nutrition questions

| October 19, 2015

Question 2:

Calcium is one of the most important minerals in the human body, and has been shown to have an important, interactive role with one of the fat-soluble vitamins, Vitamin D. Using the material from the assigned readings and discussions, list the vitamin of relevance and discuss how these two nutrients work together, and under what conditions a deficiency of one might result in a deficiency of the other. If there are any key ages of greater or lesser importance or sex differences – make sure to discuss such factors.

Answer to Question 2:

Reference(s) for Question 2:

Question 3

The attached journal reading describes several methods of diet assessment. Review that article – and list the pros and cons (strengths and limitations) of the 24-hour recall, food frequency list or questionnaire, and the diet history method.

Answer to Question 3:

Reference for Question 3:

Shim, J. S., Oh, K., & Kim, H. C. (2014). Dietary assessment methods in epidemiologic studies. Epidemiol Health, 36, e2014009.

Question 4:

List and describe specific characteristics of three possible negative health-related outcomes associated with adolescent obesity if left unchecked going into adulthood. (Make sure you provide references and citations and that your answers are specific). Number your answers 4a, 4b, and 4c.

Reference(s) for Question 4.

Dietary assessment methods in epidemiologic studies
Jee-Seon Shim1
, Kyungwon Oh2
, Hyeon Chang Kim1,3
1
Cardiovascular and Metabolic Diseases Etiology Research Center, Yonsei University College of Medicine, Seoul; 2
Division of Health and
Nutrition Survey, Korea Centers for Disease Control and Prevention, Osong; 3
Department of Preventive Medicine, Yonsei University College of
Medicine, Seoul, Korea
Diet is a major lifestyle-related risk factor of various chronic diseases. Dietary intake can be assessed by subjective
report and objective observation. Subjective assessment is possible using open-ended surveys such as
dietary recalls or records, or using closed-ended surveys including food frequency questionnaires. Each method
has inherent strengths and limitations. Continued efforts to improve the accuracy of dietary intake assessment
and enhance its feasibility in epidemiological studies have been made. This article reviews common dietary
assessment methods and their feasibility in epidemiological studies.
KEY WORDS: Dietary assessment, Food frequency questionnaire, 24-hour dietary recall, Dietary record
INTRODUCTION
Diet is a major lifestyle-related risk factor of a wide range of
chronic diseases. Changes in dietary habits have been found to
reduce cancer incidence by one-third [1]. Dietary information
has been useful in cardiovascular disease risk prediction [2] and
consuming a nutrient-dense diet was associated with a low risk
of all-cause mortality [3]. Contrary to other lifestyle risk factors
(e.g., smoking), dietary exposures are very difficult to measure
because all individuals eat foods, even if the amount and the
kind of food consumed is various between subjects, and people
rarely perceive what they eat and how much they do [4]. Inaccurate
dietary assessment may be a serious obstacle of understanding
the impact of dietary factors on disease.
Specific biochemical markers have been used as a surrogate
to measure the dietary intake of selected nutrients or dietary
components in epidemiological studies [5-7]. Previous studies
have found these markers to be highly correlated with dietary
intake levels, free of a social desirability bias, independent of
memory, and not based on subjects’ ability to describe the type
and quantity of food consumed [8]. Thus, these biochemical
markers may provide more accurate measures than dietary intake
estimates do. However, a number of biomarkers have been
known to provide integrated measures reflecting their absorption
and metabolism after consumption, and they are also affected
by disease or homeostatic regulation, thus their values
cannot be translated into the subject’s absolute dietary intake
[9]. Moreover, the results based on biomarkers cannot provide
dietary recommendations to modify a subject’s dietary habit.
Thus, direct assessment of dietary intake may be more informative
than biomarkers are [8,10].
Among the available dietary assessment methods, the food
frequency questionnaire (FFQ) has been widely used in large
epidemiological studies since the 1990s. After doubts of their
accuracy were raised in the 2000s [11,12], numerous changes
to the assessment methods have been made. Some researchers
have shifted their focus and concentrated their efforts to improve
the feasibility and accuracy of open-ended dietary assessment
methods rather than improve the FFQ or further find relevant
biomarkers. Other researchers have concentrated their
efforts to enhance the accuracy of the FFQ. Assessing dietary
exposure accurately with limited resources remains a challenge
for researchers. Thus, we aimed to review common methods for
dietary assessment and their feasibility in epidemiological studies.
Correspondence: Hyeon Chang Kim
Department of Preventive Medicine, Yonsei University College of Medicine,
Cardiovascular and Metabolic Diseases Etiology Research Center,
50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, Korea
Tel: +82-2-2228-1873, Fax: +82-2-392-8133, E-mail: hckim@yuhs.ac
Received: Jun 2, 2014, Accepted: Jul 22, 2014, Published: Jul 22, 2014
This article is available from: http://e-epih.org/
2014, Korean Society of Epidemiology
This is an open-access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
2
Epidemiology and Health 2014;36:e2014009
DIETARY ASSESSMENT BY OBJECTIVE OBSERVATION
Table 1 summarizes the available dietary assessment methods,
including the methods, collected data, strengths, and limitations
considering a conservative approach. Dietary intake can
be assessed by objective observation using a duplicate diet approach
or food consumption record by a trained research staff.
The duplicate diet method collects duplicate samples of a subject’s
normal diet, and then analyzes it to estimate potential dietary
exposures. This method has been mainly used to measure
exposure to environmental contaminants such as phthalates
and polycyclic aromatic hydrocarbons in foods and beverages
[13]. Food consumption record collects dietary information on
subjects’ food preparation and consumption in their home with
the objective observation of skilled field workers. This method
is useful in developing countries, especially among those with a
low literacy rate or those who prepare a substantial portion of
their food at home. In South Korea, the National Nutrition Survey
had surveyed households by this direct method to monitor
national food consumption from 1969 to 1995 [14]. Well-trained
staffs observed and recoded all foods prepared and consumed
in the surveyed household for two consecutive days. All data
were collected at the household level, thus no information on
how foods were consumed by each individual within household
or about foods consumed outside the home were collected.
Thus, each individual’s consumption was indirectly estimated
using data on the number, age, and sex of residents in each
household sharing the recorded food [15]. With improvement
in economic status, increase in eating-out, and advancement in
the individual dietary assessment techniques, assessment at the
individual level has become widespread in nutritional epidemiological
settings.
DIETARY ASSESSMENT BY SUBJECTIVE REPORT
Subjective dietary assessment methods that assess an individual’s
intake include the 24-hour dietary recall (24HR), dietary
record (DR), dietary history, and FFQ. Data are collected with
the help of a trained interviewer or by self-report.
Twenty-four-hour dietary recall and dietary record in a
conservative approach
The 24HR and DR are completely open-ended surveys and
collect a variety of detailed information about food consumed
over a specific period. The 24HR is conducted in an in-depth
interview manner and typically requires 20 to 30 minutes to
complete a single day recall. Detailed data about food preparation
methods, ingredients used in mixed dishes, and the brand
name of commercial products may be required according to the
Table 1. Dietary assessment methods in epidemiological studies
Duplicate diet
approach
Food consumption
record
24-Hour dietary
recall Dietary record Dietary history Food frequency
questionnaire
Methods Collection of duplicate
diet sample
and direct analysis
Objective observation
by trained staff
at the household
level
Subjective measure
using open-ended
questionnaires
administered by a
trained interviewer
Subjective measure using
open-ended, selfadministered
questionnaires

