European Journal of Clinical Nutrition (2015), 1–27
© 2015 Macmillan Publishers Limited All rights reserved 0954-3007/15
www.nature.com/ejcn
REVIEW
A systematic review of brief dietary questionnaires suitable for
clinical use in the prevention and management of obesity,
cardiovascular disease and type 2 diabetes
CY England1, RC Andrews2, R Jago1 and JL Thompson3
The aim of this systematic review was to identify and describe brief dietary assessment tools suitable for use in clinical practice in
the management of obesity, cardiovascular disease and type 2 diabetes. Papers describing development of brief ( o 35 items)
dietary assessment questionnaires, that were accessible, simple to score and assessed aspects of the diet of relevance to the
conditions of interest were identified from electronic databases. The development of 35 tools was described in 47 papers. Ten tools
assessed healthy eating or healthy dietary patterns, 2 assessed adherence to the Mediterranean diet, 18 assessed dietary fat intake,
and 5 assessed vegetable and/or fruit intake. Twenty tools were developed in North America. Test-retest reliability was conducted
on 18 tools; correlation coefficients for total scores ranged from 0.59 to 0.95. Relative validation was conducted on 34 tools. The
most common reference variable was percentage energy from fat (15 tools) and correlation coefficients ranged from 0.24, P o0.001
to 0.79, P o 0.002. Tools that have been evaluated for reliability and/or relative validity are suitable for guiding clinicians when
providing dietary advice. Variation in study design, settings and populations makes it difficult to recommend one tool over another,
although future developers can enhance the understanding and use of tools by giving clear guidance as to the strengths and
limitations of the study design. When selecting a tool, clinicians should consider whether their patient population is similar in
characteristics to the evaluation sample.
European Journal of Clinical Nutrition advance online publication, 25 February 2015; doi:10.1038/ejcn.2015.6
INTRODUCTION
The World Health Organization estimates that in 2008, 18.3 million
deaths worldwide were due to cardiovascular disease and type 2
diabetes.1 In 2010, unhealthy dietary habits, including low fruit
and vegetable consumption, high salt intake and low whole-grain
and fish consumption, combined with physical inactivity, are
estimated to account for 10% of the global burden of disease.
Assisting people with dietary modification is, therefore, a key
challenge for health professionals.
In clinical care, dietary assessment is important for providing
individualised dietary advice2 and is essential for evaluating the
success of interventions aimed at improving dietary habits, such
as cardiac rehabilitation programs.3 Dietitians typically use food
diaries and take diet histories to obtain an overview of a patient's
usual diet, with dietary advice then given based on this
assessment. This process is time-consuming and interpretation
requires specialist skills.2 However, a highly detailed assessment of
nutrient intake is not always necessary in a clinical setting. It is
often enough to review an individual’s dietary habits to determine
the potential benefit of changing specific dietary behaviours and
foods/food groups.4
Brief dietary screening tools have been developed to assist with
dietary assessment in clinical practice. These tools take the form of
a brief questionnaire that can be self-completed prior to, or
administered during, a consultation. The answers allow health
professionals and patients to quickly identify whether a diet is
appropriate or whether there are areas of concern. Dietary
changes, based upon the patient’s current dietary habits, can be
discussed and food-based dietary goals set.5 For dietary tools to
be useful in clinical practice, they need to be interpretable with
minimal nutrition knowledge, quick to complete and easy to
score. They must provide immediate guidance on healthy dietary
changes or allow clinicians to quickly identify patients who may
benefit from more intensive dietary counselling. Dietary screening
tools have been designed to assess specific foods or nutrients,3,6,7
dietary behaviours associated with obesity8 or cardiovascular
disease,9–11 adherence to specific diets12,13 or as specific aids in
dietary counselling with a prompt sheet provided to guide
discussion.14,15 They take the form of short food frequency
questionnaires (FFQs), with16 or without17 portion estimates,
behavioural questionnaires18 or a combination of FFQ and
behavioural questions.7 They are unable to give estimates of
absolute intake but can classify individuals as high, medium or low
consumers of nutrients or foods of interest, allowing dietary
advice to be targeted to an individual. Questionnaires have also
been developed to rapidly evaluate the success of dietary
interventions, for example, to measure the effect of advice to
increase fruit and vegetable intake19 or follow a lipid-lowering
diet.20 These are responsive to change and can provide outcome
data to determine whether an intervention has succeeded in
improving dietary habits. Brief questionnaires are of interest to
dietary researchers,21 but the current review focuses on instruments that might be applicable in a clinical setting to obtain a
picture of an individual’s diet.
1
Centre for Exercise Nutrition and Health Sciences, School for Policy Studies, University of Bristol, Bristol, UK; 2School of Clinical Sciences, University of Bristol, Learning and
Research, Southmead Hospital, Bristol, UK and 3University of Birmingham, School of Sport, Exercise & Rehabilitation Sciences, Edgbaston, Birmingham, UK. Correspondence:
CY England, Centre for Exercise Nutrition and Health Sciences, University of Bristol, School for Policy Studies, 8 Priory Road, Bristol, BS8 1TZ, UK.
E-mail: clare.england@bristol.ac.uk
Received 4 March 2014; revised 4 December 2014; accepted 5 January 2015
A systematic review of brief dietary questionnaires
CY England et al
2
A review of brief dietary assessment tools for potential clinical
use was published in 2000,22 but many additional tools have been
developed since then and there is a need for an update. More
recently, the US National Cancer Institute published an online
registry of validated brief dietary assessment instruments.23
Although the registry provides an overview of the tools, it does
not facilitate comparisons and provides no summarised information about applicability to clinical practice.
Our aims were to: (i) identify and describe available brief dietary
screening tools that can be used in clinical practice for the
prevention and management of obesity, cardiovascular disease
and type 2 diabetes in adults; (ii) examine the acceptability,
reliability and/or relative validity of the tools; and (iii) summarise
the data so that clinicians can quickly assess which tool is most
suitable for use with their patient group. Details are also provided
about the availability of the tools and whether there are costs
associated with their use.
METHODS
Search strategy
Electronic databases MEDLINE, EMBASE, PsycINFO, AMED (Ovid
versions) and CINAHL (EBSCOhost version) to June 2013 (week 26)
were searched using MeSH terms and text words. Search terms
were based around general terms for nutritional and dietary
assessment, and were designed to identify brief questionnaires.
Terms included nutrition assessment, diet screen, food questionnaire, nutrient questionnaire and short, brief, rapid and adult.
The full list of search terms is included in the Supplementary
Information (Appendix 1). One author (CE) screened all titles and
abstracts. Full text articles were retrieved if abstracts appeared to
meet the inclusion criteria. Additional studies were identified from
reference lists and screened similarly. Studies were initially
assessed for inclusion by one author (CE). Where it was unclear
whether a study or questionnaire met the inclusion criteria a
second author (JT) screened the reports.
Inclusion and exclusion criteria
Dietary habits or foods relevant to adults at risk for cardiovascular
disease, overweight, obesity or type 2 diabetes were derived from
national and international guidelines.24–26 Risk increases with high
consumption of energy-dense foods, trans-fats, saturated fats,
sodium and alcohol, and decreases with high consumption of
high fibre foods, fruit and vegetables, fish and low glycaemic
index foods. Dietary patterns emphasising high fibre foods, low fat
dairy, poultry, fish, non-tropical vegetable oils and nuts, whereas
limiting red and processed meats and high fat or sugar foods and
drinks, are advised. Questionnaires assessing components of the
diet that increase or decrease risk were identified.
Tools were included if they had been evaluated for reliability or
relative validity against a biomarker or against another selfreported measure of dietary intake (dietary reference). In common
with the previous review,22 sample size was not considered. On
the basis of the clinical expertise of two authors (CE, RA), tools
were deemed to be practical for clinical settings if they were brief,
available in paper format or freely accessible on the Internet, could
be scored at administration without specialist computer software
and were capable of providing immediate feedback to patients and
practitioners on an individual level. Questionnaires were defined as
‘brief’ if they were estimated to take no more than 15 min to
complete. Mean allocated appointment times for new patients in
primary care have been reported as being between 16 and 32 min
and complete physicals as 12–36 min.27 Consequently, questionnaires taking more than 15 min to complete were judged as not
feasible for use in clinical practice. However, most studies did not
estimate completion time. Preliminary work, prior to conducting
the full review, identified mean completion times of 15 min for
European Journal of Clinical Nutrition (2015) 1 – 27
a 25-item questionnaire,28 10 min for 31-item,29 20-item9 and 16item10 questionnaires and 5–10 min for a 29-item questionnaire.5
Taking these measures into account, it was estimated that
questionnaires of up to 35 items could feasibly be completed in
15 min. Tools designed to be administered by a practitioner or
completed independently by the patient were both included.
Tools that assessed micronutrient intakes, protein intake,
malnutrition screening tools or those aimed at identifying
hazardous drinking were excluded. Questionnaires for single food
groups, such as oily fish and pulses and fruit and vegetable
questionnaires containing over 10 items, were considered to be of
limited use in clinical practice and were excluded. Studies were
excluded if they only reported the use of a questionnaire during
an intervention or observational study, or described tools that
were not tested for either reliability or relative validity. Owing to
the limitations of time and cost, studies not published in English
were excluded. It was not possible to obtain copies of two tools,
despite contacting the institutions where they were developed, so
these tools were excluded from the review.30,31 A full list of
inclusion and exclusion criteria is available in the Supplementary
Information (Appendix 1).
Data extraction
The data extraction form was developed by all authors and piloted
with four studies. One author (CE) extracted data from all studies.
Data from 25% of studies were also extracted by an independent
reviewer for cross-checking.
Study characteristics
The following data were extracted: study design, study setting,
sample size, population and country. Age, gender, socio-economic
status, education, disease state and ethnicity may all impact on
the results of a relative validation study.32 As such, the sample
profiles were categorised.
Questionnaire characteristics
Data were collected on the number of items, type of questions,
scoring system and the language of the tool, the method of
administration and whether the tool was designed for a specific
population or for use in a particular setting.
Questionnaire items
Data were extracted on item generation as it is important to know
whether a questionnaire has been tailored to the population of
interest.4 Data were extracted on whether a questionnaire had
been tested for acceptability (face validity, ease of use or an
assessment of usefulness) and readability.
Reliability and relative validity
Results were extracted from test-retest reliability studies determining whether tools were consistent over two or more
administrations,33 and from internal reliability studies determining
whether items measuring the same dietary characteristic were
consistent within a tool.34 Data from relative validity studies were
extracted. In true validation studies, a new measure is compared
with an accurate measurement of the truth, but this is very
difficult for habitual diet.35 The gold standard for dietary intake is a
recovery biomarker such as doubly labelled water, for energy
intake, or urinary nitrogen for protein.36 These are expensive to
administer, only available for a limited number of nutrients and
inappropriate for brief questionnaires that do not measure the
whole diet. Even direct observation is unsuitable as a true measure
of habitual diet in free living individuals owing to the need for
24-h, possibly covert, surveillance. Consequently, short dietary
assessment tools are evaluated against imperfect reference
© 2015 Macmillan Publishers Limited
A systematic review of brief dietary questionnaires
CY England et al
3
2565 records identified through
database searching
7 additional records identified
through hand searching references
1802 records after duplicates removed
122 full-text articles
assessed for eligibility
1680 records excluded
75 full-text articles excluded:
• Questionnaire is too long = 25
• Unable to score in clinical practice = 19
• Not describing a brief dietary questionnaire = 10
• Intervention / observational study = 7
• Measures attitudes / psychological aspects = 4
• Measures group intakes only = 2
• Not tested for reliability or validity = 1
• Questionnaire is unobtainable = 2
• Malnutrition screening = 1
• Ineligible participants (bowel cancer) = 1
• Conference abstract/letter = 2
• Paper promotes an included questionnaire = 1
47 studies included in
review (describing 35
tools)
Figure 1.
Prisma diagram. Brief dietary questionnaires.
measures. These include self-reported dietary measures, for
example, food diaries, a longer FFQ or 24-h recalls; a concentration
biomarker such as plasma vitamin levels,37 or biomarkers of preclinical disease38 such as blood lipids or anthropometric measures.
None of these are true measures of habitual intake. Dietary
measures are subject to measurement error, which vary depending upon the method. For example, those reliant on memory, such
as FFQs, are subject to recall bias, whereas food records can
change dietary behaviour.4 The use of food tables for nutrient
analysis further introduces error in both self-report and direct
observation of diet.35 Furthermore, if errors in the reference
measure correlate with errors in the new measure, for example, if
both methods are subject to recall bias, relative validity of the new
measure could be overestimated.35 Concentration biomarkers and
biomarkers of pre-clinical disease are affected by metabolic and
lifestyle factors. For example, levels of plasma β-carotene are not
only determined by dietary intake but also by fat intake, body
mass index (BMI), low-density lipoprotein levels and smoking.37
However, these biomarkers can provide additional evidence of
accuracy of a questionnaire when used in conjunction with other
reference measures.
Internal reliability is typically tested using Cronbach’s α, which
assesses how closely items correlate with each other.34 Values of
40.70 indicate high internal reliability, although strong correlation between items in a dietary questionnaire may not be required
if each item is designed to assess different aspects of the diet.39
Test-retest reliability and relative validity are commonly tested at
the individual level using correlation statistics.35 The use of mean
values alone can only assess these at the group level.40 Correlation
coefficients of ⩾ 0.4 for the nutrient of interest are considered to
be adequate for FFQs when compared with another dietary
reference measure.4 Correlations of ⩽ 0.4 are more usual when
FFQs are compared with a biomarker.37 Studies calibrating long
FFQs against other dietary assessment methods, such as food
diaries, have reported coefficients between − 0.16 and 0.86 for
total fat in grams (mean 0.51), − 0.01 and 0.71 for fruit and 0.16
© 2015 Macmillan Publishers Limited
and 0.72 for vegetables.41 Test-retest reliability studies for long
FFQs quote coefficients of 0.50–0.70 for energy, fat and selected
micronutrients.41
The practice of only examining the correlations between scores
to determine test-retest reliability or validity has been criticised,
and it has been recommended that the Bland Altman method be
used in conjunction.33 Details of the statistical tests used were
summarised.
RESULTS
A total of 1802 separate records were identified, 1795 via the
electronic databases and a further 7 from hand searching references.
One hundred and twenty-two full text papers were screened and 47
met the inclusion criteria (Figure 1). The development and testing of
35 tools were described in these papers, although 2, the Block Fat,
Fruit and Vegetable Screeners (B-F&FV)6 and the Hispanic Fat, Fruit
and Vegetable Screeners (H-F&FV),42 can be split into two distinct
sets of questions that provide scores for different aspects of the diet.
In addition, two different versions of two tools, the Rapid Eating
Assessment for Patients (REAP29 and REAP-S14) and the Food
Behaviour Checklist (FBC-T10 and FBC-V43), are currently available,
and the FBC-V has been translated into Spanish (FBC-SV) and
evaluated32,44 One, the Fat Related Diet Habits Questionnaire
(FRDHQ), appears to have been used in several different versions.
Papers describing the relative validity testing of the 20-item and
24-item questionnaires are detailed here21,45–47 although 21-48
and 23-item49 versions have been used in interventions. The
current version, available online, contains 25 distinct items (http://
sharedresources.fhcrc.org/documents/fat-related-questionnaire).
For the purposes of this review, B-F&FV and H-F&FV were regarded
as single tools, REAP and REAP-S and FBC-T and FBC-V were
regarded as distinct tools, with FBC-SV as a subsidiary to FBC-V. All
the versions of FRDHQ were regarded as one tool.
