-to-face interviews

[74,476478] or observed intakes [479]. Godwin et al.

[480] and Yanek et al. [481] examined the accuracy of

portion size estimates for known quantities of foods con-

sumed that were assessed by telephone and by in-person

interviews. Both estimates were found to be similarly

accurate.

Self-administration is less costly than interv iewer-

administration. In addition, self-administered surveys tend

to minimize social desirability bias [482]. Howeve r, self-

administration may not be feasible for segments of the

population who have low literacy levels or limited moti-

vation. Thus, selection bias is a potential problem.

Web-administered questionnaires have cost advantages

and have become popular as the penetrance of the

Internet increases. In 2013, 79% of households in the

United States had Internet access [483]. Various FFQs

[122], dietary history questionnaires [484], screeners

[250,485], and 24-hour recall instruments [76,88,486]

have been developed for web administration. In general,

it has been found that initial response rates for web ques-

tionnaires are substantially lower than those for mailed or

telephone interviewer questionnaires [487]. One study

conducted in Sweden found a lower initial response rate

to a web questionnaire compared to a mailed printed

questionnaire but greater compliance in answering follow-

up questions over the web [488]. Web-administered ques-

tionnaires may be more effective than telephone

interviewer-administered questionnaires for presentation

of complex questio ns that are better processed visually

than aurally by respondents and that can be answered at a

pace set by the respondent rather than by the interviewer

[489]. Beasley et al. [490] found that the responses to

questions about diet on a web-administered FFQ were not

significantly different from responses on a paper version

of the same questionnaire. One large-scale survey found

that self-administered 24-hour recalls using the Internet

yielded nutrient intake estimates similar to interviewer

telephone-administered 24-hour recalls [94]. The Internet

version was pref erred over the telephone-administered

version by 70% to 30% [94] .

Dietary assessment with mobile phones or tablets is an

active area of devel opment and research. Several self-

administered 24-hour recalls instruments are available on

mobile devices [76]. Use of mobile phones to record and

photograph foods is also possible [491,492]. Sharp et al.

recently reviewed evaluative studies of mobile phones to

assess diet [493] and found that validity was comparable

but not superior to other conventional methods. Further

studies in larger and more diverse populations comparing

these mobile devices to other modes of data collection are

26 PART | A Assessment Methods for Research and Practice

needed to examine comparability as well as the potential

for self-selection biases.

E Estimation of Portion Size

Research has shown that untrained individuals have diffi-

culty in estimating portion sizes of foods, both when

examining displayed foods and whe n reporting about

foods previously consumed [91,389,399,480,494 510].

One study indicates that literacy, but not numeracy, is an

important factor in an individual’s ability to accurately

estimate portion size [511]. Furthermore, respondents

appear to be relatively insensitive to changes made in

portion size amounts shown in reference categories asked

on FFQs [512]. Portion sizes of foods that are commonly

bought and/or consumed in defined units (e.g., bread by

the slice, pieces of fruit, and beverages in cans or bottles)

may be more easily reported than amorphous foods

(e.g., steak, lettuce, and pasta) or poured liquids [91,509].

Other studies indicate that small portion sizes tend to

be overestimated and large portion sizes underestimated

[496,508,513].

Aids are commonly used to help resp ondents estimate

portion size. Research showing that different type s of aids

are more or less effective for different types of foods

[417,510,514] indicates that having multiple types of aids

available may be optimal. The NHANES What We Eat in

America uses an extensive set of three-dimensional mod-

els for an initial in-person 24-hour dietary recall [515] .

Respondents then are given a Food Model Booklet devel-

oped by the USDA [516] along with a limited number of

three-dimensional models and household measures (e.g.,

measuring cups and spoons) for recalls collected by tele-

phone. Food pictures and models have been developed for

other eating patterns, for exampl e, Asian foods [517] and

foods consumed in Mexico [518]. The accuracy of report-

ing using either models or household measures can be

improved with training [412,519521], but the effects

may deteriorate with time [522]. Studies comparing the

use of either household measures or pictures among chil-

dren and adolescents indicate that pictures outperform

household measures [514,518]. Studies that have com-

pared three-dimensional food models to two-dimensional

photographs in adults have shown that there is little dif-

ference in the reporting accuracy between methods

[388,480,523,524]. One study in children, however,

showed that usin g food models resulted in somewhat

larger error than using digital images [506]. Portion size

pictures, however presented, should be tailored to the par-

ticular populations and ages.