Subjective measures
using open- and
closed-ended
questionnaires
administered by a
trained interviewer
Subjective measure
using a predefined,
self- or intervieweradministered
format
Collected
data
Actual intake information
throughout a
specific period
Actual intake information
throughout a
specific period
Actual intake information
over the previous
24 hours
Actual intake information
throughout a specific
period
Usual intake estimates
over a relatively
long period
Usual intake estimates
over a relatively long
period (e.g., 6 months
or 1 year)
Strengths Measurement of dietary
exposures
possible (e.g., environmental
contaminants)

Ease of application
among those with
low literacy or those
who prepare most
meals at home
Provides detailed intake
data; relatively
small respondent
burden (literacy not
required)
Provides detailed intake
data; no interviewer
required; no recall bias
Assesses usual
dietary intake
Assesses usual dietary
intake simply; cost-effective
and time-saving;
suitable for epidemiological
studies
Limitations Not suitable for largescale
studies
Individual dietary
consumption not
accurate; Not
suitable among
those frequently
eat outside the
home
Possible recall bias;
trained interviewer
required; possible
interviewer bias;
expensive and timeconsuming;
multiple
days required to assess
usual intake;
possible changes
to diet if repeated
measures
Relatively large respondent
burden (literacy
and high motivation
required, possible
under-reporting); expensive
and time-consuming;
multiple days
required to assess
usual intake; possible
changes to diet if repeated
measures
High cost and timeconsuming;
not
suitable for epidemiological
studies
Specific to study
groups and research
aims; uses a closedended
questionnaire;
low accuracy (recall
bias); requires accurate
evaluation of developed
questionnaires