Table 1 summarises the study and tool characteristics. Over half
(n = 20) were developed and tested in the USA or Canada with the
European Journal of Clinical Nutrition (2015) 1 – 27
Ethnicity
European Journal of Clinical Nutrition (2015) 1 – 27
1) i) Acceptability
(n = 43)
ii) Acceptability
(n = 15)
2) Acceptability
(n = 20)
3) i) Test–retest
(n = 71)
ii) Validation
(n = 82)
iii) Internal
reliability
(n = 153)
1) i) Acceptability 1) Primary care
(n = 48)
2) Primary care
ii) Validation
clinic staff
(n = 261)
2) Validation;
internal reliability
(n = 60)
Food Behaviour Checklist,
visually enhanced version
(FBC-V)
1) Townsend et al.43
USA
Spanish translation of FBC-V
(FBC-SV)
2) Banna et al.32
USA
3) Banna and Townsend44
USA
Healthy Eating Vital Signs
(HEVS)
1) Greenwood et al.8
USA
2) Greenwood et al.53
USA
2) General
community
3) General
community
1) i) General
community
ii) University;
work site
1) General
community
2) General
community
1) Item
generation;
internal
reliability;
validation
(n = 100)
2) Item
generation; test–
retest; internal
reliability (n = 44)
Food Behaviour Checklist
(FBC) Text version (FBC-T)
1) Murphy et al.10
USA
2) Townsend et al.52
USA
1) General
community
2) General
community
7 subscale scores are calculated (‘Fruit and
vegetables’, ‘Milk’, ‘fat and cholesterol’, ‘diet quality’
and ‘food security’) by summing responses in that
category and dividing by the number of questions
Higher scores indicate more desirable habits
1) 100% women; age = 32.9 (8.9); mean
of 12 years education; low SES
2) Test–retest sample was a subset of
validation sample; internal reliability
redone
1) i) 79% White American
ii) 80% White American
2) 54% White American;
25% Hispanic
2) 95% Hispanic
3) Hispanic
1) i) 55.4% women); age = 42.6 (12.1)
ii) 58.2% women; age = 38.4 (11.7);
BMI = 27.7 (7.2); mean number of years
of schooling = 15.7 (3.4)
2) 93.3% women; age = 38.3 (9.6); 68%
BMI425; 100%4high school education
2) n = 20 (100% women) (face validity)
3) i) 100% women; low income
ii) 100% women; age = 36; BMI = 31.1
(6.7); low income
iii) Validation and reliability sample
combined for internal reliability
English
Individual answers are considered separately and
14 questions (food
no scores are calculated
frequency and behavioural)
Soft drink portions
described as cans
1 min to complete
Both 1 day recall (yesterday)
and typical recall with no
timescale
Spanish (USA) translation of As FBC-T
FBC-V
As FBC-T
English
Includes photographs
16 questions (food
frequency and behavioural)
Portions are not described
No completion time
estimated
Unspecified timescale
English
16 questions (food
frequency and behavioural)
Portions are not described
10 min to complete
Unspecified timescale
1 total score derived by summing responses
English
Higher scores indicate more desirable habits
25 questions (food
frequency and behavioural) Cut-offs used to define diets as healthy/unhealthy
Portions are not described
15 min to complete
Diet over last month
1) i) 54.7% women; age = 73.0 (5.0); 80%
high school education
ii) ‘similar’ gender distribution to other
samples
2) 59.7% women; age = 78.5 (4.0); 82.0%
high school education
1 total score derived by summing responses
Higher scores indicate more desirable habits
Cut-offs used to define diets as healthy/unhealthy
5 subscale scores (‘F+V’ ‘saturated fat’ and total fat’,
‘omega 3 s’ ‘fibre’ and ‘salt’) can also be calculated
by summing appropriate responses
Test scoring and outcome
English
13 questions (food
frequency and behavioural)
Portions described by
household measures
11 min to complete
Unspecified timescale
Tool characteristics
(language, number of
questions, portion estimates,
time to complete, timescale)
i) 13.5% women; age = 61.2 (10.8);
BMI = 28.7 (4.1)
ii) NR
Sample characteristics
(age (years) and BMI (kg/m2) reported as
mean values (standard deviation, when
available) unless otherwise indicated)
1) i) ‘English speaking, non- 1) i) 84% women; low SES
ii) No details
Hispanic black, nonHispanic white, and
Hispanic clients’
ii) Academic nutrition staff
(‘professionals’) (n = 6);
Nutrition educators
(‘paraprofessionals’)
(n = 10)
1) 46% African American;
23% Hispanic; 21% White
American; 3% Native
American; 7% other
2) NR
1) 99% White American
2) 98% White American
i) Cardiac rehab NR
NR
patients
ii) Health
professionals
(n = 25), cardiac
rehab patients
(n = 8)
1) i) Item
generation;
validation
(n = 179)
ii) Acceptability
(n = 17)
2) Validation
(n = 206)
i) Validationb
(n = 37)
ii) Acceptabilityc
(n = 33)
Healthy eating
Australian Diet Quality Tool
(DQT)
O'Reilly and McCann50
Australia
Recruitment
setting
Bailey Elderly Food Screener
(B-Elder)
1) Bailey et al.51
USA
2) Bailey et al.28
USA
Purpose of studya
(number of
participants)
Tool and study characteristics
Tool name
Author(ref)
Country
Table 1.
A systematic review of brief dietary questionnaires
CY England et al
4
© 2015 Macmillan Publishers Limited
© 2015 Macmillan Publishers Limited
Test–retest;
validation;
acceptability
(n = 160)
i) Acceptability
(n = 61)
ii) Validation
(n = 44)
iii) Acceptability
(n = 31)
iv) Test–retest;
validation
(n = 94)
Validation
(n = 49)
PrimeScreen
Rifas-Shirman et al.55
USA
Rapid Eating Assessment for
Patients (REAP)
Gans et al.29
USA
Rapid Eating Assessment for
Patients short form (REAP-S)
Segal-Isaacson et al.14
USA
Mediterranean diet
Brief Mediterranean Diet
Screenerd (bMDSC)
Schroder et al.13
Spain
Validation
(n = 102)
Item generation;
internal
reliability;
validation
(n = 252)
Latino Dietary Behaviors
Questionnaire (LDBQ)d
Fernandez et al.54
USA
Short Diet Quality Screener
(sDQS)
Schroder et al.13
Spain
Purpose of studya
(number of
participants)
(Continued )
Tool name
Author(ref)
Country
Table. 1.
NR
Spanish (European)
15 questions (food
frequency)
Portions described in
household measures
No completion time
estimated
Diet over last year
n = 102 (49% women); age = 58.6 (12.1); Spanish (European)
BMI = 27.6 (4.2); 62.7%4primary school 18 questions (food
frequency)
education
Portions described in
household measures
No completion time
estimated
Diet over last year
Developed in the same population as the sDQS
General
community
English
16 questions (behavioural)
Portions described by
weight
No completion time
estimated
Unspecified timescale
1 total score derived by summing responses
Higher scores indicate greater adherence to
Mediterranean diet
1 total score derived by summing responses
Higher scores indicate greater adherence to
healthy eating
As REAP
In clinic individual answers are considered
separately and a ‘physician key’ is provided to
guide discussion and advice
English
31 questions (behavioural)
Portions described by
weight
10 min to complete
Unspecified timescale
i) NR
ii) NR
iii) 62% female; age = 32 (range, 20–60);
96% some college education; 76%
household income o $76 000
iv) 57.4% women; age = 43.2 (12.5); 57%
completed high school; median income
range $51000-$60 000
i) NR
ii) NR
iii) 50% ‘people of colour’
iv) 94% White American
In clinic individual answers are coded using traffic
light codes (red, yellow, green) for food items
(It is possible to calculate nutrient intakes for
research purposes but this requires population
specific nutrient and food consumption databases)
English
15 food-based questions
(food frequency and
behavioural)+8 questions
on vitamin/mineral
supplements
Portions are not described
5 min to complete
Diet over last year
1 total score derived by summing responses
Spanish (USA and Central
Higher scores indicate more desirable habits
America)
13 questions (food
frequency and behavioural)
Portions are not described
No completion time
estimated
Unspecified timescale
76.6% women; age = 55.2 (11.2);
BMI = 34.8 (7.0); about 75% ohigh
school education; 50% household
income o $10 000
Test scoring and outcome
Tool characteristics
(language, number of
questions, portion estimates,
time to complete, timescale)
Sample characteristics
(age (years) and BMI (kg/m2) reported as
mean values (standard deviation, when
available) unless otherwise indicated)
63% White American, 31% 56.9% women; age = 48.0 (range, 19–65);
African American
BMI = 27.3 (range, 15.5–57.7); 59%
college graduates; 59% executive or
professional
100% Hispanic
Ethnicity
Undergraduates 65% White American, 21% 44.5% women; age = 24.2 (3.8);
Asian
BMI = 23.4 (5.0); some college
i) Physicians and
medical
students
ii)
Undergraduates
iii) Work site;
students
iv) General
community
Primary care
Primary care
Recruitment
setting
A systematic review of brief dietary questionnaires
CY England et al
5
European Journal of Clinical Nutrition (2015) 1 – 27
European Journal of Clinical Nutrition (2015) 1 – 27
1) Test–retest;
internal
reliability;
validation
(n = 97)
2) Internal
reliability;
validation
(n = 354)
3) i) Validation
(n = 1022)
ii) Validation
(n = 105)
iii) Test–retest
(n = 89/39)
4) i) Internal
reliability
(n = 178)
ii) Test–retest
(n = 42)
iii) Validation
(n = 32)
Short Fat Questionnaire (SFQ) i) Acceptability
Dobson et al.56
ii) Validation
(n = 90)
Australia
iii) Test–retest
(n = 25)
Fat-Related Diet Habits
Questionnaire/Kristal’s Food
Habits Questionnaire (FRDHQ)
20 item version
1) Kristal et al.11
USA
2) Birkett and Boulet46
Canada
3) Glasgow et al.21
USA
24-item version
4) Spoon et al.47
USA
i) Test–retest
(n = 639)
ii) Validation
(n = 52)
Validation
(n = 7146)
Mediterranean Diet
Adherence Scored (MEDAS)
Schroder et al.56
Spain
Total fat
Dutch fat consumption
questionnaired (D-Fat1)
Van Assema et al.57
The Netherlands
Purpose of studya
(number of
participants)
(Continued )
Tool name
Author(ref)
Country
Table. 1.
NR
ii) General
community;
work site
1) Primary care
2) Work site
3) i) Work site
ii) Primary care
4) Work site
General
community
Primary care
Recruitment
setting
NR
NR
NR
82% White American
NR
1)
2)
3)
4)
NR
NR
Ethnicity
English
20/25 questions
(behavioural)
Interview administered and
self- administered versions
of both older 20 item and
newer 25 item available
Portions are not described
No completion time
estimated
Diet over last month
1 total score derived by summing responses
5 behavioural subscales can be calculated by
summing responses in that category and dividing
by the number of questions
Lower scores indicate lower fat habits
1 total score derived by summing responses
Dutch
Lower scores indicate lower fat diet
25 questions (food
frequency and behavioural)
Portions described in
household measures
No completion time
estimated
Diet over last 6 months
1 total score derived by summing responses
English
i) NR
Lower scores indicate lower fat diet
17 questions (food
ii) NR
iii) Test–retest sample is a subset of the frequency and behavioural)
Portions are not described
validation sample
3 min to complete
Unspecified timescale
1) 100% women; age = 51.5 (4.3);
BMI = 24.5 (3.5); 60.0% completed
college; 57.2% household income
4$40 000
2) 100% men; age = 41.0 (9.8); BMI = 28.5
(4.4); 100% manual workers; mean
number of years education = 12.4 (3.3);
56.9% household income4CA$40 000
3) i) ‘majority blue collar’
ii) 60% women; age = 63
iii) Test–retest samples were subsets of
validation sample
4) i) 40.0% women; age = 40.7 (10.6);
BMI = 27.1 (27.1); 24% completed
college; 29% o $12 000
ii) Test–retest sample was a subset of
internal reliability sample
iii) Validation was a different subset of
internal reliability sample
i) 52.1%; age range = 18–93
ii) 55.8% women; age range = 21–68
1 total score derived by summing responses
Spanish (European)
Higher scores indicate greater adherence to
14 questions (food
frequency and behavioural) Mediterranean diet
Portions described in
household measures
No completion time
estimated
Unspecified timescale
57.2% women; age = 67; BMI = 30
Test scoring and outcome
Tool characteristics
(language, number of
questions, portion estimates,
time to complete, timescale)
Sample characteristics
(age (years) and BMI (kg/m2) reported as
mean values (standard deviation, when
available) unless otherwise indicated)
A systematic review of brief dietary questionnaires
CY England et al
6
© 2015 Macmillan Publishers Limited
© 2015 Macmillan Publishers Limited
French
11 questions (food
frequency)
Portions described by
weight
5 min to complete
Unspecified timescale
i) 45% women; age = 60.9 (15.5);
BMI = 26.9 (6.5)
ii) NR
iii) 39.7% women; age = 58.0 (16.0);
BMI = 27.0 (8.0)
iv) 56.9% women; age = 56.0 (12.0)
NR
i) Acceptability
(n = 131)
ii) Test–retest
(n = 20)
iii) Validation
(n = 58)
iv) Internal
reliability
(n = 1048)
NLSChol Questionnaired
Beliard et al.58
France
Primary care
English
20 questions (food
frequency)
Portions described by
weight and people are
asked to indicate ‘small’,
‘medium’ or ‘large’ servings
No completion time
estimated
Unspecified timescale
1) i) NR
ii) NR
iii) NR
2) 20.1% women; age = 42.0 (2.0);
BMI = 27.0 (4.0)78.4% college educated
3) n = 184 (100% women); age = 36.7
(5.3); BMI = 30.7 (6.9)
4) 65.9% women; 96.4% high school or
greater
1) NR
2) 65.9% White American
3) 100% African American
4) 64.4% White American;
24% Hispanic
1) i) Primary
care
ii) Primary care
iii) Pre-existing
food diaries
2) Armed forces
3) Primary care
4) Primary care
1) i) Validation
(n = 22)
ii) Validation
(n = 26)
iii) Validation
(n = 16)
2) Validation
(n = 164)
3) Validation
(n = 184)
4) Validation
(n = 501)
MEDFICTS (Meats, Eggs, Dairy,
Fried foods, fat In baked
goods, Convenience foods,
fats added at the Table and
Snacks)
1) Kris-Etherton et al.12
2) Taylor et al.62
3) Teal et al.63
4) Mochari and Gao64
USA
1 total score derived by summing responses
Higher scores indicate lower fat diet
1 total score derived by summing responses
Lower scores indicate greater adherence to diet
Cut-offs used to define diets as adherent/nonadherent
1 total score derived by summing responses
Lower scores indicate greater adherence to diet
Cut-offs used to define diets as adherent/nonadherent
English
1 total score derived by summing responses
10 questions (food
Lower scores indicate lower fat diets
frequency and behavioural)
Portions described by
weight
No completion time
estimate
Unspecified timescale
i) 100% men
ii)100% men
NR
English
20 questions (food
frequency)
Portions described as
undefined ‘servings’
6 min to complete
Unspecified timescale
Work site
51% White American; 48% i) 100% women; age = 51.0 (0.7);
African American
BMI = 38 (0.4); 65% low SES; 60% high
school education or less
ii) Test–retest sample was a subset of the
validation sample
1 total score derived by summing responses
English
8 questions (food frequency Lower scores indicate more desirable habits
and behavioural)
Portions are not described
No completion time
estimate
Diet over the ‘past few
months’
i) 49.7% women; age = 58.4 (9.2);
BMI = 34.8 (6.5); 19.1% high school or
less; 47.3% household income
o $49 999
ii) Test–retest was a subsample of
validation study
Test scoring and outcome
1 total score derived by summing responses then
English
dividing by the number of non-missing questions
30 questions (food
Lower scores indicate lower fat habits
frequency)
Portions are not described
No completion time
estimate
Diet over the last 3 months
Tool characteristics
(language, number of
questions, portion estimates,
time to complete, timescale)
100% women (49 participants
completed Sister Talk at phase 1 and 2
but test–retest not calculated)
Sample characteristics
(age (years) and BMI (kg/m2) reported as
mean values (standard deviation, when
available) unless otherwise indicated)
i) Test–retest
(n = 22)
ii) Validation
(n = 68)
Primary care
NR
100% African American
Ethnicity
Heart Disease Prevention
Project Screener (HDPPS)
Heller et al.61
UK
Specific dietary fats and/or dietary cholesterol
Dietary Fat Quality
i) Validation
Assessment (DFA)
(n = 120)
60
Kraschnewski et al.
ii) Test–retest
USA
(n = 96)
Primary care
i) Validation;
internal reliability
(n = 372)
ii) Test–retest
(n = 114)
Starting the Conversation
(STC)
Paxton et al.39
USA
Primary care
Internal
reliability;
validation
(n = 95)
Sister Talk Food Habits (short
form) (SisterTalk-S)
Anderson et al.3
USA
Recruitment
setting
Purpose of studya
(number of
participants)
(Continued )
Tool name
Author(ref)
Country
Table. 1.