With the increased use of technology in dietary assess-

ment, digital food images in multiple portion sizes are

being tested. Studies have investigated the effects of num-

ber of portion pictures, size of picture, and concurrent

versus sequential display on accuracy of report

[91,399,505]. Such studies indicate preferences by respon-

dents but generally little difference in accuracy. However,

in two studies, one with adults [91] and the other with

children [400], accuracy was higher when more portion

size choices were offered. An emerging use of digital

technology removes respondent judgments of portion size,

instead relying on digital images of foods taken before

and after consumption, either actively by the respondent

[525,526] or passively by a wearable camera [527,528].

Computer software is then used to both identify foods and

estimate the amount consumed.

F Choice of Nutrient and Food Database

It is necessary to use a nutrient composition database when

dietary data are to be converted to nutrient intake data.

Typically, such a database includes the description of the

food, a food code, and the nutrient composition per 100 g

of the food. The number of foods and nutrients included

varies with the database. Research on nutrients, other

dietary components, and foods is ongoing, and there is con-

stant interest in updating current values and providing new

values for a variety of dietary components of interest.

Some values in nutrient databases are obtained from

laboratory analysis; however, because of the high cost of

laboratory analyses, many values are estimated based on

conversion factors or other knowledge about the food

[529]. In addition, accepted analytical methods are not yet

available for some nutrients of interest [530], analytical

quality of the information varies with nutrient [530,531],

and the variances or ranges of nutrient composition of

individual foods are in most cases unknown but are

known to be large for some nutrients [532]. Rapid growth

in the food processing sector and the global nature of the

food supply add further challenges to estimating the mean

and variability in the nutrient composition of foods eaten

in a specific locale.

One of the USDA’s primary missions is to provide

nutrient composition data for foods in the U.S. food sup-

ply, accounting for various types of preparation [533].

Information about the USDA’s nutrient composition data-

bases is available at the USDA’s Nutrient Data

Laboratory home page [534]. The USDA produces and

maintains the Nutrient Database for SR. New releases are

issued yearly; these include information on new foods and

revised information on already included foods, and they

identify foods deleted from the previous version of the

database. The most recent release, SR28, includes infor-

mation on up to 150 food components for 8789 foods

[535], and is available online.

Interest in nutrients and food components potentially

associated with di seases has led the USDA to develop

specialized databases for a smaller number of food

Dietary Assessment Methodology Chapter | 1 27

components, such as flavonoids [534]. A separate data-

base developed by the USDA Food Surveys Research

Group—the Food and Nutrient Database for Dietary

Studies (FNDDS)—is used by many investigators in

analyses of foods reported in NHANES’ What We Eat

in America dietary recalls and is based on nutrient

values in the USDA SR database [92].TheFNDDSpro-

vides information for 65 nutrients and food components,

and has no missing data for nutrient fields.

Nutrient composition data are also com piled by a

number of other countries, and the International Network

of Food Data Systems maintains an international directory

of nutrient composition tables [536]. Combining different

food composition databases across countries poses com-

parability challenges, however. The European Food

Information Resource [537] was formed to support the

harmonization of food composition data among the

European nations [538]. The International Nutrient

Databank Directory, an online compendium developed by

the National Nutrient Databank Conference, provides

information about the data included in a variety of data-

bases, national reference databases, and specialized data-

bases developed for software applications, such as the

date the database was mos t recently updated, the number

of nutrients provided for each food, and the completeness

of the nutrient data for all foods listed [539].

In addition to nutrient databases, databases that relate

dietary intake to dietary guidance have been developed in

the United States [540,541]. The USDA Food Patterns

Equivalents Database (FPED) provi des quantities of

specific food groups consistent with dietary guidance

recommendations in order to allow for evaluation of

whether diets meet dietar y guidelines at a variety of calo-

rie levels [542]. Just as FNDDS provides nutrient compo-

sition data, the FPED provides food group data per 100 g

of each food code in FNDDS. Importantly, mixed dishes,

such as pizza, are disaggre gated to their food group com-

ponents. The FPED contains data for 37 food group com-

ponents (e.g., dairy, fruits, vegetables) [543].