3
Shim J-S et al.: Dietary assessment methods in epidemiologic studies
research question. The amounts of each food consumed are estimated
in reference to a common size container (e.g., bowls,
cups, and glasses), standard measuring cups and spoons, a threedimensional
food model, or two-dimensional aids such as photographs.
One advantage of the 24HR is that a relatively minimal
burden is imposed on respondents. However, an inevitable
limitation is that all information depends on the respondents’
memory and the skills of a well-trained interviewer to minimize
recall bias. Conversely, DR collects data by subjects’ self-record
at the time the food are eaten, thus minimizes reliance on a subjects’
memory. To obtain accurate data, however, respondents
must be trained before participating the survey. Therefore, a
high level of motivation is required and relatively large burden
is passed onto the respondents [4,15].
Both methods have a few common strengths. Both use openended
questions so that abundant information can be collected
and analyzed in various aspects. In addition, both methods can
be easily applied to diverse groups with a wide range of eating
habits and may be used to estimate the average intake of a certain
population. In many countries including South Korea, the
24HR is the most commonly used in national surveys [16], and
both methods are also frequently applied to randomized clinical
trials and cohort studies [17,18]. However, these methods
have limitations when used to study chronic diseases, a major
public health concern. One limitation is that both methods are
mainly focused on short-term intake, but long-term dietary exposure
is especially of interest when investigating chronic diseases.
Thus, to measure average intake, multiple 24HRs or DRs
are needed. Repeated measurement not only requires a lot of
resources and time but survey repetition can also influence a
respondents’ diet. Previous studies have found some respondents
may improve their dietary habits unintentionally through
self-reflection. However, some respondents may alter their diet
intentionally to avoid a burden on responses or even choose to
not report actual intake [4,15]. Another limitation spawns from
the open-ended format that requires considerable efforts in the
course of data collection, entry, and analyses. Each questionnaire
requires careful review by the research staff to ensure that
all reported data are included. After initial review, all foods and
mixed dishes consumed according to the detailed descriptions
of the respondents should be matched and coded with the most
appropriate food listed in the food composition database. Moreover,
the quantity of food consumed should be converted to its
actual weights. When the reported information is changed to
the corresponding food code and weight, actual intakes can be
calculated. These processes tend to be time-consuming, laborious,
and highly expensive to implement.
Twenty-four-hour dietary recall and dietary record with
newer technologies
Despite the aforementioned limitations, multiple 24HRs and
DRs have inherent strengths in etiologic studies of chronic diseases.
First, both methods collect actual intake on specific days.
Second, the burden of memory may be less for these methods
than that of the FFQ, which requires recall over a long period
(e.g., the previous 12 months). Last, usual intake can also be estimated
if repeated. Owing to these strengths, innovative technologies
focusing on reducing the respondents’ burden, improving
accuracy, and making multiple self-administrations possible
have been recently incorporated to improve their feasibility in
epidemiological studies. Recently, several reports have discussed
their use and implications in clinical and research settings [19-
21].
Although many techniques are still under development, major
advances have been made. Interactive computer-based technologies,
which were introduced relatively early in dietary assessment
method development, aims to be a comprehensive
system for data collection, coding, entry, and calculation of intakes.
Examples includes the Automated Multiple Pass Method
(AMPM) for administering the 24HR in the US National Health
and Nutrition Examination Survey [22] and a menu-driven standardized
24HR program (called the EPIC-Soft) in the European
Prospective Investigation into Cancer and Nutrition study [23]
that allow interviewers to collect, probe, and identify reported
intake in a standardized manner, thus improving the accuracy
of the data, even if they are used in diverse populations. Having
limitations in time, location, and the number of interviewers
available for each study, these technologies remain relatively
costly for implementation in large-scale epidemiological studies.
Considerably overlapped with the computer-based approach,
web-based technologies enable researchers to collect data regardless
of a time and a location, assuming internet access is
available. Recently the National Cancer Institute in the US. has
developed an internet-based technique, called the Automated
Self-Administered 24HR that is based on the AMPM approach
[24]. This internet-based technique includes an online tutorial,
digital images for food identification and portion-size estimation,
and various audio files. Thus, those with low literacy can
easily complete the survey, and researchers can collect real-time
data. Other internet-based technologies designed for face-toface,
standardized interview administration have been developed,
such as the Diet Evaluation System (DES) that was developed
in South Korea [25].
In addition, mobile phone applications that allow users to enter
dietary intake data have been released. Subjects can manually
record their diet by choosing corresponding items from a
pre-defined list of foods and beverages, and the quantity of food
consumed can be recorded by selecting from pre-defined por-
4
Epidemiology and Health 2014;36:e2014009
tion sizes [26]. In South Korea, SmartDiet is an application that
was developed for dietary management and education, and this
application have been evaluated for their effectiveness and feasibility
in clinical settings [27]. Multiple functions embedded in
a mobile device can be used to collect data. In Japan, the mobile
phone application (called Wellnavi) uses the subject’s camera
and mobile phone card to report everything that was consumed
by sending images before and after eating to the study
dietitian [28]. In addition, voice recording such as the Spoken
Diet Records has been used to collect data [29]. In Australia,
Nutricam allows subjects to capture an image of foods and drinks
before consumption and verbally describe the items in the image
[30]. Then, subjects upload both the image and voice file to
a website for analysis [30]. Recently a wearable electronic device
that resembles a necklace includes a camera, microphone,
and several other sensors has been introduced [31]. This technology
uses the video recording to collect dietary information,
and the software identifies eating episode and estimates the
amount consumed in the video file. Then, final dietary intakes
are calculated automatically. This method is likely to minimize
the burden of the subjects using objective observation; however,
the technology is still in the experiment stage for using in researches.