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CY England et al
7
European Journal of Clinical Nutrition (2015) 1 – 27
European Journal of Clinical Nutrition (2015) 1 – 27
Fat and Fibre Diet Behaviour
Questionnaire (FFDBQ)
Shannon et al.18
USA
i) Item
generation
(n ⩾ 200)
ii) Validation
(n = 1795)
iii) Test–retest
(n = 943)
Fat and Fibre Barometer (FFB) i) Test–retest
Wright and Scott9
(n = 115)
Australia
ii) Validation
(n = 98)
Validation
(n = 206)
i) ‘Convenience
samples’
ii) Primary care
General
community/
work site
Work site
i) NR
ii) 93% White American
NR
‘majority white’
Total and saturated fat and free sugar
Dietary Fat and Free Sugar
Undergraduates 75% Australian
i) Validation;
Short Questionnaire (DFFQA) internal reliability
69
Francis and Stevenson
(n = 40)
ii) Test–retest
Australia
(n = 29)
Dietary fats and fibre
Dietary Instrument for
Nutrition Education (DINE)
Roe et al.5
UK
English
12 questions (food
frequency and behavioural)
Portions described by
weight
3 min to complete
Diet over the last month
37.4% women; age = 42.3; BMI = 27.7;
95.5% high school or greater
i) 1 focus group and 2 convenience
samples of approximately 100 each;
ii) 68.0% women; age = 51.0; 50%
college educated
iii) Test–retest sample was a subset of
validation sample
i) 47.8% women; higher than average
education
ii) Validation sample was a subset of
test–retest sample; 52.0% women
38% women; age = 44.8 (range, 17–62);
BMI = 25.7; 66% skilled manual workers
i) 60% women; age = 21.3 (5.8);
BMI = 23.4 (3.4); 100%4high school
education
ii) Test–retest sample was a subset of
validation sample: 62% women
1 total score derived by summing responses
Lower scores indicate lower fat diet
Cut-offs used to define diets as high/low in fat and
cholesterol
Test scoring and outcome
English
29 questions (behavioural)
Portions are not described
No completion time
estimated
Diet over the last 3 months
English
20 questions (food
frequency and behavioural)
Portions described by
household measures or
undescribed
10 min to complete
Unspecified timescale
English
29 questions (food
frequency)
Portions described by
household measures,
volumes and undefined
‘servings’
5–10 min to complete
Unspecified timescale
English
26 questions (food
frequency)
Portions are not described
5 min to complete
Unspecified timescale
Total fat score derived by summing relevant
responses
5 fat subscale scores can also be calculated
Lower scores for fat indicate lower fat diet
Total fibre score derived by summing relevant
responses
3 fibre subscale scores can also be calculated
Higher scores for fibre indicate higher fibre diet
1 total score derived by summing responses
Higher scores indicate lower fat/higher fibre diet
People are encouraged to consider changes in
questions where they scored 3 or less
3 subscale scores (‘total fat’, ‘total fibre’,
‘unsaturated fat’) are derived by summing relevant
items
Lower scores for fat and unsaturated fat indicate
low fat diet
Higher scores for fibre indicate high fibre diet
Cut-offs used to identify diets as high/low in fat/
fibre
1 total score derived by summing responses
3 subscales can be calculated by summing
responses for that category
Lower scores indicate lower sugar/fat
Suggested cut-off used to define diets as high/low
in undesirable foods
1 total score derived by summing responses
English
Higher scores indicate healthier choices
23 questions (food
frequency and behavioural) Cut-offs used to define diets as healthy/unhealthy
Portions described by
weight
No completion time
estimated
Unspecified timescale
Tool characteristics
(language, number of
questions, portion estimates,
time to complete, timescale)
Sample characteristics
(age (years) and BMI (kg/m2) reported as
mean values (standard deviation, when
available) unless otherwise indicated)
23.5% Portuguese heritage 57.8% women; age = 38.1 (13.1);
BMI = 26.5 (5.9); 83.3% completed high
school
Primary care
Rate Your Plate (RYP)
Gans et al.15
USA
Validation
(n = 102)
90% White American
Work site
Purpose of studya
(number of
participants)
Test–retest;
Northwest Lipid Research
Clinic Fat Intake Score (NWFIS) validation
20
(n = 310)
Retzlaff et al.
USA
Tool name
Author(ref)
Country
Ethnicity
(Continued )
Recruitment
setting
Table. 1.
A systematic review of brief dietary questionnaires
CY England et al
8
© 2015 Macmillan Publishers Limited
© 2015 Macmillan Publishers Limited
i) Test–retest;
acceptability
(n = 111)
ii) Validation
(n = 101)
Norwegian SmartDiet
Questionnaired (N-Smart)
Svilaas et al.7
Norway
Dutch
8 questions (food
frequency)
Portions described by
household measures
2 min completion time
Diet over the last month
English
7 questions (food
frequency)
Portions are not described
No completion time
estimated
Diet over the last month
i) 100% women; age = 41.0 (range,
29–40); BMI = 24.0 (range, 18.7–35.9);
95% intermediate to high education
level
ii) Test–retest sample was a subset of the
validation sample
1) 53.0% women; aged 450
2) 56.9% women; age = 42.0 (range,
20–67); 55% had 16 or more years of
education
1) NR
2) 89% White American
1) General
community
2) Work site
1) Validation
(n = 436)
2) Validation
(n = 260)
Five a day screener (5-F&V)
1) Thompson et al.17
2) Kristal et al.67
USA
Estimated number of fruit and vegetables/day
calculated by summing responses
Estimated number of fruit and vegetables/day
calculated by summing responses and dividing by
7
French
Estimated number of fruit and vegetables/day
6 questions (food
calculated by summing servings per week and
frequency)
dividing by 7
Portions described as cups/
volumes
No completion time
estimated
Diet over the last week
Fat screener
1 total score derived by summing responses
Lower scores indicate lower fat diets
Cut-offs used to identify diets as high/low fat
Fruit and vegetable screener
Estimated number of fruit and vegetables/day
calculated by summing responses and dividing by
7
Fat and fruit and vegetable subscale scores derived
by summing relevant items
Lower scores for fat indicate lower fat diet
Higher scores for fruit and vegetables indicate
higher consumption
Cut-offs used to identify diets as high/low in fat/
fruit and vegetables
56.2% women; age = 37.2 (11.5);
BMI = 27.7 (5.6)
Spanish (USA and Central
America)
16 questions on fat (food
frequency)
7 questions on fruit and
vegetables (food frequency)
Portions are not described
5 min to complete
Diet over the last months
NR
NR
i) NR
ii) 51.0% women; 38% aged o 30years;
42% o eighth grade education
ii) 58.0% women; age = 36.5 (14.5)
General
community
General
community
100% Hispanic (‘primarily’
Mexican and Mexican
Americans; 91% born in
Mexico)
English
17 questions on fat (food
frequency)
7 questions on fruit and
vegetables (food frequency)
Portions are not described
5 min to complete
Diet over the last year
1 total score derived by summing responses
Norwegian
Lower scores indicate less healthy choices
15 questions (food
frequency and behavioural) Cut-offs used to define diets as healthy/unhealthy
Portions described by
weight
9 min to complete
Unspecified timescale
i) 60.4% women; age = 51 (range, 28–52);
BMI = 26.5 (4.8)
ii) Validation study was a subsample of
the test–retest sample; 61.4% women
Test scoring and outcome
Tool characteristics
(language, number of
questions, portion estimates,
time to complete, timescale)
Sample characteristics
(age (years) and BMI (kg/m2) reported as
mean values (standard deviation, when
available) unless otherwise indicated)
65% White American; 22% 64.4% women; age = 41
Asian/Pacific Islander; 7%
Hispanic; 3% African
American
NR
Ethnicity
i) Validation
(n = 157)
ii) Test–retest
(n = 73)
Validation
(n = 350)
i) Item
General
generation
community
(n = 70)
ii) Acceptability
(n = ‘almost’ 300)
ii) Test–retest
(n = 93)
Work site
Primary care;
work site
Recruitment
setting
Dutch fruit and vegetable
questionnaired (D-F&V)
Bogers et al.19
The Netherlands
Fruit and/or vegetables
Canadian Fruit and Veg
Questionnaire (CFV-Q)d
Godin et al.66
Canada
Hispanic Fat and Fruit and
Vegetable Screeners (H-F&FV)
(These questionnaires can be
used separately)
Wakimoto et al.42
USA
Total fat and fruit and vegetables
Block Fat, Fruit and Vegetable Validation
Screeners
(n = 208)
(B-F&FV)
(These questionnaires can be
used separately)
Block et al.6
USA
Purpose of studya
(number of
participants)
(Continued )
Tool name
Author(ref)
Country
Table. 1.
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CY England et al
9
European Journal of Clinical Nutrition (2015) 1 – 27
A systematic review of brief dietary questionnaires
CY England et al
European Journal of Clinical Nutrition (2015) 1 – 27
Abbreviation: NR, not reported. aRoman numerals indicate where different samples or sub-samples were used during different phases of tool development. bValidation = calibration against a reference measure.
c
Acceptability encompasses face validity, clarity and ease of use. dTranslated into English.
Estimated number of fruit and vegetables/day
calculated by summing responses and dividing by
7
Dutch
10 questions (food
frequency)
Portions are described by
household measures
No completion time
estimated
Unspecified timescale
Short Dutch questionnaire to Validation
measure fruit and vegetablesd (n = 49)
(SD-F&V)
Van Assema et al.16
The Netherlands
General
community
NR
51.0% women; age = 45 (range, 21–68);
50% ‘low level of education’
Estimated number of servings of fruit per day
calculated by summing responses
English
5 questions (food
frequency)
Portions are not described
No completion time
estimated
Diet over the last month
50% women; age = 38.1 (8.1); 45%
o high school education; low SES
Validation
(n = 100)
Mainvil Fruit Habits
Questionnaire (M-FrHQ)
Mainvil et al.68
New Zealand
Unemployment
training
programme
80% European or Other
ethnicity; 11% Maori; 9%
Pacific, Asian, Middle
Eastern, Latin American,
African
Tool characteristics
(language, number of
questions, portion estimates,
time to complete, timescale)
Purpose of studya
(number of
participants)
Table. 1.
(Continued )
Tool name
Author(ref)
Country
Recruitment
setting
Ethnicity
Sample characteristics
(age (years) and BMI (kg/m2) reported as
mean values (standard deviation, when
available) unless otherwise indicated)
Test scoring and outcome
10
remainder in European countries (n = 10) and Australia or New
Zealand (n = 5).
Dietary assessment
Fifteen papers described 10 tools assessing healthy eating or
healthy dietary patterns8,10,13,14,28,29,32,43,44,50–55 and 2 assessing
adherence to the Mediterranean diet.13,56 Twenty-four papers
described 18 tools providing information on the intake of dietary
fats or dietary behaviours associated with fat intake. Of these, 11
were specific for dietary fats alone,3,12,15,20,21,39,45–47,57–64 1
assessed dietary fat and free sugars,65 4 assessed dietary fat and
fibre intakes5,7,9,18 and 2 assessed dietary fat and fruit and
vegetable intake (although these can be used separately as one
screener for fat and one for fruit and vegetables).6,42 Four tools
assessed fruit and vegetable intake16,17,19,66,67 and one assessed
fruit intake alone.68 With the exception of questionnaires specific
for fruit and vegetable intake, no tool was designed to
characterise diets by food groups, although three broader tools
also provided a fruit and vegetable sub-score.10,43,50
Fifteen tools were short FFQs and asked questions on the
frequency of consumption of specific foods.3,5,6,12,13,42,58,60,69
All of the fruit and vegetable questionnaires were in this
form.16,17,19,66,68 Four exclusively asked about food behaviours, for example, ‘In the past month how often did you eat
fish or chicken instead of red meat?’ or, ‘In an average
week, how often do you skip breakfast?’14,18,29,45 The remaining 16 contained a mixture of FFQ and behavioural
questions.7–10,15,20,28,39,44,50,54–57,59,61
All except six8,10,14,29,44,52,55 were scored numerically, with a
total score or subscales for separate nutrients or fruit and
vegetable intakes. The six that were not scored in this manner
give individual guidance for each item, and two14,29 also provide a
prompt sheet to aid advice.
Item generation
Item generation was described for 27 tools, with 8 employing
more than one method. Fourteen were adapted from longer FFQs
and other questionnaires,3,7,12,14,15,18,20,39,43,50,54,56,59,69 of which six
were initially based upon other tools included in this
review.14,15,18,20,43,54 Six used national databases to identify foods
most commonly consumed from a particular category, or foods
that contributed most to the nutrient of interest in the population
of interest.5,42,54,57,68,69 Seven used recommendations or clinical
guidelines5,10,29,53,55,56,58 and four were developed using an expert
panel.9,10,45,53 Five were developed from data collected from
participants, either quantitative in the form of dietary patterns51 or
through qualitative work.10,18,42,54
Fourteen reported being evaluated in some way for
acceptability to check that wording was clear, questions were
relevant and the general layout of the tool was appropriate.
Four employed cognitive interviewing,29,32,43,51,68 three used
survey methods,7,50,55 five used unspecified qualitative
interviews10,18,42,53,58 and two used unspecified pilot testing.20,59
Only the FBC-T and the visual versions derived from it were
evaluated for reading comprehension.32,43,52 The FBC-T and FBCSV were of low reading difficulty and the colour version of the
FBC-SV was ‘very easy’.
Reliability and relative validity
Table 2 summarises the results of reliability and relative
validity studies. Just over half the tools (n = 18) were tested for
test-retest reliability,7,9,18–20,29,39,42,44,52,55,57–61,69 with one being
tested in three different samples.21,45,47 Test-retest time varied
from several hours7 to 1 year18,19,57 and different studies
employed different statistical tests, although correlations were
most often used (14 tools).7,9,18–20,29,39,42,44,45,52,55,57,59 Test-retest
© 2015 Macmillan Publishers Limited
A systematic review of brief dietary questionnaires
CY England et al
correlation coefficients for total scores ranged from 0.5921 to 0.95.7
Four studies did not calculate a total score but used individual
items, group classifications or derived nutrient intakes from the
screener as test-retest variables.52,55,58,60 One study61 was
evaluated exclusively at the group level. Internal reliability was
tested in nine tools3,8,39,44,54,58,69 with two employing more than
one sample.10,45–47,52 Values for Cronbach’s α were reported from
0.47 54 to 0.83.47All tools were examined for relative validity at the
individual level against a reference measure except one.42
A number of different reference measures, with a range of
different times between tests, different test variables and different
statistical tests were used to determine relative validity. No study
employed a recovery biomarker. Nine tools were compared with
an FFQ that had previously reported relative validity against food
diaries or dietary recalls6,9,14,15,18,55,59,60,66 and 13 were compared
with food diaries,5,16,50,57,61 recalls13,17,44,54,67 or a diet history.58,68
One was compared with a different brief questionnaire that had
been previously tested for relative validity against 24 h recalls.39
Nine tools were compared with more than one reference
measure;8,10,12,20,21,28,29,45–47,52,53,56,62–64,69 and three were compared with more than one dietary reference.12,21,29,45–47,62–64
Alongside a dietary reference, four10,28,56,58 were compared with
biomarkers of preclinical disease, four28,53,56,69 with anthropometric measures, and two10,28 with concentration biomarkers. Two
did not use a dietary reference measure but compared change in
total score with change in BMI3 and change in total score with
change in plasma carotenoids and plasma vitamin C.19 The
variation in study designs makes direct comparisons between
tools problematic, but total score (or fat score) from 11
tools5,9,12,15,18,20,21,29,45–47,54,59,62–65 were reported to have been
compared with % energy from total fat from food diaries or FFQs.
Correlation coefficients ranged from 0.2446 to 0.79.12 Total scores
from two of these tools were compared with % energy from total
fat from a dietary reference in more than one population: the
FRDHQ reported correlation coefficients ranging from 0.2446 to 0.
6045 and MEDFICTS from 0.3063 to 0.79.12
Table 3 gives an ‘at a glance’ summary of the characteristics of
each tool, the evaluation studies and provides information on
access.
DISCUSSION
Main findings
This systematic review identified 35 tools with potential application to dietary assessment in clinical settings. Around half assess
dietary fat intake, with or without other nutrients, a third assess
the overall diet for healthy eating or adherence to the
Mediterranean diet, and the remainder assess fruit and vegetable
intake. More tools have been developed and evaluated in the USA
than in any other country.