Other databases are available in the United States for

use in analyzing dietary records and 24-hour recalls, but

most are based fundame ntally on the USDA SR database,

often with added foods and specific brand names. One

prominent such database is the University of Minnesota’s

Nutrition Coordinating Center’s (NCC) Food and Nutrient

Database [544]. This database includes information on

165 nutrients, nutrient ratios, and other food compon ents

for more than 18,000 foods, including 8000 brand-name

products. The NCC is constantly updating its database to

reflect values in the latest release of the USDA SR

database.

One limitation in all nutrient databases is the variabil-

ity in the nutrient content of foods within a food category

and the volatility of nutrient composition in manufactured

foods. Recent changes in the sodium and fatty acid com-

position of manufactured foods, for example, illustrate the

difficulty in maintaining accurate nutrient composition

databases [545,546]. Obviously, a key consideration is

how the database is maintained and supported.

Estimates of nutrient intake from 24-hour recalls and

dietary records are often affected by the nutrient composi-

tion database that is used to process the data [547549].

Inherent differences in the database used for analysis

include factors such as the number of food items included

in the database, how recently nutr ient data were updated,

and the number of missing or imputed nutrient composi-

tion values. Therefore, before choosing a nutrient compo-

sition database, a prime factor to consider is the

completeness and accuracy of the data for the nutrients of

interest. For some purposes, it may be useful to choose a

database in which each nutrient value for each food also

contains a code for the quality of the data (e.g., analytical

value, calculated value, imputed value, or missing).

Investigators need to be aware that a value of zero is

assigned to missing values in some databases, whereas for

other databases, the number of nutrients provided for each

food may fluctuate depending on whether or not a value

is missing, and for others all unknown values may be

imputed.

The nutrient database should also include weight/vol-

ume equivalency information for each food item. Many

foods are reported in volumetri c measures (e.g., 1 cup)

and must be converted to weight in grams in order to

apply nutrient values. The number of common mixtures

(e.g., spaghetti with sauce) available in the database is

another important factor. If the study requires precision of

nutrient estimates, then procedures for calculating the

nutrients in various mixtures must be developed and

incorporated into nutrient composition calculations.

Developing a nutrient database for an FFQ presents

additional challenges [550] because each item on the FFQ

represents a food grouping rather than an individual

food item. Various approaches that rely on 24-hour recall

data, either from a national population sample or from a

sample similar to the target population, have been used

[551553]. Generally, individual foods reported on

24-hour recalls are grouped into FFQ food groupings, and

a composite nutrient profile for each food grouping is

estimated based on the individual foods’ relative con-

sumption in the population. For this approach to be effec-

tive, the 24-hour recall data must be representative of the

population for whom the FFQ is designed and connected

to a trustworthy nutrient database.

G Choice of Dietary Analysis Software

Data processing of 24-hour recalls and dietary record

requires creating data that include a food code and an

28 PART | A Assessment Methods for Research and Practice

amount consumed for each food reported. Computer soft-

ware then links the nutrient composition of each food on

the separate nutrient composition database file, converts

the amount reported to multiples of 100 g, multiplies by

that factor, stores that information, and sums across all

foods for each nutrie nt for each individual for each day of

intake. Many software packages have been developed that

include both a nutrient composition database and software

to convert individual responses to specific foods and, ulti-

mately, to nutrients. A listing of many commercial dietary

analysis software products has been compiled [539].

Software should be chosen on the basis of the researc h

needs, the level of detail necessary, the quality of the

nutrient composition database, and the hardware and soft-

ware requirements [554]. If precise nutrient information is

required, it is important that the system be able to expand

to incorporate information about newer foods in the mar-

ketplace and to integrate detailed information about food

preparation by processing recipe information (e.g., the

ingredients and cooking steps for homemade stew).

Sometimes the study purpose requires analysis of dietary

data to derive intake estimates not only for nutrients but

also for food groups (e.g., fruits and vegetables), food

components other than standar d nutrients (e.g., nitrites),

or food characteristics (e.g., fried foods). These additional

requirements limit the choice of appropriate software.