Most state-of-the-art technologies must give enormous potentials
to be adapted as a major dietary assessment tool in various
epidemiological studies to the conservative open-ended methods
depending on paper and pencil surveys [19,20,24,32,33].
Table 2 summarizes the strengths and limitations of dietary assessment
methods with newer techniques. Software development
and the required hardware need high costs in the early
stage of the research. However, only if they are prepared, DRs
and 24HRs with innovative technologies may reduce their costs
and resources for organizing study as well as collecting and handling
data, improve consistency of data, collect data in real time
and calculate dietary intakes automatically, and allow respondents
to focus on dietary assessment [20,23,25,32,33]. While
the feasibility of multiple 24HRs and DRs in epidemiological
studies has considerably improved with the help of these new
technologies, there are still some limitations. First, these methods
may be difficult to apply to certain populations who are not
familiar with innovative technologies or new devices [32]: Training
subjects on how to use these technologies and use a computer
including accessing the internet is also required [25]. Furthermore,
technical problems in data transfer, storage, battery
life, and other concerns must be improved [31]. Most importantly,
these new methods do not seem to overcome the methodological
problems related to self-report. A previous report
found that subjects still had difficulties in recalling and reporting
their diet, underreported in repeated assessments, and altered
food intake when they knew the survey date in advance
[19]. For these reasons, open-ended methods with new technologies
have not yet been widely implemented as the primary
assessment tool in epidemiological studies.
Dietary history
To assess individual long-term dietary intake, Burke [34] developed
a dietary history method in 1947. This method requires
that subjects complete a 24HR, 3-day food diary, and checklist
of foods usually consumed. Highly skilled professionals are required
to collect information on the participant’s usual diet using
an in-depth interview (approximately 90 minutes to complete).
Thus, this method is rarely used in epidemiological studies.
Food frequency questionnaire
The FFQ is an advanced form of the checklist in dietary history
method, and asks respondents how often and how much
food they ate over a specific period [4]. Presenting about 100
to 150 foods, this questionnaire takes 20-30 minutes to complete
and can self-administered or collected via interview. This
method enables the assessment of long-term dietary intakes in
a relatively simple, cost-effective, and time-efficient manner.
Thus, various FFQs have been widely employed as a practical
instrument since the 1990s [35-37]. FFQs should be developed
specifically for each study group and research purposes because
diet may be influenced by ethnicity, culture, an individual’s preference,
economic status, etc. [38]. In South Korea, approximately
20 FFQs have been developed and used in epidemiological
studies.
Table 2. Strengths and limitations of new techniques in dietary assessment
24-hour dietary recall Dietary record Food frequency questionnaire
Required technology Software, internet, etc. Software, internet, PDA, mobile phone,
application, etc.
Skip algorithms, questions that ask for multiple
details, pictures of foods, etc.
Strengths Standardized data collection possible
(reducing interviewer bias); likely reduce
time and cost; improve feasibility
Standardized, real-time data collection
possible; likely reduce time and cost;
improve feasibility
Able to collect complex information and
highly accurate data
Limitations Inherent bias related to self-report Inherent bias related to self-report; requires
participant training on how to use the
technology
Measurement errors related to methodology
remain
5
Shim J-S et al.: Dietary assessment methods in epidemiologic studies
In South Korea, the first FFQ was developed through modification
of the FFQs used in Western countries to meet Korean
diet characteristics [39]. After, some FFQs were developed following
the opinion of experienced dietitians and epidemiologists
based on the nutrient contents in Korean food and the results
of previous studies [40-42]. Recent FFQs have been developed
in a more sophisticated way using actual dietary data collected
by the open-ended surveys. Among the various foods consumed
by subjects, informative foods are selected according to
the extent to which the foods contribute specific nutrients intakes
or the extent that the foods explained between-persons
variations [43-47]. Then the selected foods are grouped by their
nutritional contents or cooking methods, and finally presented
in a closed-ended format.
According to the interests of the researchers, FFQs may focus
on the intake of specific nutrients [48,49], dietary exposures related
to a certain disease [43], or comprehensively assess various
nutrients [44,46,47]. In prospective studies, comprehensive
assessment is generally recommended because it enables us to
assess any dietary components, which were not important at
the beginning of a study but might emerge as an important factor
later. Comprehensive assessment also enables us to estimate
the intakes of various dietary components that might act as a
confounder in relation to a key dietary factor and diseases, which
allows for statistical adjustment.
According to the way which informative foods present in FFQs,
food-based FFQs [16,46,47] such as the Harvard FFQ [50,51]
and dish-based FFQs [43-45,52,53] have been developed. Korean
and Asian food mainly contains many mixed dishes that
are cooked with individual ingredient foods, seasonings, and
cooking oils. Thus, food-based FFQs may raise subjects’ burden
and increase response error, when their subjects do not typically
cook their food or are unaware of the ingredients. Moreover,
the food-based FFQ [54] tends to underestimate dietary intake