Fewer than half the tools reported evaluations for clarity of
language and acceptability with users. Owing to the variation in
methodology, it is not possible to determine whether the tools
that were evaluated for acceptability show greater reliability or
relative validity than those that were not. However, best practice
in FFQ design involves pre-testing.41
All tools, except one, were tested for relative validity against
one or more reference measures, although there was a wide
variation in the design of studies, the variables used and the
statistical tests employed. Three quarters were tested against a
different dietary reference measure, with over a quarter using a
FFQ or a different brief questionnaire. As the majority of brief
questionnaires were themselves FFQs, or included many food
frequency questions, errors between the tools and the FFQs may
have been correlated and the relative validity of these questionnaires overestimated. Around half were evaluated for testretest reliability with similar variation in study design. This
© 2015 Macmillan Publishers Limited
11
variation makes direct comparison between tools difficult, and
as a consequence, it is not possible to state that one tool is
superior for a particular nutrient or population. However,
correlation coefficients for relative validity against food diaries
and biomarkers and those for reliability studies are similar to those
obtained in studies that evaluate longer FFQs against food diaries.
This indicates that these brief dietary screening tools can be
expected to produce a fair approximation of dietary habits and
consequently could be of use in clinical practice for the dietary
management of cardiovascular disease, obesity and type 2
diabetes. It is worth noting, however, that few tools reported
sensitivity, specificity or predictive values28 55,62–64,66,68 and only
six (17%) have assessed sensitivity to change over time;3,18–20,39,54
therefore, their utility in an intervention setting is unclear.
Strengths and limitations of the review
The strengths of this review are the application of a systematic
search strategy and systematic data extraction techniques. Dietary
assessment tools developed since the review by Calfas et al.22 in
2000 and validated tools that are not listed in the NIC registry
have been identified and described. Tools that were not included
in study reports were obtained online or from the original authors
to ensure they met the inclusion criteria. The results are presented
so that clinicians and researchers can select available tools that are
most suitable for their purposes.
The review has some important limitations. The piloting
and use of dietary screening tools in practice has not been
examined, which means it is not possible to determine whether
use of a tool has a positive effect on patient behaviour.
The inclusion and exclusion criteria were developed for this
review and assessment of whether a tool would be useful in
clinical practice was derived from the expert opinion of only two
clinicians. Other reviewers or clinicians may disagree with the
criteria and may have included or excluded different brief tools.
Calfas et al.22 judged that tools suitable for use in primary care
would take 15 min to complete or be around 50-items long but
provided no justification for this. The current review based an
estimate of completion time on preliminary data obtained from
brief dietary questionnaires. We excluded tools assessing
single food groups because there is limited clinical benefit in a
detailed assessment of one food group, with the exception
of fruit and vegetable intake. However, fruit and vegetable
questionnaires of greater than 10 items were excluded because
increased patient burden reduces feasibility in clinical practice.
Only peer-reviewed studies published in English were included.
There may be evaluated tools that are used in clinical practice in
other countries, or that have not been peer-reviewed that have
not been identified here. However, owing to the heterogeneity of
studies, this would be unlikely to change the broader conclusions
of this review.
Comparison with other studies
The review by Calfas et al.22 used wider inclusion criteria than this
current review and did not consider whether a tool could be easily
scored in practice. They identified 14 dietary assessment tools, of
which 7 are included in the present review.5,6,11,12,15,20,55 All
measured dietary fat, making comparisons between tools more
straightforward. Four were evaluated for test-retest reliability, with
correlation coefficients ranging from 0.67 to 0.91. The 11 validated
tools were either validated against a food diary or a longer FFQ,
and correlation coefficients for % energy from fat ranged from
0.30 to 0.80. These ranges are similar to coefficients reported in
the current review.
In 2003, Kim et al.70 reviewed tools reported as validated,
containing up to 16 items, and designed to assess fruit and
vegetable intake. They identified 10 instruments, of which 1 is
included in the current review.17 The remainder were excluded in
European Journal of Clinical Nutrition (2015) 1 – 27
European Journal of Clinical Nutrition (2015) 1 – 27
NR
NR
NR
Individual
items
NR
NR
Food Behaviour 1) NR
Checklist—text 2) 3 weeks
version (FBCT)10,52
Bailey Elderly
Food Screener
(B-Elder) 28,51
NR
Variables
NR
Retest time
NR
Spearman's
correlation
NR
NR
NR
Test
Test retest reliability
Summary of reliability and relative validity
Healthy eating
Australian Diet
Quality Tool
(DQT)50
Tool
Table 2.
NR
From r = 0.35,
P o0.05 (do you eat
more than one type
of fruit/day) to 0.83,
P o0.001 (do you
drink regular soft
drinks).
NR
NR
NR
Results
1) Subscale
ranges,
α = 0.28 (fat
and
cholesterol) to
0.79 (fruit and
vegetables)
(Spearman’s)
rho = 0.85
(food security)
2) Subscale
ranges, α =
0.61 (diet
quality) to
0.80 (F+V)
NR
NR
NR
Internal
reliability
Completed
at home
within the
same two
weeks
Time
between
tests
4–6 weeks
Same visit
As above
2) 4 × 24 h recall,
Anthropometrics,
Dietary index,
Concentration
biomarkers
1) 3 × 24 h recall,
Concentration
biomarkers
2) As above
1) 4 × 24 h recall, 4–6 weeks
Anthropometrics,
Concentration
biomarkers,
Biomarkers of
preclinical disease
4 day unweighed
food diary
Reference measure
Pearson's correlation
Pearson's correlation
Test
Spearman's correlation
Subscales and
individual items
from screener
Plasma
carotenoids; HEI
score, nutrients
and food groups
from recall
As above
3 categories of
risk from
screener—at risk,
possible risk, notat-risk; Nutrients,
MAR and
HEI-2005 from
recalls
Factor scores
(dietary
patterns), MAR
and nutrients
from recalls;
biomarkers
Total DQT score;
DQT subscales;
Nutrients from
food diaries
Variables
Validity
Total DQT score with %E sfa, r = − 0.50; fibre (g) r = 0.56;
omega-3(mg) r = 0.33 (P o0.05).
Fibre subscale with fibre (g), r = 0.42; fat subscale with
% E sfa, r = 0.49; omega-3 subscale with
omega-3 (mg), r = 0.37 (Po 0.05).
No correlations for TF, vitamin C or salt subscales.
At risk group reported significantly higher consumption
of TF, sfa, transF and lower consumption fibre and
protein. HEI and MAR were lowest in at risk group
(corrections made for multiple comparisons, P o 0.05)
Calculated sensitivity = 83%; specificity = 75%
accuracy = 79%; positive predictive value = 75%.
Pattern 2 correlations:
With nutrients: Sugar (g), r = 0.2; protein (g), r = − 0.26;
fibre (g),
r = − 0.20, P o0.05.With biomarkers: Serum B12 (mg),
r = − 0.19
●
●
●
Serum carotenoids, from r = 0.27 Po 0.05 (‘fruit and
vegetables as snacks’) to r = 0.48 P o 0.001 (‘do you
eat low-fat instead of high-fat foods’)
17 items did not correlate and were removed.
Individual items
Nutrient/food groups, from r = 0.20, P o0.05 (‘one kind
of fruit’ with vitamin C; FBC-T servings of fruit/vegetables
with HEI; ‘use nutrition labels’ with fibre’; ‘worry about
food running out’ with recall servings of fruit) to r = 0.41,
P o0.001 ('use nutrition labels' with vitamin A).
●
●
Subscales
%E TF, r = − 0.25, P o 0.01 (‘diet quality’)
HEI, from r = 0.20, P o0.05 (‘fat and cholesterol’, ‘food
security’) to from r = 0.32, P o0.001 (‘diet quality’).
● Serum carotenoids, r = 0.28, P o0.05 (‘fat and
cholesterol’) to r = 0.48, P o0.001 (‘diet quality’)
Multiple comparisons made with subscales/individual
items and nutrients, HEI score, food groups, serum
carotenoids
●
●
2)
●
●
Pattern 1 correlations:
With nutrients: MAR, r = 0.37; sfa (g), r = − 0.25; CHO (g),
r = 0.19; fibre (g) r = 0.45, P o0.001; protein (g)
r = 0.25; omega-3 s (g), r = 0.16; P o 0.05;TF (g), r = − 0.20
With biomarkers: HDL-C, r = 0.17; TGs, r = − 0.15;
WC r = − 0.18, P o 0.001
1) 2 patterns identified: pattern 1 = ‘prudent dietary score’;
pattern 2 = ‘Western dietary score’.
●
●
●
Results
A systematic review of brief dietary questionnaires
CY England et al
12
© 2015 Macmillan Publishers Limited
Retest time
© 2015 Macmillan Publishers Limited
Latino Dietary
Behaviors
Questionnaire
(LDBQ)54
Healthy Eating
Vital Signs
(HEVS)8,53
NR
NR
2) NR
NR
NR
Results
NR
NR
NR
NR
NR
NR
ICC, Spearman's Total score, r = 0.71,
correlation
P o0.001; Subscales
from r = 0.48 (food
security) to 0.78,
P o0.001 (dairy/
calcium)
Individual items,
from r = 0.35,
P o0.01 (more than
2 servings
vegetables at a
main meal) to 0.79,
P o0.0001 (rate
eating habits).
ICC total
scale = 0.75
Subscales from 0.26
(sweetened
beverages) to 0.80
(F+V).
Individual items
from 0.34 (servings
of fruit and more
than 2 servings
vegetables) to 0.81
(drink milk).
Test
Test retest reliability
Total score
individual
items
Variables
1) NR
Food Behaviour 3) 3 weeks
Checklist—
visual version,
Spanish
translation
(FBC-SV)44
Tool
Table. 2. (Continued )
1)
Anthropometrics
3) 3 × 24 h recall
Reference measure
Baseline total 3 × 24 h recall
score α =
0.47; 12 m α =
0.48. Healthy
dietary
change
subscale had
the strongest
baseline and
12 month
internal
consistency
(α = 0.60 and
α = 0.58)
2) Total score, Block Food
α = 0.49
Frequency
Questionnaire
NR
Total score,
α = 0.75
Subscales
from, α = 0.49
(diet quality)
to α = 0.80 (F
+V)
2 item
subscales r =
0.42 (dairy)
and r = 0.26
(sweetened
beverages)
(n = 154)
Internal
reliability
Unclear but
both
dietary
measures
collected at
baseline
and
12 months
1 week
Same visit
Unclear but
FBC
completed
at the same
time as
second or
third recall
Time
between
tests
Test
Total score,
subscale scores
and change
scores from
LDBQ;
Nutrients, proxy
'behavioural'
items and
change scores
from recalls
(baseline and
12 months)
Baseline clinical
measures
Individual items
from HEVS
Items and
nutrients from
FFQ
Subscales from
HEVS;
BMI
Correlation
Independent samples ttest
Pearson's correlation
Linear regression analysis
Spearman's correlation
Subscales and
individual items
from screener
Nutrients and
cups of F+V from
recalls
Variables
Validity
'Multiple items' on HEVS with FFQ items/nutrients, from
r = 0.30 (1- day and typical ‘eating fast food’ with
TF (g); 1- day and typical F+V questions with fibre (g),
P o0.05) to r = 0.5 (1-day ‘eating fast food’ with
transF (g), P o0.001).
●
●
●
●
Subscales, r = − 0.15 (‘artificial sweeteners’ with % E sfa)
to r = − 0.43, (‘healthy changes’ with %E TF), P o0.01.
Sensitivity to change: LDBQ showed greater change
over time in the intervention group (n = 67; 7.10 (5.53))
compared with control group (n = 75; 3.36 (5.12)), P o0.001.
12 months:
Total LDBQ score, from r = − 0.16, P o0.05(%E transF) to
r = 0.37, P o0.01 (%E protein)
Baseline:
Total LDBQ score with sodium (mg) r = − 0.25, and
energy (kcal) r = − 0.34, P o0.01 (no other correlations
with nutrients)
● Subscales, from r = 0.15, P o0.05 (‘fat consumption’
with %E transF) to − 0.39, P o0.01 (‘healthy dietary
change’ with energy, kcal)
● Clinical measures, r = − 0.16, P o 0.05 (HbA1c) to r = 0.24,
P o0.01 (diastolic blood pressure).
●
●
A 0.61 increase in BMI was associated with an additional
sugary drink in a typical day (0.17–1.04, P o0.001) but
no association in 1- day recall.
A 0.91 reduction in BMI was associated with a 1- day
increase in physical activity in a typical week (−1.39 to
− 0.44, P o0.001) with no association in 1- day recall.
●
Individual items
● Nutrients, from r = − 0.21, P o0.05 (‘red meat or pork
yesterday’ (higher score represents lower intake) with
Dchol, mg), to r = 0.43, P o 0.001 (‘drink milk’ with
vitamin D)
●
Behavioural subscales
‘dairy/calcium’, from r = 0.25, P o0.05 (vitamin A (RE))
to r = 0.43, P o0.001 (calcium (mg));
● ‘food security’ with USDA food security scale r = 0.42,
P o0.001;
● ‘diet quality’, from r = − 0.23, P o0.05 (MyPyramid
grains oz) to r = − 0.33, P o 0.01 (% E transF);
● ‘fast food’ (higher score represents lower intake) with
vitamin A and B-12, r = 0.23, P o0.05;
● ‘sweetened beverages’ (higher score represents lower
intake) from r = − 0.33 (% E TF) to r = − 0.41, P o0.001
(total sugar (g)).
● No correlation for F+V subscale
Multiple comparisons made with subscales/individual items
and nutrients/cups of F+V
Results
A systematic review of brief dietary questionnaires
CY England et al
European Journal of Clinical Nutrition (2015) 1 – 27
13
European Journal of Clinical Nutrition (2015) 1 – 27
NR
NR
NR
NR
Rapid Eating
Assessment for
Patients short
form
(REAP-S)14
Short Diet
Quality
Screener
(sDQS)13
Developed in
the same
population as
the Brief
Mediterranean
Diet Screener
(bMDSC)
Adherence to the Mediterranean diet
NR
NR
Brief
Mediterranean
Diet Screener
(bMDSC)13
Developed in
the same
population as
the Short Diet
Quality
Screener
(sDQS)
Total score;
individual
items
1 week
Rapid Eating
Assessment for
Patients
(REAP)29
Test
NR
NR
NR
Correlation
Nutrients and Spearman's
food group
correlation
consumption
derived from
PrimeScreen
Variables
Test retest reliability
2 weeks
Retest time
(Continued )
PrimeScreen55
Tool
Table. 2.
NR
NR
NR
NR
NR
NR
10 × 24 h recall
10 × 24 h recall
Block 1998 semi
quantitative food
frequency
questionnaire
Food diaries
(unknown),
Women's Health
Initiative FFQ
Total score, r = 0.86, NR
P o0.001; For
individual items
from r = 0.79 (type
of ice cream) to
0.33 (servings of
fruit and
vegetables)
(P o0.001)
Reference measure
Willet's SFFQ
Internal
reliability
For foods and food NR
groups r = 0.50
(other vegetables)
to 0.87 (adding salt)
(no P values); for
nutrients r = 0.59
(lutein/zeaxanthin)
to 0.86 (vitamin A
with supplements)
(no P values). No
difference across
demographic
strata.
Results
24 h recalls
41 year.
bMDSC
first, then
1 week
later sDQS
24 hrecalls
41 year.
(bMDSC
first, then
1 week
later sDQS)
Unclear
Sample 2:
1 week
(sample 1
not stated)
2-4 weeks
Time
between
tests
Pearson's correlation,
Spearman's correlation
Test
Total and
subscales
(ANTOX-S and
mMDS) from
bMDSC; ANTOXS and mMDS
scores derived
from recalls
Total score from
sDQS (DQI);
DQI from recalls
REAP Food
groups;
Food groups, TF,
fibre (g),Dchol
(mg) sugar (g)
from FFQ
Pearson's correlation, ICC
LOA
Mann Whitney U
Gross misclassification
Pearson's correlation,
ICC,
LOA
Mann Whitney U
Gross misclassification
Pearson's correlation
Correlation
Study 1: REAP
total score and
subscales
Total HEI and HEI
subscales from
diaries.