The semiautomated food coding system used for

NHANES is USDA’s Dietary Intake System, consisting

of the AMPM for collecting food intakes; the Post-

Interview Proce ssing System, which translates the AMPM

data and provides initial food coding; and the Survey Net

food coding system for the final coding of the intake data

[86]. Survey Net is a network dietary coding system that

provides online coding, recipe modification and develop-

ment, data editing and management, and nutrient analysis

of dietary data; multiple users can use the software to

manage the survey activities. It is available to government

agencies and the general public only through special

arrangement with the USDA. NCI’s ASA24 instrument

performs automated coding of all reported foods. Foods

which are not completely described are assigned default

values.

Many diet history and food frequency instruments

have also been automated. Users of these software

packages should be aware of the source of information in

the nutrient database and the assumptions about the nutri-

ent content of each food item listed in the questionnaire.

H Estimating Usual Intakes of Nutrients and

Foods

Usual intake is conceptualized as the long-term average

intake of a food or nutrient. The concept of long-term

average daily intake, or “usual intake,” is important

because dietary recommendations are intended to be met

over time and diet health hypotheses are based on

dietary intakes over the long term. Consequently, it is the

usual intake that is often of most interest to policymakers

(e.g., the proportion of the population at or below a

certain level of intake) or to researchers (e.g., relation-

ships between diet and health).

Data from FFQs, 24-hour recalls, and dietary records

have all been used to estimate usual intake at the group

level. Obtaining accurate estimates of usual intake at the

individual level is generally not possible with the dietary

assessment tools available even for FFQs which attempt

to estimate usual intake generally over a longer period

such as the past year. FFQs are known to contain a sub-

stantial amount of measurement error (see Section II.C)

[54,79,100103,117,149]. Dietary recalls or records

generally provide more accurate short-term intake esti-

mates than frequency-type instruments.

For estimates of mean usual intake in the population,

data from just a single day of recall or record can be

used. Multiple days of recalls and records are needed to

estimate the distribution of intakes. However, the distribu-

tion of simple within-person averages of intakes acro ss a

few days does not adequately represent the population’s

usual intake distribution [555], because of the large

day-to-day variability of individuals’ diets. Distributions

generated from averaging only a few days of data are

generally substantially wider than those of true usual

intakes, and thus lead to overestimating the proportion of

the population above or below a certain cut point, as

illustrated in Fig. 1.1.

Intake

Single-day intake

2-day mean intake

Usual intake

Density

FIGURE 1.1 Effect of day-to-day variability on distributions. Adapted

from NCI Dietary Assessment Primer, Epidemiology and Genomics

Research Program, Division of Cancer Control and Population

Sciences, National Cancer Institute. Available from https://dietassess-

mentprimer.cancer.gov/.

Dietary Assessment Methodology Chapter | 1 29

Statistical modeling can be used to more accurately

portray the population’s distribution by analytically esti-

mating and removing the effects of day-to-day variation

in dietary intake [555]. These methods rely on a minimum

of two administrations of 24-hour recalls or dietar y

records to capture day-to- day variation. The earliest

efforts at statistical modeling of usual intake were made

by the Institute of Medicine [556] for nutrients, most of

which are consumed nearly every day by most everyone,

and then extended and updated for nutrients or foods that

are more episodically consumed (e.g., dark green vegeta-

bles) by researchers at Iowa State University [557559].

Others have developed usual intake statistical approaches

as well [189,560563]. The NCI method uses a minimum

of two 24-hour recalls to estimate intake of both nutrients

and episodically consumed foods [296]. This model as

well as others [189] allows for covariates such as sex,

age, race/ethnicity, or information from an FFQ to supple-

ment the model [562]. One study using the NCI method

showed that including FFQ data as covariates in modeling

usual intakes from 24-hour recalls increased prec ision for

assessing the relationship of a highly epis odically con-

sumed food, fish, with blood mercury levels [190].

Modeling usual intakes to assess relationships to health

outcomes by combining data from a few 24-hour recalls

with an FFQ has been shown to provide better estimates

compared to a single FFQ or a few 24-hour recalls alone

[188,189,295].

The NCI Measurement Error Webinar Series [564]

provides a thorough discussion of dietary measurement

error, including usual intake estimation.

ACKNOWLEDGMENTS

We gratefully acknowledge the contributions of Susan M. Krebs-

Smith and Thea Zimmerman in reviewing and editing portions of

this chapter. We also thank Penny Randall-Levy for invaluable

research assistance.

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