more than the dish-based FFQs do [44] because various seasonings
(e.g., salt, soy sauce, red pepper paste, soybean paste,
etc.) and cooking oils which are highly contributing to the nutrients
(e.g., energy, fat, sodium, and β-carotene intake, etc.) intakes
are not considered in dietary intake calculations [55,56].
Therefore, the dish-based approach has been recommended as
a new strategy to improve dietary assessment in countries with
an Asian diet [57-59].
Average consumption frequency can be assessed using openended
questions [41], but most FFQs collect data across nine
possible responses from never to three or more times per day.
Various answer choices have been used to improve data quality
and reduce the burden on the subjects [60]. For foods eaten
seasonally, subjects are typically asked how frequently and over
what duration they ate these seasonal foods [42,44,47]. For frequently
consumed foods such as coffee, answers are collected
directly as an open-ended question in some FFQs [44,61,62].
The utility of questions in FFQs about portion size has been
controversial [4]: Some researchers reported that between-person
variations in portion size were not important because that
variation tends to be smaller than the variation in frequency of
consumption [63]. In South Korea, however, data on the portion
size of some foods seems to be important, such as cooked
rice, because between-person variations might be highly explained
by the portion size rather than the frequency [64]. Until
now, semi-quantitative FFQs collecting data on the average
portion sizes in a closed format have been more widely used in
epidemiological studies [39,40,42-49,52,53,61,65-69] than has
been the simple FFQs which solely asks about the frequency
[16,70] or quantitative FFQs which queries about the amount
of food consumption using completely open-ended questions
[41], respectively.
FFQs, which use a closed format, should be evaluated for their
accuracy before being used as a dietary assessment tool in studies.
A correlation coefficients ranging from 0.5 to 0.7 is considered
moderate [4]; however, most FFQs from Asian countries including
South Korea tend to have correlation coefficients ranging
from 0.3 to 0.5 [40-43,61,66,67,71], which is lower than
that from Western countries.
Some researchers questioned the value of using FFQs in epidemiological
studies [11,12], and this topic continues to be highly
debated [57,72-76]. In addition, concentrated efforts to assess
usual dietary intakes accurately using FFQs as well as multiple
24HRs or DRs have been made. Newer techniques introduced
FFQs that can be optically scanned, perform complex
skip algorithms and probe multiple details, and range checks as
well as allows for the presentation of pictures of foods for ease
in reporting portion sizes. All of these efforts improve the quality
of dietary data and enhance our capability to collect complex
information.
CONCLUSION
Dietary intake is difficult to measure, and any single method
cannot assess dietary exposure perfectly. Nutritional biomarkers
are valid for objective estimates of dietary exposures in anthropometric
and clinical assessment, while the 24HR, DR, dietary
history, and FFQ are subjective estimates. Numerous efforts
have made progress in the accuracy of dietary intake assessment
methods, thus the feasibility of open-ended methods
with various innovative technologies in epidemiological studies
has been substantially enhanced. However, new methods needs
higher costs than the FFQs, and intrinsic problems related to
self-report remain unsolved. Notwithstanding the discussed
limitations, FFQs are still widely used as the primary dietary
6
Epidemiology and Health 2014;36:e2014009
assessment tool in epidemiological studies.
Recently, it has been suggested that a combination of methods,
such as the FFQ with DRs (or 24HR) or the FFQ with biomarker
levels, be used to obtain more accurate estimates of dietary
intakes than that of individual methods. Considerable efforts
to improve the accuracy and feasibility of large epidemiological
studies are still ongoing.
In summary, dietary assessment methods should be selected
with caution and while considering the research objective, hypothesis,
design, and available resources.
ACKNOWLEDGEMENTS
This review was supported by a grant of the Korean Health
Technology R&D Project, Ministry of Health & Welfare, Republic
of Korea (HI13C0715).
CONFLICT OF INTEREST
The authors have no conflicts of interest to declare for this
study.
REFERENCES
1.Doll R, Peto R. The causes of cancer: quantitative estimates of avoidable
risks of cancer in the United States today. J Natl Cancer Inst 1981;
66:1191-1308.
2.Baik I, Cho NH, Kim SH, Shin C. Dietary information improves cardiovascular
disease risk prediction models. Eur J Clin Nutr 2013;67:
25-30.
3.Streppel MT, Sluik D1, van Yperen JF1, Geelen A1, Hofman A, Franco
OH, et al. Nutrient-rich foods, cardiovascular diseases and all-cause
mortality: the Rotterdam study. Eur J Clin Nutr 2014;68:741-747.
4.Nutrition epidemiology. New York: Oxford University Press; 1998.
5.Kim YJ, Kim OY, Cho Y, Chung JH, Jung YS, Hwang GS, et al. Plasma
phospholipid fatty acid composition in ischemic stroke: importance
of docosahexaenoic acid in the risk for intracranial atherosclerotic
stenosis. Atherosclerosis 2012;225:418-424.
6.Kho M, Lee JE, Song YM, Lee K, Kim K, Yang S, et al. Genetic and
environmental influences on sodium intake determined by using halfday
urine samples: the Healthy Twin Study. Am J Clin Nutr 2013;98:
1410-1416.
7.Lim S, Shin H, Kim MJ, Ahn HY, Kang SM, Yoon JW, et al. Vitamin
D inadequacy is associated with significant coronary artery stenosis
in a community-based elderly cohort: the Korean Longitudinal Study
on Health and Aging. J Clin Endocrinol Metab 2012;97:169-178.
8.Potischman N. Biologic and methodologic issues for nutritional biomarkers.
J Nutr 2003;133 Suppl 3:875S-880S.
9.Kaaks R, Ferrari P, Ciampi A, Plummer M, Riboli E. Uses and limitations
of statistical accounting for random error correlations, in the