Study 2:
Modified REAP
subscales; foods/
nutrients from
FFQ
Nutrients and
food groups
calculated from
both
Primescreen and
FFQ
Plasma
carotenoids and
plasma vitamin
levels
Variables
Validity
Food groups, from r = 0.36 (‘other vegetables’) to 0.82
(‘whole eggs’).
Nutrients, from r = 0.43 (iron) to 0.74 (vitamin E with
supplements).
Plasma levels, PrimeScreen nutrients with vitamin E,
r = 0.33; beta-carotene, r = 0.43; lutein/zeaxanthin,
r = 0.43 (for the SFFQ these were 0.19, 0.43, 0.34
respectively).
Specificity for o3/day F+V = 67%; Sensitivity for 5/day
F+V = 73%. Sensitivity for 410% E sfa was 81% and
specificity 66%. (no P values given)
Study 1
Total scores, r = 0.49, P = 0.007
Subscales: from r = 0.31, P = 0.04 (variety subscales) to
r = 0.55, P o0.001 (fat subscales)
DQI, r = 0.61 (no P value)
sDQS DQI mean vs 24 h DQI mean = 39.3 (2.8) vs
35.5 (2.8), difference 3.82 (95% CI, 3.33, 4.31). LOA =
96–126%. ICC = 0.32.
48.5% participants classified in the same tertile, 3.9%
in the opposite tertile.
●
●
●
●
●
●
●
●
●
bMDSC mMDS with 24 h mMDS, r = 0.40
bMDSC ANTOX-S with 24 h ANTOX-S, r = 0.45 (no
P values)
bMDSC mMDS mean = 18.3 vs 24 h mMDS mean = 20.7,
difference = -2.44 (95% CI − 3.01, − 1.82). Mean
differences for the ANTOX-S was zero.
For the mMDS 44% participants classified in the same
tertile, 11% in the opposite tertile.
LOA = 61– 118%, ICC = 0.30.
For the ANTOX-S 50% in the same tertile with 9% in the
opposite tertile. LOA = 59–144%, ICC = 0.45.
Food groups, from r = − 0.38 (added fat servings) to
r = 0.51 (fruit servings), P o 0.001.
REAP-S food servings with FFQ nutrients, from r = − 0.20,
P = 0.034 (‘fish, poultry and meat servings’ with Dchol
(mg)) to 0.52, P o0.001 (‘vegetable servings’ with
fibre (g)).
●
●
Other subscales, from r = 0.30, P = 0.024 (fruit servings)
to r = − 0.62, P o0.001 (alcohol)
●
Study 2
● REAP energy subscale with energy (kcal), r = − 0.44,
P o 0.001
●
●
●
●
●
●
Results
A systematic review of brief dietary questionnaires
CY England et al
14
© 2015 Macmillan Publishers Limited
Retest time
(Continued )
© 2015 Macmillan Publishers Limited
NR
Fat Related Diet 2)
Habits
NR
Questionnaire/
Kristal’s Food
Total score;
tertiles
Total FRDHQ
score;
Behavioural
subscales
1 year
NR
Variables
NR
Correlation
Pearson's
correlation
Gross
misclassification
NR
Test
Test retest reliability
Fat Related Diet 1)
3 months
Habits
Questionnaire/
Kristal’s Food
Habits
Questionnaire –
20 items
(FRDHQ)11
Total Fat
Dutch fat
consumption
questionnaire
(D-Fat1)57
Mediterranean NR
Diet Adherence
Score
(MEDAS)56
Tool
Table. 2.
NR
Total FRDHQ score,
r = 0.87; for
subscales from r =
0.67 (replace high
fat foods with
naturally low fat
foods) to 0.90
(avoid fat as a
seasoning), (no P
values).
r = 0.71 (no P
value); 3.9% of
participants were
classified in
opposite fat
consumption
tertiles.
NR
Results
2)
Total FRDHQ
score, α =
0.73;
2)
Ontario Health
Survey Food
After the
1st food
diary was
returned
1)
8 day food diaries
(unknown),
Modified Block/
National Cancer
Institute FFQ
1)
Total FRDHQ
score, α =
0.62;
subscales
range α =
0.54 (replace
high fat foods
with naturally
low fat foods)
to 0.76 (avoid
meat)
Unclear—
baseline
Time
between
tests
7 day un-weighed 1 week—
food diary
1 month
Anthropometrics,
Biomarkers of
preclinical
disease,
PREDIMED FFQ
Reference measure
NR
NR
Internal
reliability
Adjusted correlation
Pearson's correlation
Unweighted kappa
statistics
Gross misclassification
Pearson's correlation
General linear modelling
ICC, LOA
Kappa statistics
Test
Total FRDHQ
Adjusted correlation
score and
subscales; % E TF
Total FRDHQ
score and
behavioural
subscales from
KFHQ;
%E TF from diet
records and FFQ;
Total Dutch fat
score
TF (g) from
diaries
PREDIMED score
and individual
items from
MEDAS
PREDIMED score
from FFQ,
individual items
and nutrient
intakes from FFQ;
anthropometrics
biomarkers
Variables
Validity
Total PREDIMED scores, r = 0.52, P o 0.001.
Absolute ICC = 0.52 for men, 0.51 for women, P o0.001.
Associations found for CVD risk factors and MEDAS for
BMI (β = − 0.146, P o0.001) and waist circumference
(β = − 0.562, P o 0.001) with smaller associations for
lipids and fasting blood glucose.
Total FRDHQ score with %E TF, r = − 0.60 P o 0.001
●
Linear relationship with %E TF and 'avoid fat as
seasoning', 'substitution of high fat foods with
manufactured low fat alternatives' and 'replace high fat
foods with naturally low fat foods'.
On 'avoiding meat' those scoring o2.0 (n = 55) had
a higher
%E TF than those with scores 42 (n = 40); on the 'modify
high fat food (trimming fat/skin from meat)' those scoring
4 (n = 59) had a lower %E TF than other groups (30.6%
vs approx 36%). (No statistical tests or P values are
described).
In a multiple regression model predicting %E TF from
all components summary R squared = 0.47.
● Total FRDHQ score with %E TF, r = − 0.24, P o0.001.
Subscales
● %E TF, r = − 0.12, P o 0.05 (‘substitute low-fat for high
fat’) to − 0.24, P o0.001 (‘avoid fat as a seasoning’).
●
●
●
Subscales
● %E TF, from r = − 0.29, P o 0.01 (‘avoid meat’) to r = 0.50,
Po 0.001, (‘avoid fat as seasoning’).
●
●
Dutch Fat score with TF (g), r = 0.59 (no P value)
Kappa = 0.42 with 2 categories and 0.25 with 3.
Gross misclassification = 15.4%
Kappa scores for individual items ranged from 0.03
(‘consuming sauces with tomatoes’) to 0.81 (‘wine’),
with a mean of 0.43 (moderate): 21.4% of items
showed poor agreement between screener and the
FFQ with 21.4% of items good or excellent.
47.9% of individuals were grouped into the same
PREDIMED tertile on MEDAS and FFQ; 8.6% grouped in
opposite tertiles.
●
●
●
●
Individual items
Associations between nutrients/foods on the FFQ and
PREDIMED quintiles as derived by MEDAS were in the
expected direction, except for vitamin E where there
was no association. For example the 1st quintile
consumed 155 g fruit vs 180 g for the 5th quintile
(P o0.001).
●
●
●
Results
A systematic review of brief dietary questionnaires
CY England et al
15
European Journal of Clinical Nutrition (2015) 1 – 27
European Journal of Clinical Nutrition (2015) 1 – 27
Total FRDHQ
score;
Behavioural
subscales
Total score
3) ii)
3 months
Fat Related Diet 4)
9 months
Habits
Questionnaire/
Kristal’s Food
Habits
Questionnaire –
24 items
(FRDHQ)47
NR
Variables
Pearson's
correlation
Correlation
NR
Test
Test retest reliability
3) i)
NR
Retest time
(Continued )
Habits
Questionnaire –
20 items
(FRDHQ)46,21
Tool
Table. 2.
Total FRDHQ score,
r = 0.74 P o 0.01
Subscales from, r =
0.48 (replace with
fruit) to 0.68 (avoid
fat), (P o0.01)
All participants
r = 0.59, P o0.01;
UC only r = 0.56,
P o0.01
NR
Results
Reference measure
Total FRDHQ 4 day un-weighed baseline to
9 months
score, α = 0.83 food diary
Subscales
from, α = 0.47
(replace fat)
to 0.76
(substitute
fat)
NR
2 years (BFHQ)
Same visit
(B-FHS)
Time
between
tests
and total energy
(kcal) from FFQ
Variables
Correlation
‘Responsiveness’ = mean
difference between UC
(n = 39) and intervention
(n = 50)
Correlation
Test
Correlation
Total FRDHQ
score and
subscales from
KFHQ; Energy
(kcal), %E TF and
TF (g) from
diaries(pre and
post
intervention)
Total score from
FRDHQ;
%E TF from
B-FFQ;
Total score from
B-FS
BMI; TChol
3) ii)
After return Total score from
4 day food diaries of food
FRDHQ;
diaries
Energy (kcal), % E
TF, kcal from
diary;
BMI, HbA1c (%),
TChol (mmol/l) at
baseline
Follow up scores,
adjusted for
baseline used to
calculate
responsiveness
to change
Frequency
subscales
Questionnaire
range,
α = 0.13
(replace high
fat foods) to
0.53 (make
modifications
to meat prep).
When used as
a behavioural
checklist α =
0.70.
(Item-scale
correlations
also tested)
NR
3) i)
Block/National
Cancer Institute
FFQ (B-FFQ)
Block fat screener
(B-FS)
Internal
reliability
Validity
score
score
score
score
score
with
with
with
with
with
energy (kcal), r = 0.27, P o 0.01
%E TF, r = 0.44, P o0.01
TChol, r = 0.19, P o0.05
HbA1c, r = 0.32, P o0.01
BMI, r = 0.22, P o 0.05
Intervention mean FRDHQ score = 1.97
FRDHQ responsiveness = 0.4
●
‘Replace with fruit’ subscale did not correlate with the
nutrient estimates.
Post-intervention:
● Total FRDHQ score with TF (g) r = 0.46, P o0.01. No other
correlation for total FRDHQ score.
Subscales
● r = 0.21 (‘modify meats’ with energy), P o 0.05–0.47
(‘substitute fat’ and TF (g)), P o0.01.
Pre-intervention
Total FRDHQ score with energy (kcal), r = 0.43, P o 0.05;
TF (g), r = 0.52 with % E TF, r = 0.47, P o0.01.
Subscales
● r = 0.35 (‘avoid fat’ and ‘substitute fat’ with energy) to
r = 0.43 (‘modify meats’ with TF (g)), P o0.05. No subscale
correlated with %E TF.
●
●
●
● Intervention mean FRDHQ score = 2.16
3 months
● UC mean FRDHQ score = 2.16
Baseline:
● UC mean FRDHQ score = 2.14
●
●
●
●
●
●
●
●
FRDHQ
FRDHQ
FRDHQ
FRDHQ
FRDHQ
Total FRDHQ score with %E TF r = 0.48, P o0.01
Total FRDHQ score with B-FS score, r = 0.61, P o0.01
Total FRDHQ score with BMI, r = 0.1, P o 0.01
No correlation with TChol
●
●
Total
Total
Total
Total
Total
No significant correlations for energy with total score or
subscales.
Confirmatory factor analysis showed discrepancy between
the hypothesised structure and the actual responses
(likelihood ratio (160) = 256.98, P = 0.001. Some items
were not related to hypothesised factor e.g., loadings
of less than 0.3 for replace high fat food subscale.
When the tool was used as a behavioural checklist
total score with %E TF, r = − 0.27 (P o 0.001).
●
●
Results
A systematic review of brief dietary questionnaires
CY England et al
16
© 2015 Macmillan Publishers Limited
© 2015 Macmillan Publishers Limited
Total score;
individual
items
NR
Pearson's
correlation
NR
Pearson's
correlation
Test
Heart Disease
Prevention
Project
Screener
(HDPPS)61
3–4 months Total score
(Pearson's)
Individual
items
correlated
with the
summary (r =
0.39–0.59,
P o0.05).
Total score r = 0.66,
P o 0.05
Individual items
from, r = 0.4 to r =
0.62 (no details),
P o 0.05.
ICC range = 0.48–
NR
0.59 for dietary fats
(no CIs given)
Total score,
α = 0.79
NR
r = 0.85 (95% CI,
0.69–0.93)
NR
Internal
reliability
Results
NR
Mean scores,
‘Only small
gross
differences
misclassification between occasions
(20 of the men had
a difference of 2
points or less) and
the mean scores for
the group were
identical on each
occasion’ (p365)
Specific dietary fats and/or dietary cholesterol
2–4 weeks Dietary fats
ICC
Dietary Fat
quantified
Quality
from DFQA
Assessment
servings
(DFA)60
4 months
Sister Talk Food NR
Habits (short
form)3
Starting the
Conversation
(STC)39
Variables
Test retest reliability
7–9 months Total score
Retest time
(Continued )
Short Fat
Questionnaire
(SFQ)56
Tool
Table. 2.
Unstated,
possibly
same visit
Time
between
tests
2–4 weeks
Both tools
completed
at the same
visit
Total DFQA;
Quantified fat
and cholesterol
intakes from
DFQA and FFQ
pufa:sfa from
FFQ
STC total score
and change in
STC total score
Pfat score and
reduction in TF
from Pfat
Change in BMI
Change in short
Sister Talk
Total score from
SFQ
%E TF, % E sfa;
pufa:sfa from
FFQ
Variables
3 day food diaries Around the Total screener
(unknown)
same time score;
Mean sfa (g)
from food diary
Fred Hutchinson
Cancer Research
Center FFQ
NCI Percentage
energy from fat
(Pfat) screener
Anthropometrics, Same time
91 item SisterTalk
FFQ
179-item CSIRO
FFQ
Reference measure
Correlation, Independent
samples t-test
Spearman's correlation
Gross misclassification
Pearson's correlation
Pearson's correlation
Bootstrapping,
Correlation
Misclassification
Test
Validity
Total score with %E TF, r = 0.55 (CI 0.39–0.68), %E sfa,
r = 0.67 (CI 0.54–0.77) and pufa:sfa, r = − 0.44 (CI − 0.60
to − 0.26).
38% of participants were in the same quartile for %E TF,
46% differed by one quartile
43% same quartile for %E sfa, 44% differed by one
quartile.
●
●
●
●
●
●
●
●
●
Total score with sfa (g) r = − 0.30, P o0.05
Estimated mean sfa (g) for 34 men with scores of o15
was 53.4 g vs 41.2 g for those with scores of 416 (n = 34),
P o0.001
Total DFQA score with PUFA:SFA ratio, r = 0.4, P o0.001.
DFQA with FFQ fat estimates, r = 0.54 (Dchol, (g)) to
r = 0.66 (sfa, (g)), P o 0.001.
DFQA classified 39% (mufa, (g)) to 55% (sfa, (g)) of
participants into the same nutrient quartile as the FFQ
and 80–87% into adjacent quartiles. 2% of participants
were grossly misclassified for sfa, mufa, omega-3 s and
dchol.
Baseline STC total score and Pfat score, r = 0.39, P o0.05;
Change in STC score and reduction in Pfat TF, r = 0.22,
P o0.05.
Total change in SisterTalk with total change in BMI,
r = 0.35 (95% CI 0.08, 0.58), P o0.05
(Correlations were not significantly different between
short and long Sister Talk FHQ.)
Post intervention
Change in SisterTalk with change in BMI, r = 0.17 (95%
CI 0.02, 0.39)
During maintenance
● Change in SisterTalk with change in BMI, r = 0.28 (95%
CI 0.02, 0.50), P o0.001.
●
●
●
●
Results
A systematic review of brief dietary questionnaires
CY England et al
17
European Journal of Clinical Nutrition (2015) 1 – 27
European Journal of Clinical Nutrition (2015) 1 – 27
MEDFICTS63,64
NR
NR
4)
NR
NR
2)
NR
3)
NR
NR
Variables
1)
NR
Retest time
(Continued )
MEDFICTS12,62
Tool
Table. 2.
NR
NR
NR
NR
Test
Test retest reliability
NR
NR
NR
NR
Results
NR
NR
NR
NR
Internal
reliability
4)
Block 98 FFQ
3)
Arizona FFQ
2)
Reduced Block
FFQ
1)
3 × food diaries
(unknown)
Reference measure
Same visit
Mean of
52 days
Total score; %E
TF, %E sfa and
Dchol from
diaries
Total score;
consumption of
Step 1, 2 and 3
diets from food
diaries as
assessed by a
dietitian
‘recent’
Pearson's correlation
Test
Total score and
classified as
adherent/nonadherent to TLC
diet from
Medficts
%E TF, %E sfa
and Dchol (mg)
from FFQ
Total score from
Medficts %E TF;
%E sfa; Dchol
from FFQ
Pearson's correlation
Chi squared
Kappa statistics
Spearman's correlation
Chi squared
Independent samples ttest ROC curve analysis
Spearman's correlation
Kappa statistics ROC
Total score and
diet classification curve analysis
(high fat, step 1,
step 2) from
Medficts
Energy (kcal), %E
TF,%E sfa and
Dchol from FFQ
Variables
Time
between
tests
Validity
Total score, r = 0.52, P o 0.0001 (%E TF and %E sfa),
Dchol, r = 0.55, P o0.0001.
Identified as high fat diet: FFQ identified 76.2% vs
MEDFICTS identifying 17.7% of this group.
Recommended MEDFICTS cut-offs correctly identified
23.3% high fat diets and 19.2% Step 1 diets. No
agreement for diet steps between FFQ and Medficts,
kappa = 0.036.
ROC curve analysis showed that a single cut-off of 38
gave sensitivity of 75% and specificity of 72% and
modest agreement with FFQ, kappa = 0.39, P o0.001
for a high fat diet.
●
Total score with %E TF, r = 0.30, P o0.001
Identified high fat diet: FFQ identified 71.2% vs
Medficts identified 50.5%. 59.8% identified on both tools.
Dichotomized Medficts score 430% energy from fat,
chi squared = 8.19, P o0.01; sensitivity of Medficts for
430% energy from fat = 57.3%; specificity = 66%.
Positive predictivity (ie classifying high fat diets as the
FFQ) = 80.6%, negative predictivity (classifying low fat
diets as the FFQ) = 38.5%.ROC curve analysis indicated
Medficts was better than chance (P = 0.03).
Total score with %E sfa, r = 0.52, %E TF, r = 0.31,
Dchol r = 0.54, P o0.0001.
Medficts categorised 44.9% of participants as adherent
to the TLC diet. FFQ categorised 4.2% as adherent.
Categorical agreement
Overall, k = 0.08, P o0.001; o 7% sfa; k = 0.13, P o0.001;
o30% TF, k = 0.16, P o 0.001; o200 mg Dchol, k = 0.34,
P o 0.001.
Sensitivity, adherent to TLC diet = 85.7% of the time,
and specificity, non-adherent = 56.9% of the time.
Specificity lower for women (48.4%) vs men (72.9%)
P o 0.001. Optimal cut-off point o25 improved specificity
to 82.5% and sensitivity of 76.2% overall but men and
women were different with men having an optimal
cut-off o37 (specificity of 80% and sensitivity of 78.3%)
and women an optimal cut-off o 20 (specificity o83.8%,
sensitivity o75%). No difference seen for ethnicity for
sensitivity or specificity.
●
●
●
●
●
●
●
●
●
●
●
●
●
Pre-existing food diaries: ‘Medficts scores correctly
identified the 11 patients consuming a Step 1 diet…the
2 patients consuming a Step 2 diet and the 3 patients
consuming an average American diet’ p85
●
Sample 2 (n = 26)
● Total score with %E TF, r = 0.54 (P = 0.009), Dchol,
r = 0.39 (P = 0.051).
●
Sample 1 (n = 22)
Total score with %E TF, r = 0.79 (P o0.002), %E sfa,
r = 0.60 (P o 0.003), Dchol, r = 0.71 (P = 0.001)
Results
A systematic review of brief dietary questionnaires
CY England et al
18
© 2015 Macmillan Publishers Limited
© 2015 Macmillan Publishers Limited
NR
NR
Dietary fats and fibre
NR
Dietary
Instrument for
Nutrition
Education
(DINE)5
NR
Total and saturated fat and free sugar
Total score
Dietary Fat and 158 (10)
days
Free Sugar
Short
Questionnaire
(DFFQA)69
Rate Your Plate
(RYP)15
Total score
2–3 weeks;
Northwest
Lipid Research 6–8 weeks
Clinic Fat Intake
Score (NWFIS)20
Variables
NR
ICC
NR
Pearson's
correlation
ICC, Paired
Wilcoxon rank
score
Percentage
agreement in
classification
Test
Test retest reliability
3 groups
from NLSChol
3 groups
derived from
diet history.
Retest time
(Continued )
30 days
NLSChol
Questionnaire58
Tool
Table. 2.
Internal
reliability
NR
ICC = 0.83 (95% CI
0.66–0.91)
NR
NR
Total score,
α = 0.76.
NR
Retest after 2–
NR
3 weeks r = 0.88 for
men, 0.90 for
women (P o0.001)
After 6–8 weeks r =
0.76 for men and
0.78 for women
(P o0.001)
ICC = 0.89 (no Cis); Total score,
α = 0.69
Agreement in
classification = 85%
(17 patients), 15%
(3 patients) moved
up or down a
group. Comparison
of medians was not
significant, P = 0.52.
Results
Time
between
tests
Same visit
for FFQ,
shortly
after for
food diary
Same visit
4 day un-weighed 5 days
food diary
Anthropometrics,
Commonwealth
Scientific and
Industrial
Research
Organisation
Food Frequency
Questionnaire
4 day un-weighed
food diary
Willett SFFQ
4 day un-weighed NR
food diary,
Biomarkers of
preclinical disease
Diet history
Reference measure
Pearson's correlation
Kappa statistics
Bowker’s test of
symmetry
Test
Pearson's correlation
DINE fat score;
DINE fibre score;
Fat and fibre
intake from
diaries
Pearson's correlation
Weighted kappa
Percentage agreement of
classification
Gross misclassification
Total DFFS score; Spearman's correlation
DFFS subscales; Independent samples tNutrients from
test
FFQ
Total score from
RYP; dietary fats
and Dchol from
FFQ
Total NWFIS and Pearson's correlation
change in FIS TF,
sfa, Dchol
(adjusted and
not adjusted for
energy), Keys
score, RISCC
score, change in
nutrients, change
in Keys score and
change in RISCC
score from
diaries
Change in
plasma
cholesterol
3 groups from
NLSChol
3 groups derived
from diet history.
Variables
Validity
Group classification, r = 0.3, P = 0.029
Agreement of 72% between dietitian classification and
NLSChol score, kappa = 0.48 (0.10; 0.69).
Bowker's test of symmetry was not significant.
●
●
●
●
●
●
●
DINE fat score, from r = 0.28 (%E TF) to 0.57 (sfa, (g));
fibre score with fibre (g) r = 0.46; DINE unsaturated
fat score with pufa:sfa ratio, r = 0.43, P o0.001.
Weighted kappa = 0.38 for TF (g) and 0.30 for fibre (g).
Exact agreement of categorization was 53% for TF (g),
52% for fibre (g). Gross misclassification = 6% for TF (g)
and 5% for fibre (g).
By tertiles 53% agreed and 7% misclassified for TF (g)
and 49% agreed with 10% misclassification for fibre (g).
DFFS with food diary nutrients, from r = 0.35 (energy),
P o 0.05 to r = 0.46 (% E sfa), P o0.01.
DFFS with FFQ nutrients, from r = 0.40 (energy) to
r = 0.71 (% E sfa), P o 0.01.
Subscales with nutrients, from r = 0.33 (fat subscale with
diary %E sfa), P o0.05 to r = 0.68 (fat-sugar subscale
with FFQ %E sfat).
For DFS scores o 60 mean %E TF = 28.56 vs DFS
score460 mean %E TF = 33.51 (3.87), P o 0.01; DFS
scores o60 mean %E free sugars = 7.41 (4.54) vs DFS460
mean %E free sugars = 11.39 (6.15), P o 0.05.
Total score, r = − 0.28 (% E TF, less trimmed fat) to
r = − 0.48 (% E sfa), P o 0.05.
●
●
Change in NWFIS with change in TF (g), r = 0.38 (men)
and r = 0.40 (women), in sfa (g), r = 0.42 (both), in
Dchol (mg), r = 0.32 (men) and 0.52 (women), in Keys
score, r = 0.38 (men) and 0.48 (women), in RISCC,
r = 0.39 (men) and 0.51 (women) P o0.001.
●
Baseline
Total NWFIS with %E TF, r = 0.49, %E sfa, r = 0.44,
Dchol mg/1000kcal, r = 0.46, Keys score, r = 0.46, RISCC,
r = 0.53 P o0.001.
18 months
● Total NWFIS with %E TF, r = 0.55, %E sfa, r = 0.64,
P o 0.001 and Dchol mg/1000kcal, r = 0.30, P o0.01,
Keys score, r = 0.58 and RISCC, r = 0.56, P o0.001
●
●
●
●
Results
A systematic review of brief dietary questionnaires
CY England et al
19
European Journal of Clinical Nutrition (2015) 1 – 27
European Journal of Clinical Nutrition (2015) 1 – 27
Same day
Total score;
individual
items
FFDB Fat
score; FFDB
fibre score;
subscale
scores
3 months
1 year
Fat and Fibre
Diet Behaviour
Questionnaire
(FFDBQ)18
Norweigian
SmartDiet
Questionnaire
(N-Smart)7
Total score
1)
7–9 weeks
Fat and Fibre
Barometer
(FFB)9
Pearson's
correlation,
Percentage
agreement of
classification,
Weighted
kappa
Spearman's
correlation
Pearson's
correlation
Test
Test retest reliability
Variables
(Continued )
Retest time
Tool
Table. 2.
Variables
Test
NR
r = 0.95 (no P
value); mean
agreement
rate = 0.93 (range
0.85 for vegetables
to 0.98 for milk);
Weighted Kappa
ranged from 0.75
(95% CI, 0.63–0.86)
(vegetables) to 0.97
(95% CI, 0.94–1.00)
(cheese).
7 day weighed
food diaries
8 days
(screener
first)
Same time
FFQ was
developed for the
study and based
on a pre-existing
FFQ evaluated
against diet
records
Total score, foods Pearson's correlation
and food groups LOA
from screener
Weighted kappa
Total calculated
score, food, food
groups and
nutrients from
diary
Spearman's correlation
FFDB Fat score;
FFDB fibre score,
subscale scores;
%E TF, fibre/
1000kcal;
servings of F+V
from FFQ
Time
between
tests
3 months: FFDB fat NR
score, r = 0.79;
FFDB fibre score,
r = 0.74, P o 0.001
Subscales from r =
0.60 (modify meals
to be low in fat) to
0.74 (substitute for
low-fat foods),
P o 0.001.
1 year: FFDB fat
score, r = 0.74;
FFDB fibre score,
r = 0.70 for fibre
score, P o0.001
Subscales r = 0.53
(modify meals to be
low in fat) to 0.66
(substitute highfibre for low-fibre
foods; Substitute
especially
manufactured low
fat foods),
P o0.001.
Reference measure
1)
7–10 weeks FFB score;
Pearson's correlation
Geelong, meal
TF (g), total fibre Weighted kappa Gross
based FFQ
(g), % E TF, fibre misclassification
developed by the
(g/10MJ) from
Deakin Institute
FFQ
Internal
reliability
r = 0.92 (95% CI,
1)
0.89–0.94). No
NR
difference between
men and women
Results
Validity
Men: FFB score with %E TF, r = − 0.33 (−0.05, − 0.56);
fibre (g/10MJ) r = 0.83 (0.71, 0.90).
Women: FFB score with %E TF, r = − 0.75 (−0.60, − 0.85),
fibre (g/10MJ) r = 0.58 (0.36, 0.74).
Weighted kappa
● Men: %E TF = 0.39 (0.18, 0.61), fibre (g/10MJ) =
0.59 (0.42, 0.76)
Women: 12% for TF (g), 2% for %E TF and 10% for both
fibre variables
●
●
●
●
●
●
●
●
Total scores, r = 0.73
Correlations with nutrients was highest for sfa (g),
r = − 0.59
Kappa from 0.71 (0.56–0.86) for milk and 0.73 (0.60–0.86)
for spreads to 0.42 (0.28–0.55) for fruit and vegetables.
Agreement ranged from 0.98 for milk to 0.38 for fish,
mean agreement = 0.73
Distribution of difference between the food diaries
and tool total score, mean = 1.9 (95% CI 1.4–2.5),
LOA = − 0.8–7.7
Fibre/1000kcal, from r = 0.24 (substitute high fibre for
low fibre) to r = 0.43 (F+V), P o 0.001.
FFDB fat score with % E TF, r = 0.53, P o0.001
FFDB fibre score with fibre/1000kcal, r = 0.50, P o 0.001;
with servings of F+V, r = 0.50, P o 0.001.
Subscales
● %E TF, from r = 0.2 (replace high-fat meats) to r = 0.43
(avoid fat as a flavouring), P o 0.001.
●
●
Women: %E TF = 0.58 (0.41, 0.75), fibre (g/10MJ) = 0.27
(0.06, 0.48).
Gross misclassification
● Men: 15% for TF (g), 9% for %E TF, 6% for fibre (g), 0 for
fibre g/10MJ
●
●
Results
A systematic review of brief dietary questionnaires
CY England et al
20
© 2015 Macmillan Publishers Limited
Retest time
(Continued )
Variables
© 2015 Macmillan Publishers Limited
1 month
Dutch fruit and 1 month; 1
year
vegetable
questionnaire
19
(D-F&V)
Fruit and/or vegetables
Canadian Fruit NR
and Vegetable
Questionnaire
(CFV-Q)66
Hispanic Fat
and Fruit and
Vegetable
screeners
(H-F&FV)42
NR
Test
Test retest reliability
F+V intake;
tertiles of
intake
NR
Spearman's
correlation
Percentage
agreement of
classification
NR
F+V score; fat Pearson's
score
correlation,
percentage
agreement
Total fat and fruit and vegetables
Block Fat, Fruit NR
NR
and Vegetable
Screeners
(B-F&FV)6
Tool
Table. 2.
NR
Internal
reliability
NR
Retest at 1 month: NR
Total F+V, r = 0.80.
Individual variables,
r = 0.49 (other
fruits) to 0.82 (F+V
juices). Agreement
classification:
vegetables = 59%;
41% up or down a
class
fruits = 74%; 24%
up or down a class;
3% up two classes.
Retest at 1 year:
total F+V, r = 0.79.
Individual variables
r = 0.31 (other
fruits) to 0.81 (total
vegetables).
Agreement
classification,
vegetables = 70%;
30% up or down a
class; fruits = 57%;
40% up or down a
class; 2% down two
classes.
NR
r = 0.64 for F+V
NR
score, 0.85 for fat
score, P o0.001.
84% agreement for
vitamin
supplement use,
P o 0.001
NR
Results
Change in
concentration
biomarkers
FFQ developed
and tested by
Goulet et al. at
Laval University
NR
100-item Block
FFQ
Reference measure
Same time
Same visit
NR
Posted
together
Time
between
tests
Test
Pearson's correlation
ICC
ROC curve analysis
NR
F+V intake from Spearman's correlation
screener; mean
changes in
screener score;
Mean change in
plasma
carotenoids;
Mean change in
plasma vitamin C
Servings F+V
from FV-Q and
FFQ
NR
Spearman's correlation
Meats/Snacks
score and
F+V scores from
screener; fat
nutrients, F+V
servings, fibre (g)
and
micronutrients
from FFQ;
Variables
Validity
Meat/snack score, from r = 0.60 (Dchol, mg) to r = 0.72
(sfa, g), P o 0.0001
F+V score (without pulses), from r = 0.41 (magnesium,
mg) to r = 0.71 (fruit and vegetable servings), P o0.0001.
F+V screener (including pulses), from r = 0.46
(magnesium, mg) to r = 0.62 (fibre, g), P = 0.0001.
89% of people low on F+V score were very low or
quite low on FFQ. 12% of people scored as needing
advice on fat on the screener did not need advice
according to FFQ.
Servings, r = 0.66, P o0.001 (obese participants); r = 0.65,
P o0.001 (non-obese).
ICC for obese = 0.44, for non-obese = 0.46.
Sensitivity in obese group = 88.5%, specificity = 63.6%.
Positive and negative predictive values = 45% and 94%.