validation of dietary questionnaire assessments. Public Health Nutr
2002;5:969-976.
10.Wild CP, Andersson C, O’Brien NM, Wilson L, Woods JA. A critical
evaluation of the application of biomarkers in epidemiological studies
on diet and health. Br J Nutr 2001;86 Suppl 1:S37-S53.
11. Schatzkin A, Kipnis V, Carroll RJ, Midthune D, Subar AF, Bingham
S, et al. A comparison of a food frequency questionnaire with a 24-
hour recall for use in an epidemiological cohort study: results from
the biomarker-based Observing Protein and Energy Nutrition (OPEN)
study. Int J Epidemiol 2003;32:1054-1062.
12.Freedman LS, Potischman N, Kipnis V, Midthune D, Schatzkin A,
Thompson FE, et al. A comparison of two dietary instruments for
evaluating the fat-breast cancer relationship. Int J Epidemiol 2006;
35:1011-1021.
13.Fromme H, Gruber L, Schlummer M, Wolz G, Böhmer S, Angerer J,
et al. Intake of phthalates and di(2-ethylhexyl)adipate: results of the
Integrated Exposure Assessment Survey based on duplicate diet samples
and biomonitoring data. Environ Int 2007;33:1012-1020.
14.Kim S, Moon S, Popkin BM. The nutrition transition in South Korea.
Am J Clin Nutr 2000;71:44-53.
15.Margetts BM, Nelson M. Design concepts in nutritional epidemiology.
New York: Oxford University Press; 1997.
16.Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, et al. Data resource
profile: the Korea National Health and Nutrition Examination
Survey (KNHANES). Int J Epidemiol 2014;43:69-77.
17.Dauchet L, Kesse-Guyot E, Czernichow S, Bertrais S, Estaquio C,
Péneau S, et al. Dietary patterns and blood pressure change over 5-y
follow-up in the SU.VI.MAX cohort. Am J Clin Nutr 2007;85:1650-
1656.
18.Luke A, Bovet P, Forrester TE, Lambert EV, Plange-Rhule J, Schoeller
DA, et al. Protocol for the modeling the epidemiologic transition study:
a longitudinal observational study of energy balance and change in
body weight, diabetes and cardiovascular disease risk. BMC Public
Health 2011;11:927.
19.Illner AK, Freisling H, Boeing H, Huybrechts I, Crispim SP, Slimani
N. Review and evaluation of innovative technologies for measuring
diet in nutritional epidemiology. Int J Epidemiol 2012;41:1187-1203.
20.Shriver BJ, Roman-Shriver CR, Long JD. Technology-based methods
of dietary assessment: recent developments and considerations
for clinical practice. Curr Opin Clin Nutr Metab Care 2010;13:548-
551.
21.Stumbo PJ. New technology in dietary assessment: a review of digital
methods in improving food record accuracy. Proc Nutr Soc 2013;
72:70-76.
22.Moshfegh AJ, Rhodes DG, Baer DJ, Murayi T, Clemens JC, Rumpler
WV, et al. The US Department of Agriculture Automated Multiple-Pass
Method reduces bias in the collection of energy intakes. Am
J Clin Nutr 2008;88:324-332.
23.Slimani N, Casagrande C, Nicolas G, Freisling H, Huybrechts I, Ocké
MC, et al. The standardized computerized 24-h dietary recall method
EPIC-Soft adapted for pan-European dietary monitoring. Eur J Clin
Nutr 2011;65 Suppl 1:S5-S15.
24.Schatzkin A, Subar AF, Moore S, Park Y, Potischman N, Thompson
FE, et al. Observational epidemiologic studies of nutrition and cancer:
the next generation (with better observation). Cancer Epidemiol
Biomarkers Prev 2009;18:1026-1032.
25.Jung HJ, Lee SE, Kim D, Noh H, Song S, Kang M, et al. Development
and feasibility of a web-based program ‘Diet Evaluation System
(DES)’ in urban and community nutrition survey in Korea. Korean
J Health Promot 2013;13:107-115 (Korean).
26.Lieffers JR, Hanning RM. Dietary assessment and self-monitoring
with nutrition applications for mobile devices. Can J Diet Pract Res
2012;73:e253-e260.
7
Shim J-S et al.: Dietary assessment methods in epidemiologic studies
27.Lee W, Chae YM, Kim S, Ho SH, Choi I. Evaluation of a mobile
phone-based diet game for weight control. J Telemed Telecare 2010;
16:270-275.
28.Kikunaga S, Tin T, Ishibashi G, Wang DH, Kira S. The application
of a handheld personal digital assistant with camera and mobile phone
card (Wellnavi) to the general population in a dietary survey. J Nutr
Sci Vitaminol (Tokyo) 2007;53:109-116.
29.Lacson R, Long W. Natural language processing of spoken diet records
(SDRs). AMIA Annu Symp Proc 2006:454-458.
30.Rollo ME, Ash S, Lyons-Wall P, Russell A. Trial of a mobile phone
method for recording dietary intake in adults with type 2 diabetes:
evaluation and implications for future applications. J Telemed Telecare
2011;17:318-323.
31.Sun M, Fernstrom JD, Jia W, Hackworth SA, Yao N, Li Y, et al. A
wearable electronic system for objective dietary assessment. J Am
Diet Assoc 2010;110:45-47.
32.Long JD, Littlefield LA, Estep G, Martin H, Rogers TJ, Boswell C,
et al. Evidence review of technology and dietary assessment. Worldviews
Evid Based Nurs 2010;7:191-204.
33.Hercberg S. Web-based studies: The future in nutritional epidemiology
(and overarching epidemiology) for the benefit of public health?
Prev Med 2012;55:544-545.
34.Burke BS. The dietary history as a tool in research. J Am Diet Assoc
1947;23:1041-1046.
35.Bhupathiraju SN, Wedick NM, Pan A, Manson JE, Rexrode KM,
Willett WC, et al. Quantity and variety in fruit and vegetable intake
and risk of coronary heart disease. Am J Clin Nutr 2013;98:1514-
1523.
36.Méjean C, Droomers M, van der Schouw YT, Sluijs I, Czernichow S,
Grobbee DE, et al. The contribution of diet and lifestyle to socioeconomic
inequalities in cardiovascular morbidity and mortality. Int J
Cardiol 2013;168:5190-5195.
37.Nam CM, Oh KW, Lee KH, Jee SH, Cho SY, Shim WH, et al. Vitamin
C intake and risk of ischemic heart disease in a population with
a high prevalence of smoking. J Am Coll Nutr 2003;22:372-378.
38.Teufel NI. Development of culturally competent food-frequency questionnaires.
Am J Clin Nutr 1997;65:1173S-1178S.
39.Kim MK, Choi BY, Shin YJ, Ahn YO, Lee SS, Cho YS. Semiquantitative
food frequency method as an epidemiological tool in a rural
community, Korea. Korean J Epidemiol 1994;16:54-65 (Korean).
40.Shim JS, Oh KW, Suh I, Kim MY, Sohn CY, Lee EJ, et al. A study