Non-obese sensitivity = 80% and specificity = 66%,
positive predictive values = 40% and negative = 92%
(no difference between obese and non-obese).
ROC curve indicated that the more accurate cut-off point
⩾ 5 servings/day vs o5 servings/day (c = 0.74).
Baseline
Total F+V intake, from r = 0.23 (plasma B-carotene and
lutein) to r = 0.37 (plasma vitamin C), P o 0.01.
After intervention
● Changes in total F+V consumption, from r = 0.26 (change
in B-carotene) to r = 0.39 (change in B-crytoxanthin),
P o0.01.
●
●
●
●
●
●
NR
●
●
●
●
Results
A systematic review of brief dietary questionnaires
CY England et al
21
European Journal of Clinical Nutrition (2015) 1 – 27
European Journal of Clinical Nutrition (2015) 1 – 27
NR
NR
NR
NR
Test
Test retest reliability
NR
NR
NR
NR
Results
NR
NR
NR
NR
Internal
reliability
7 day un-weighed
food diary
Diet history
3 × 24 h recall
2 × 24 h recall
Reference measure
Servings F+V,
including and
not including
salad, potatoes,
fried vegetables,
and fruit juices
from screener
and recalls.
Mean and
median servings
F+V/day
(adjusted for
within person
variation) from
screener and
recalls.
Variables
Mean daily
intakes of fruit
and vegetables
from both
questionnaires
Same day
Daily servings of
(diet
fruit from
history last) M-FRHQ and diet
history
NR
1 year
Time
between
tests
Spearman's correlation,
Percentage agreement of
classification
Pearson’s correlation
LOA,
Kappa statistics
Percentage of agreement
Gross misclassification
Pearson's correlation
Paired sample t-test
Men
β = 0.52; median (IQR) F+V servings from the recall
vs the screene r = 6.6 (3.6) vs 3.7 (2.6), P o0.001
Women
● β = 0.50; median (IQR) F+V servings from recall vs
screene r = 5.5 vs 4.2 (no IQRs quoted), P o 0.001.
Percentages eating 5 a day
● Men = 73% for recall vs 24% for the screener
Linear regression
analysis; Maximum
likelihood estimates
Total F+V servings, r = 0.50, P o0.001
Individual items, from r = 0.27, P o0.01 (‘cooked,
excluding fried vegetables’) to r = 0.59, P o0.001
(‘fruit’).
Total F+V servings, recall vs screene r = 4.1 (2.1) vs
3.3 (1.9), P o0.001.
Fruit alone recall vs screene r = 0.97 vs 0.84; juice
alone recall vs screene r = 0.37 vs 0.56; total vegetable
recall vs screene r = 2.53 vs 1.80, P o0.001.
No difference for fruit+juice.
Daily servings, r = 0.57, P o0.001 (men, r = 0.67,
P o0.001;women, r = 0.49, P o0.001
LOA = − 2.98–4.31, mean difference = 0.66 (CI: 0.32,1.02)
kappa = 0.41, P o0.001
Percentage of agreement = 60%; 12.5% were grossly
misclassified
Correctly classified 70% as achieving or not achieving
⩾ 2 servings/day.
Overestimated group mean by 32%, P o0.001
Positive predictive value was 87% (64% sensitivity),
negative predictive value was 66% (88% specificity)
Total F+V, r = 0.43; total fruit, r = 0.51; total vegetables,
r = 0.35.
36.8% misclassified for total fruit intake; 22.5% for total
vegetable intake. 7 people (14%) were classified as
meeting recommendations for total F+V intake on
screener who were not on FFQ.
●
●
●
●
●
●
●
●
●
●
●
●
●
Women = 59% for recall vs 36% for the screener
●
●
Results
Test
Validity
Abbreviations: ANTOX-S, Antioxidant Score; BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; Dchol, dietary cholesterol; DQI, Diet Quality Index; %E, % energy; FFQ, Food frequency
questionnaire; F+V, Fruit and vegetables; HEI, Healthy Eating Index; HDL-C, HDL-cholesterol (mmol/l); ICC, Intra-class correlation; LOA, Limits of agreement; MAR, Mean Nutritional Adequacy Ratio;
mMDS = modifided Mediterranean Diet Score; mufa, monounsaturated fat; OR, odds ratio; PCA, principal component analysis; pufa, poloyunsaturated fat; ; RISCC, ratio of ingested saturated fat and cholesterol
to calories; sfa, saturated fat; TChol, total cholesterol; TF, total fat; TG, triglycerides (mmol/l); TLC, Adult Treatment Panel III Therapeutic Lifestyle Changes diet; transF, trans fat;.WC, waist circumference (cm).
NR
NR
2)
NR
Short Dutch
NR
questionnaire
to measure fruit
and
vegetables16
NR
1)
NR
NR
Variables
Retest time
NR
Mainvil Fruit
Habits
Questionnaire
(M-FRHQ)68
Five a day
screener
(5- F&V)
Five a day
screener
(5- F&V)17,67
Tool
Table. 2. (Continued )
A systematic review of brief dietary questionnaires
CY England et al
22
© 2015 Macmillan Publishers Limited
© 2015 Macmillan Publishers Limited
D
D
M
M
M
M/C
D
M
M
M
M
13
15
16
14
13
15 food 8
vit/minb
31
16
18
15
14
Bailey Elderly Food
Screener28,51
Food Behaviour
16
Checklist: Text version10
16
Australian Diet Quality
Tool50
Food Behaviour
Checklist: Visual
version43
Food Behaviour
Checklist: Visual
version, Spanish44
Healthy Eating Vital
Signs 18,53
Latino Dietary
Behaviors
Questionnaire54
PrimeScreen55
REAP (2006)
REAP-S29
Short Diet Quality
Screener13
Brief Mediterranean
Diet Screener13
Mediterranean Diet
Adherance Score56
Dutch fat consumption 25
questionnaire (Dutch)57
M/C
M/C
M
M
30
8
20
10
Sister Talk Food Habits
(short form)3
Starting the
Conversation39
Dietary Fat Quality
Assessment60
Heart Disease
Prevention Project
Screener61
M
17
Short Fat
Questionnaire56
M
20/25
Fat-Related Diet Habits
Questionnaire/Kristal’s
Food Habits
Questionnaire11,46,21,47
D
D
M
Purposea D = Dietary advice (includes
clear clinical guidance) M = dietary
monitoring (limited clinical guidance)
C = sensitive to change
S
S/I
S
S
S
S/I
T
I
S
S
S
S
S
I
I
I
I
I
S
S
Administration
I = interview
S = self
T = Telephone
✓1
✓
✓1
✓1
✓1
✓
✓
✓
✓1
✓5
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓1
✓1
✓
✓1
✓
✓
✓2
✓
✓
✓1
✓
✓
✓2
✓1
✓
✓1
✓1
✓
✓
✓1
✓2
Some relative
validity
correlation
coefficients
40.4
Relative
validity
study
✓
✓
✓
Testretest
reliability
study
Summary of key characteristics of dietary assessment tools and evaluation studies
Tool name (date of most Number of
recent evaluation)
questions
Table 3.
✓
✓
✓
✓
✓
✓
✓
✓
✓
Relative
validity in
a clinical
sample
✓m
✓w
✓w
✓w
Relative
validity in
men (m) or
women (w)
only
Tool and detailed score sheet available in Heller (1981)
Tool available and scoring described in Kraschnewski (2013)
Tool and detailed score sheet available in Paxton (2011)
Tool available and scoring derived from Anderson (2007). No
response to a request for a full description of scoring.
Tool and detailed score sheet available in Dobson (1993)
Tool and detailed score sheet available from: http://sharedresources.
fhcrc.org/documents/fat-related-questionnaire. Rights from nasr@fhcrc.
org
Tool available and scoring described in van Assema (1992)
Tool available and scoring described in Martinez-Gonzalez (2012)
Tools available on request from authors. Scoring derived from
Schroder (2011).
Tools available on request from authors. Scoring described in
Schroder (2011).
Tool and detailed score sheet available from: http://www.einstein.yu.
edu/centers/diabetes-research/research-areas/survey-instruments.
aspx
Tool and detailed score sheet available from: http://publichealth.
brown.edu/ICHP/research-tools
Tool and detailed score sheet available free, request from:
Department of Nutrition, Harvard School of Public Health (http://
www.hsph.harvard.edu/)
Tool available and scoring described in Fernandez (2011)
Tool available and scoring described in Greenwood (2008)
Visual tools, with instructions, are available from http://townsendlab.
ucdavis.edu/PDF_files/UCCE/UCCE_FBC_InstructGuide.pdf
Tool and scoring derived from validation results in Townsend (2003)
Tool and detailed score sheet available in Bailey (2009)
Tool available in paper. Score sheet available on request from
authors. Free to use, acknowledgement needed
Access and availability of score sheet
A systematic review of brief dietary questionnaires
CY England et al
23
European Journal of Clinical Nutrition (2015) 1 – 27
D
D
D
M
D
M/C
20
11
12
23
26
29
20
29
15
Fat 17
F&Vb 7
Fat 16 F&V D
7
Medficts12,62,63,64
NLSChol
Questionnaire58
Northwest Lipid
Research Clinic Fat
Intake Score20
Rate Your Plate15
Dietary Fat and Free
Sugar Short
Questionnaire69
DINE5
European Journal of Clinical Nutrition (2015) 1 – 27
Fat and Fibre
Barometer9
Fat and Fibre Diet
Behaviour
Questionnaire18
Norweigian SmartDiet
Questionnaire7
Block fat and fruit and
vegetable screener6
Hispanic fat and fruit
and vegetable
screener42
M/C
Dutch fruit and
vegetable
questionnaire19
M
10
Short Dutch
questionnaire to
measure fruit and
vegetables16
S
S
S
S
S
✓
✓
✓
✓
✓
✓1
✓1
✓
✓2
✓1
✓1
✓
✓1
✓
✓
✓1
✓
✓
✓1
✓
✓
✓1
✓
✓1
✓
✓
✓1
✓
✓1
✓
✓
✓1
✓
✓
✓
✓6
✓1
Some relative
validity
correlation
coefficients
40.4
Relative
validity
study
✓
Testretest
reliability
study
✓
✓
✓
✓
✓
Relative
validity in
a clinical
sample
✓w
Relative
validity in
men (m) or
women (w)
only
Tool available and scoring described in van Assema (2002)
Tool and detailed score sheet available on request from the authors
Tool and detailed score sheet available in Thonpson (2000)
Tool available and scoring described in Bogers (2004)
Tool and detailed score sheet available in Godin (2008)
Tool and detailed score sheet available in Wakimoto (2006)
Tool and detailed score sheet available for research (pay to use).
Available on-line to individuals and provides instant feedback. http://
nutritionquest.com/assessment/list-of-questionnaires-and-screeners/
Tool available in and scoring described in Svilaas (2002). No response
to request for further information re administrator copy
Tool and scoring derived from validation results in Shannon (1997)
A version with detailed score sheet was developed for use in practice
by Seal and O'Keef, (creative commons licence) available from: http://
www.diabetesoutreach.org.au/7Steps/HealthyEating/docs/Healthy%
20EAting%20Fat%20%20Fibre%20Barometer.pdf
Tool and detailed score sheet available. Copyright is held by The
Department of Primary Care at Oxford University and permission
must be sought from the Department or from Liane Roe, lsr7@psu.
edu.
Tool and description of scoring available in Francis (2012)
Tool and detailed score sheet available from Nutrition in Clinical
Care. 2000; 3: 163–169. RYP was developed for and used during the
Pawtucket Heart Health Program as a clinical tool to guide
consultations.
Tool and detailed score sheet obtained directly from Alice Dowdy.
Tool is considered to be outdated (Dowdy, personal communication,
2013)
Tool and detailed score sheet available in Beliard (2012)
Tool and detailed score sheet available in Kris-Etherton (2001)
Access and availability of score sheet
a
Tested for acceptability by an undescribed sample or not in the population of interest. cPre-tested by clinicians. Numerical superscript indicates the number of samples for reliability and validity testing.
Clinical guidance may be provided in the form of a crib sheet or scoring cut-offs. Questionnaires that do not include this may still be suitable for the provision of advice but there may be a need for more
training before use. bvit/min = vitamin/mineral supplements; F&V = fruit and vegetables.
M
5
Mainvil fruit habits
questionnaire68
u
M
Five a day screener/NCI 7
fruit and vegetable
screener17,67
8
D
Canadian Fruit and Veg 6
Questionnaire66
S
D
S
S
I
S
S/I
S
S
S
S
S
Administration
I = interview
S = self
T = Telephone
D
D
D/C
Purposea D = Dietary advice (includes
clear clinical guidance) M = dietary
monitoring (limited clinical guidance)
C = sensitive to change
(Continued )
Tool name (date of most Number of
recent evaluation)
questions
Table. 3.
A systematic review of brief dietary questionnaires
CY England et al
24
© 2015 Macmillan Publishers Limited
A systematic review of brief dietary questionnaires
CY England et al
25
Table 4.
A checklist for choosing a brief dietary questionnaire for clinical use
Purpose
Population
Setting
Administration
Reliability
Validation
Acceptability
Timescale
Use
What is the dietary component of interest?
What is the purpose of the dietary assessment?
To assist in the provision of dietary advice
To measure dietary change
To monitor dietary habits
Other
What is the population of interest?
Country
Language
Demographics
What is the setting?
Clinical
Community
Other
How is the tool administered?
Interview
Self-administered
Telephone
On-line
Other
Has the tool undergone retest assessment?
Has the tool been tested for relative validity in the community in which it will be used within the last 10 years?
Could the conduct of the study have affected the results (e.g., reference measure and tool completed at the same time)?
Are correlations for the dietary components of interest ⩾ 0.4?
Was the Bland–Altman method employed? Are the limits of agreement acceptable?
Did stratification by, e.g, gender/age/ethnicity affect the results?
Has the clarity of language been checked with users?
What is the time period of interest (last week/last month/last year)?
Are the number of questions and the estimated time to complete acceptable?
Is the tool easy to score?
Are the results easy to interpret?
Is an administrator copy easily available?
What permissions are needed for use and are there costs involved?
the current review for reasons of length or because the scoring
algorithms were complex and unlikely to be used in clinical
practice. Tools were reported as validated against longer FFQs,
food diaries or 24-h recalls. Correlation coefficients for total fruit
and vegetable intakes ranged from 0.29 to 0.80. As the tools
measured the same aspect of the diet, comparisons were possible
and this review concluded that more detailed tools that asked
about portion sizes and the consumption of mixed vegetable
dishes showed greater relative validity. Cade et al.41 also comment
that FFQs asking people to estimate their own portion sizes are
more reliable. Only one tool included in the current review asks
people to estimate their portion sizes by providing a multiple
choice list of three different sizes.12
All the studies previously reviewed used correlations alone to
assess reliability and relative validity. This remains the most
common method, and only five studies in the present review
made use of the Bland–Altman method. Correlation coefficients
are not measures of absolute agreement but are instead measures
of relative agreement, assessing whether an individual has
maintained their ranking relative to other participants. The intraclass correlation coefficient was used to evaluate four tools, but
this measure has also been criticised and data simulations have
shown that high correlations can be achieved with low absolute
agreement.71 The Bland–Altman method assesses limits of
agreement that define the range that 95% of the differences
between the measures lie within, and may include graphical
presentation of the data. Clinical knowledge must be used to
decide if the limits of agreement are acceptable.72 Of the studies
that used the Bland–Altman method, one was published in 20027
and the remainder after 2010, with three studies conducted by the
same team.13,56,68
© 2015 Macmillan Publishers Limited
Clinical implications
It is important that clinicians are clear about their purpose when
selecting a tool for use. In clinical practice, dietary assessment is
required to assist in the provision of dietary advice or to measure
the impact of dietary intervention.4 Brief dietary questionnaires
used for the former purpose are those that give clear guidance on
moving to healthier dietary habits rather than obtaining a
detailed, quantitative assessment of an individual’s diet. Assessment may be focussed on certain nutrients to be disease-specific
or may be concerned with overall diet quality. Typical questions
from tools included in the current review include asking about the
frequency of consumption of sweet foods or savoury snacks, with
responses ranging from less than once a week to more than three
times a day. The answers can be used to target dietary advice to
the individual. Tools suitable for measuring the impact of a dietary
intervention must also be able to measure change.