on validity of a semi-quantitative food frequency questionnaire for
Korean adults. Korean J Community Nutr 2002;7:484-494 (Korean).
41.Ji SK, Kim HS, Choi HM. A study on development and validation of
food frequency questionnaire for estimating energy intake of women
in child-bearing age. Korean J Community Nutr 2008;13:111-124
(Korean).
42.Hong S, Choi Y, Lee HJ, Kim SH, Oe Y, Lee SY, et al. Development
and validation of a semi-quantitative food frequency questionnaire to
assess diets of Korean type 2 diabetic patients. Korean Diabetes J
2010;34:32-39.
43.Park MK, Kim DW, Kim J, Park S, Joung H, Song WO, et al. Development
of a dish-based, semi-quantitative FFQ for the Korean diet
and cancer research using a database approach. Br J Nutr 2011;105:
1065-1072.
44.Yun SH, Shim JS, Kweon S, Oh K. Development of a food frequency
questionnaire for the Korea National Health and Nutrition Examination
Survey: data from the fourth Korea National Health and Nutrition
Examination Survey (KNHANES IV). Korean J Nutr 2013;
46:186-196 (Korean).
45.Kim YO, Kim MK, Lee SA, Yoon YM, Sasaki S. A study testing the
usefulness of a dish-based food-frequency questionnaire developed
for epidemiological studies in Korea. Br J Nutr 2009;101:1218-1227.
46.Kim J, Kim Y, Ahn YO, Paik HY, Ahn Y, Tokudome Y, et al. Development
of a food frequency questionnaire in Koreans. Asia Pac J Clin
Nutr 2003;12:243-250.
47.Ahn Y, Lee JE, Paik HY, Lee HK, Jo I, Kimm K. Development of a
semi-quantitative food frequency questionnaire based on dietary data
from the Korea National Health and Nutrition Examination Survey.
Nutr Sci 2003;6:173-184.
48.Son SM, Huh GY, Lee HS. Development and evaluation of validity
of dish frequency questionnaire (DFQ) and short DFQ using Na index
for estimation of habitual sodium intake. Korean J Community
Nutr 2005;10:677-692 (Korean).
49.Park YK, Kim Y, Park E, Kim JS, Kang MH. Estimated flavonoids
intake in Korean adults using semiquantitative food-frequency questionnaire.
Korean J Nutr 2002;35:1081-1088 (Korean).
50.Hu FB, Rimm E, Smith-Warner SA, Feskanich D, Stampfer MJ, Ascherio
A, et al. Reproducibility and validity of dietary patterns assessed
with a food-frequency questionnaire. Am J Clin Nutr 1999;69:243-
249.
51.Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J,
et al. Reproducibility and validity of a semiquantitative food frequency
questionnaire. Am J Epidemiol 1985;122:51-65.
52.Bae YJ, Choi HY, Sung MK, Kim MK, Choi MK. Validity and reproducibility
of a food frequency questionnaire to assess dietary nutrients
for prevention and management of metabolic syndrome in
Korea. Nutr Res Pract 2010;4:121-127.
53.Na YJ, Lee SH. Development and validation of a quantitative food
frequency questionnaire to assess nutritional status in Korean adults.
Nutr Res Pract 2012;6:444-450.
54.Ahn Y, Lee JE, Cho NH, Shin C, Park C, Oh BS, et al. Validation
and calibration of semi-quantitative food frequency questionnaire:
with participants of the Korean Health and Genome Study. Korean J
Community Nutr 2004;9:173-182 (Korean).
55.Shim JE, Ryu JY, Paik HY. Contribution of seasonings to nutrient intake
assessed by food frequency questionnaire in adults in rural area
of Korea. Korean J Nutr 1997;30:1211-1218 (Korean).
56.Yun SH, Choi BY, Kim MK. The effect of seasoning on the distribution
of nutrient intakes by a food-frequency questionnaire in a rural
area. Korean J Nutr 2009;42:246-255 (Korean).
57.Kristal AR, Peters U, Potter JD. Is it time to abandon the food frequency
questionnaire? Cancer Epidemiol Biomarkers Prev 2005;14:
2826-2828.
58.Keshteli A, Esmaillzadeh A, Rajaie S, Askari G, Feinle-Bisset C, Adibi
P. A Dish-based Semi-quantitative Food Frequency Questionnaire
for Assessment of Dietary Intakes in Epidemiologic Studies in Iran:
Design and Development. Int J Prev Med 2014;5:29-36.
59.Kobayashi T, Tanaka S, Toji C, Shinohara H, Kamimura M, Okamoto
N, et al. Development of a food frequency questionnaire to estimate
habitual dietary intake in Japanese children. Nutr J 2010;9:17.
60.Subar AF, Thompson FE, Smith AF, Jobe JB, Ziegler RG, Potischman
N, et al. Improving food frequency questionnaires: a qualitative approach
using cognitive interviewing. J Am Diet Assoc 1995;95:781-
788.
61.Won HS, Kim WY. Development and validation of a semi-quantitative
food frequency questionnaire to evaluate nutritional status of Korean
elderly. Korean J Nutr 2000;33:314-323 (Korean).
62.Park MK, Noh HY, Song NY, Paik HY, Park S, Joung H, et al. Validity
and reliability of a dish-based, semi-quantitative food frequency
questionnaire for Korean diet and cancer research. Asian Pac J Cancer
Prev 2012;13:545-552.
8
Epidemiology and Health 2014;36:e2014009
63.Samet JM, Humble CG, Skipper BE. Alternatives in the collection
and analysis of food frequency interview data. Am J Epidemiol 1984;
120:572-581.
64.Kim MK, Choi BY. The influence of portion size data on the agreement
of classification of individuals according to nutrient estimates
by food frequency questionnaire in a rural area of Korea. Nutr Res
2002;22:271-281.
65.Paik HY, Ryu JY, Choi JS, Ahn YJ, Moon HK, Park YS, et al. Development
and validation of food frequency questionnaire for dietary
assessment of Korean adults in rural area. Korean J Nutr 1995;28:
914-922 (Korean).
66.Kim MK, Lee SS, Ahn YO. Reproducibility and validity of a selfadministered
food semiquantitative frequency questionnaire among
middle-aged men in Seoul. Korean J Community Nutr 1996;1:376-
394 (Korean).
67.Kim WY, Yang EJ. A study on development and validation of food
frequency questionnaire for Koreans. Korean J Nutr 1998;31:220-
230 (Korean).
68.Lim Y, Oh SY. Development of a semi-quantitative food frequency
questionnaire for pre-school children in Korea. Korean J Community
Nutr 2002;7:58-66 (Korean).
69.Lee HJ, Park SJ, Kim JH, Kim CI, Chang KJ, Yim KS, et al. Development
and validation of a computerized semi-quantitative food frequency
questionnaire program for evaluating the nutritional status of
the Korean elderly. Korean J Community Nutr 2002;7:277-285 (Korean).

70.Kim J, Lee Y, Lee SY. Legumes and soy products consumption and
functional disability in older women. Maturitas 2011;69:268-272.
71.Wakai K. A review of food frequency questionnaires developed and
validated in Japan. J Epidemiol 2009;19:1-11.
72.Willett WC, Hu FB. The food frequency questionnaire. Cancer Epidemiol
Biomarkers Prev 2007;16:182-183.
73.Willett WC, Hu FB. Not the time to abandon the food frequency questionnaire:
point. Cancer Epidemiol Biomarkers Prev 2006;15:1757-
1758.
74.Kelemen LE. Food frequency questionnaires: not irrelevant yet. Cancer
Epidemiol Biomarkers Prev 2006;15:1054.
75.Kristal AR, Potter JD. Not the time to abandon the food frequency
questionnaire: counterpoint. Cancer Epidemiol Biomarkers Prev 2006;
15:1759-1760.
76.Freedman LS, Schatzkin A, Thiebaut AC, Potischman N, Subar AF,
Thompson FE, et al. Abandon neither the food frequency questionnaire
nor the dietary fat-breast cancer hypothesis. Cancer Epidemiol
Biomarkers Prev 2007;16:1321-1322.

Get a 5 % discount on an order above $ 150
Use the following coupon code :
2020Discount
Conduct research to determine the common fuel sources a developing country of your choice, such as China, India, or Brazil.
Nuclear Medicine

Category: Science

Our Services:
Order a customized paper today!
Open chat
Hello, we are here to help with your assignments
Powered by