This review provides evidence that tools developed and tested
in one population may not have the same relative validity in a
different population. Equally, tools developed in different
countries will include different food items, also affecting relative
validity. It should be noted that English translations of tools
developed in Spanish, French, Norwegian or Dutch have not been
validated and that older tools may no longer be appropriate
because of shifts in food habits and processing.73 In common with
previous reviews,22,70 studies with small sample sizes were not
excluded. Cade et al.41 report a wide range of sample sizes for
relative validation studies of long FFQs and found no difference in
reported correlation coefficients between studies with large
sample sizes compared with small sample sizes. However, with
small sample sizes, confidence intervals are likely to be wide and
consequently sample sizes of around 100–200 are advised.40
European Journal of Clinical Nutrition (2015) 1 – 27
A systematic review of brief dietary questionnaires
CY England et al
26
Clinicians should consider the sample sizes of test-retest and
relative validation studies if tools are to be used ‘off the shelf’.
Developers of future tools can enhance understanding
of the development, relative validity and reliability of tools by
clearly describing: (i) how items were derived; (ii) the population of
interest; (iii) the characteristics of the sample for reliability and
relative validation studies; (iv) the results of these studies; and
(v) whether stratification by age, gender, ethnicity and socioeconomic status affected results. Tools that are most helpful for
clinical use need to have a clearly described and simple scoring
system, and ideally a copy presented in the paper or in an online
appendix for evaluation with clear information about copyright.
Table 4 provides a checklist to assist practitioners when choosing
a brief dietary questionnaire for clinical use.
CONCLUSION
This review identified and summarised 35 short dietary assessment tools of potential use in clinical practice for the dietary
management of cardiovascular disease, obesity and type 2
diabetes. In general, tools demonstrated adequate reliability
and/or relative validity, although around half have been developed and evaluated exclusively in US populations. It is not
possible to determine whether any one tool is clearly better than
another for a given population or purpose owing to differences in
the design of reliability and relative validity studies. If tools are to
be used in different countries or populations, they need to be
adapted and evaluated locally to ensure they are reliable and have
acceptable levels of relative validity.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
We thank the developers of brief questionnaires who provided access to their
questionnaires for evaluation, their scoring algorithms and supplementary
information on usage and copyright. We thank Amir Emadian for independent data
extraction on 25% of the included papers. Clare England is supported by NIHR Clinical
Doctoral Research Fellowship 10-017. The study was carried out at The University of
Bristol, Senate House, Tyndall Avenue, Bristol BS8 1TH.
DECLARATION
This submission represents original work that has not been published previously and
it is not being considered for publication elsewhere.
AUTHOR CONTRIBUTIONS
The work contained in this article is part of the PhD of Clare England which is
supervised by Drs’ Andrews, Jago and Thompson. All authors assisted in the
design of the data extraction form and development of the search strategy. Ms
England screened all titles and abstracts and extracted the data with advice on
clinical application from Dr Andrews and final inclusion from Professor
Thompson. Professor Jago provided analytical guidance. The first draft of the
manuscript was prepared by Ms England with critical input and revisions by all
other authors. All authors approved the final manuscript.
REFERENCES
1 Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H et al. A comparative
risk assessment of burden of disease and injury attributable to 67 risk factors and
risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global
Burden of Disease Study 2010. Lancet 2012; 380: 2224–2260.
2 Thomas B, Bishop JManual of Dietitic Practice4th EditionJohn Wiley and Sons Ltd:
Chicester, 2007.
3 Anderson CAM, Kumanyika SK, Shults J, Kallan MJ, Gans KM, Risica PM. Assessing
change in dietary-fat behaviors in a weight-loss program for African Americans: a
potential short method. J Am Diet Assoc 2007; 107: 838–842.
European Journal of Clinical Nutrition (2015) 1 – 27
4 Thompson FE, Byers T. Dietary Assessment Resource Manual. J Nutr 1994; 124 (11
Suppl): 2245s–2317s.
5 Roe L, Strong C, Whiteside C, Neil A, Mant D. Dietary intervention in primary care:
Validity of the DINE method for diet assessment. Fam Pract 1994; 11: 375–381.
6 Block G, Gillespie C, Rosenbaum EH, Jenson C. A rapid food screener to assess fat
and fruit and vegetable intake. Am J Prev Med 2000; 18: 284–288.
7 Svilaas A, Strom EC, Svilaas T, Borgejordet A, Thoresen M, Ose L. Reproducibility
and validity of a short food questionnaire for the assessment of dietary habits.
Nutr Metab Cardiovasc Dis 2002; 12: 60–70.
8 Greenwood JLJ, Lin J, Arguello D, Ball T, Shaw JM. Healthy eating vital sign: a new
assessment tool for eating behaviors. ISRN Obes 2012; 2012: 7.
9 Wright JL, Scott JA. The Fat and Fibre Barometer, a short food behaviour questionnaire: reliability, relative validity and utility. Australian Journal of Nutrition &
Dietetics 2000; 57: 33–39.
10 Murphy SP, Kaiser LL, Townsend MS, Allen LH. Evaluation of validity of items for a
food behavior checklist. J Am Diet Assoc 2001; 101: 751–761.
11 Kristal AR, Shattuck AL, Henry HJ. Patterns of dietary behavior associated with
selecting diets low in fat: reliability and validity of a behavioral approach to
dietary assessment. J Am Diet Assoc 1990; 90: 214–220.
12 Kris-Etherton P, Eissenstat B, Jaax S, Srinath UMA, Scott L, Rader J et al.
Validation for MEDFICTS, a Dietary Assessment Instrument for Evaluating
Adherence to Total and Saturated Fat Recommendations of the National
Cholesterol Education Program Step 1 and Step 2 Diets. J Am Diet Assoc 2001;
101: 81–86.
13 Schroder H, Benitez Arciniega A, Soler C, Covas M-I, Baena-Diez JM, Marrugat J.
Validity of two short screeners for diet quality in time-limited settings. Public
Health Nutr 2012; 15: 618–626.
14 Segal-Isaacson CJ, Wylie-Rosett J, Gans KM. Nutrition update. Validation of a short
dietary assessment questionnaire: the Rapid Eating and Activity Assessment for
Participants Short Version (REAP-S). Diabetes Educ 2004; 30: 774.
15 Gans KM, Sundaram SG, McPhillips JB, Hixson ML, Linnan L, Carleton RA. Rate your
plate: An eating pattern assessment and educational tool used at cholesterol
screening and education programs. J Nutr Educ 1993; 25: 29–36.
16 Van Assema P, Brug J, Ronda G, Steenhuis I, Oenema A. A short dutch questionnaire to measure fruit and vegetable intake: relative validity among adults
and adolescents. Nutr Health 2002; 16: 85–106.
17 Thompson FE, Kipnis V, Subar AF, Krebs-Smith SM, Kahle LL, Midthune D et al.
Evaluation of 2 brief instruments and a food-frequency questionnaire to estimate
daily number of servings of fruit and vegetables. Am J Clin Nutr 2000; 71:
1503–1510.
18 Shannon J, Kristal AR, Curry SJ, Beresford SA. Application of a behavioral approach
to measuring dietary change: the fat- and fiber-related diet behavior questionnaire. Cancer Epidemiol Biomarkers Prev 1997; 6: 355–361.
19 Bogers RP, Van Assema P, Kester ADM, Westerterp KR, Dagnelie PC.
Reproducibility, validity, and responsiveness to change of a short questionnaire
for measuring fruit and vegetable intake. Am J Epidemiol 2004; 159:
900–909.
20 Retzlaff BM, Dowdy AA, Walden CE, Bovbjerg VE, Knopp RH. The Northwest Lipid
Research Clinic Fat Intake Scale: validation and utility. Am J Public Health 1997; 87:
181–185.
21 Glasgow RE, Perry JD, Toobert DJ, Hollis JF. Brief assessments of dietary behavior
in field settings. Addict Behav 1996; 21: 239–247.
22 Calfas KJ, Zabinski MF, Rupp J. Practical nutrition assessment in primary care
settings: a review. Am J Prev Med 2000; 18: 289–299.
23 National Cancer Institute. Register of Validated Short Dietary Assessment Instruments, National Institutes of Health, 2013. Available from: http://appliedresearch.
cancer.gov/diet/shortreg/ (Accessed 11 February 2015).
24 Eckel RH, Jakicic JM, Ard JD, Miller NH, Hubbard VS, Nonas CA et al.
2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular
risk: a report of the American College of Cardiology/American Heart Association
Task Force on Practice Guidelines. J Am Coll Cardiol 2013; 63(25 Pt B)
2960–2984.
25 Dyson PA, Kelly T, Deakin T, Duncan A, Frost G, Harrison Z et al.
Diabetes UK Nutrition Working Group Diabetes UK evidence-based nutrition
guidelines for the prevention and management of diabetes.Diabet Med 2011; 28:
1282–1288.
26 WHO/FAO Diet, nutrition and the prevention of chronic diseases 2002, World Health
Organisation: Geneva.
27 Konrad TR, Link CL, Shackelton RJ, Marceau LD, von dem Knesebeck O, Siegrist J
et al. It's about time: physicians' perceptions of time constraints in primary
care medical practice in three national healthcare systems. Med Care 2010; 48:
95–100.
28 Bailey RL, Miller PE, Mitchell DC, Hartman TJ, Lawrence FR, Sempos CT et al.
Dietary screening tool identifies nutritional risk in older adults. Am J Clin Nutr
2009; 90: 177–183.
© 2015 Macmillan Publishers Limited
A systematic review of brief dietary questionnaires
CY England et al
27
29 Gans KM, Risica PM, Wylie-Rosett J, Ross EM, Strolla LO, McMurray J et al.
Development and evaluation of the nutrition component of the Rapid Eating and
Activity Assessment for Patients (REAP): a new tool for primary care providers.
J Nutr Educ Behav 2006; 38: 286–292.
30 Ling AM, Horwath C, Parnell W. Validation of a short food frequency questionnaire
to assess consumption of cereal foods, fruit and vegetables in Chinese Singaporeans. Eur J Clin Nutr 1998; 52: 557–564.
31 Peters JR, Quiter ES, Brekke ML, Admire J, Brekke MJ, Mullis RM et al. The eating
pattern assessment tool: A simple instrument for assessing dietary fat and
cholesterol intake. J Am Diet Assoc 1994; 94: 1008–1013.
32 Banna JC, Vera Becerra LE, Kaiser LL, Townsend MS. Using qualitative methods to
improve questionnaires for Spanish speakers: assessing face validity of a food
behavior checklist. J Am Diet Assoc 2010; 110: 80–90.
33 Cade J, Thompson R, Burley V, Warm D. Development, validation and utilisation
of food-frequency questionnaires – a review. Public Health Nutr 2002; 5:
567–587.
34 Gleason PM, Harris J, Sheean PM, Boushey CJ, Bruemmer B. Publishing nutrition
research: validity, reliability, and diagnostic test assessment in nutrition-related
research. J Am Diet Assoc 2010; 110: 409–419.
35 Block G, Hartman AM. Issues in reproducibility and validity of dietary studies.
Am J Clin Nutr 1989; 50: 1133–1138.
36 Freedman LS, Commins JM, Moler JE, Arab L, Baer DJ, Kipnis V et al. Pooled results
from 5 validation studies of dietary self-report instruments using recovery
biomarkers for energy and protein intake. Am J Epidemiol 2014; 180: 172–188.
37 Kaaks RJ. Biochemical markers as additional measurements in studies of the
accuracy of dietary questionnaire measurements: conceptual issues. Am J Clin
Nutr 1997; 65: 1232S–1239S.
38 Arab L, Akbar J. Biomarkers and the measurement of fatty acids. Public Health Nutr
2002; 5: 865–871.
39 Paxton AE, Strycker LA, Toobert DJ, Ammerman AS, Glasgow RE. Starting the
conversation: performance of a brief dietary assessment and intervention tool for
health professionals. Am J Prev Med 2011; 40: 67–71.
40 Willett WC (ed). Nutritional Epidemiology 2nd edition. Oxford University Press Inc.:
New York, 1998.
41 Cade JE, Burley VJ, Warm DL, Thompson RL, Margetts BM. Food-frequency
questionnaires: a review of their design, validation and utilisation. Nutr Res Rev
2004; 17: 5–22.
42 Wakimoto P, Block G, Mandel S, Medina N. Development and reliability of brief
dietary assessment tools for Hispanics. Prev Chronic Dis 2006; 3: A95.
43 Townsend MS, Sylva K, Martin A, Metz D, Wooten-Swanson P. Improving readability of an evaluation tool for low-income clients using visual information
processing theories. J Nutr Educ Behav 2008; 40: 181–186.
44 Banna JC, Townsend MS. Assessing factorial and convergent validity and reliability of a food behaviour checklist for Spanish-speaking participants in US
Department of Agriculture nutrition education programmes. Public Health Nutr
2011; 14: 1165–1176.
45 Kristal AR, Abrams BF, Thornquist MD, Disogra L, Croyle RT, Shattuck AL et al.
Development and validation of a food use checklist for evaluation of community
nutrition interventions. Am J Public Health 1990; 80: 1318–1322.
46 Birkett NJ, Boulet J. Validation of a food habits questionnaire: poor performance in
male manual laborers. J Am Diet Assoc 1995; 95: 558–563.
47 Spoon MP, Devereux PG, Benedict JA, Leontos C, Constantino N, Christy D et al.
Usefulness of the food habits questionnaire in a worksite setting. J Nutr Educ
Behav 2002; 34: 268–272.
48 Kristal AR, Curry SJ, Shattuck AL, Feng Z, Li S. A randomized trial of a tailored, selfhelp dietary intervention: The Puget Sound Eating Patterns Study. Prev Med 2000;
31: 380–389.
49 Kristal AR, Shattuck AL, Patterson RE. Differences in fat-related dietary patterns
between black, Hispanic and white women: results from the Women's Health Trial
Feasibility Study in Minority Populations. Public Health Nutr 1999; 2: 253–262.
50 O'Reilly SL, McCann LR. Development and validation of the Diet Quality Tool for
use in cardiovascular disease prevention settings. Aust J Prim Health 2012; 18:
138–147.
51 Bailey RL, Mitchell DC, Miller CK, Still CD, Jensen GL, Tucker KL et al. A dietary
screening questionnaire identifies dietary patterns in older adults. J Nutr 2007;
137: 421–426.
52 Townsend MS, Kaiser LL, Allen LH, Joy AB, Murphy SP. Selecting items for a food
behavior checklist for a limited-resource audience. J Nutr Educ Behav 2003; 35:
69–77.
53 Greenwood JLJ, Murtaugh MA, Omura EM, Alder SC, Stanford JB. Creating a
clinical screening questionnaire for eating behaviors associated with overweight
and obesity. J Am Board Fam Med 2008; 21: 539–548.
54 Fernandez S, Olendzki B, Rosal MC. A dietary behaviors measure for use with
low-income, Spanish-speaking Caribbean Latinos with type 2 diabetes: The Latino
Dietary Behaviors Questionnaire. J Am Diet Assoc 2011; 111: 589–599.
55 Rifas-Shiman SL, Willett WC, Lobb R, Kotch J, Dart C, Gillman MW. PrimeScreen,
a brief dietary screening tool: reproducibility and comparability with both
a longer food frequency questionnaire and biomarkers. Public Health Nutr 2001; 4:
249–254.
56 Schroder H, Fito M, Estruch R, Martinez-Gonzalez MA, Corella D, Salas-Salvado J
et al. A short screener is valid for assessing Mediterranean diet adherence among
older Spanish men and women. J Nutr 2011; 141: 1140–1145.
57 Van Assema P, Brug J, Kok G, Brants H. The reliability and validity of a Dutch
questionnaire on fat consumption as a means to rank subjects according to
individual fat intake. Eur J Cancer Prev 1992; 1: 375–380.
58 Beliard S, Coudert M, Valero R, Charbonnier L, Duchene E, Allaert FA et al.
Validation of a short food frequency questionnaire to evaluate nutritional
lifestyles in hypercholesterolemic patients. Ann Endocrinol (Paris) 2012; 73:
523–529.
59 Dobson AJ, Blijlevens R, Alexander HM, Croce N, Heller RF, Higginbotham N et al.
Short fat questionnaire: a self-administered measure of fat-intake behaviour. Aust
J Public Health 1993; 17: 144–149.
60 Kraschnewski JL, Gold AD, Gizlice Z, Johnston LF, Garcia BA, Samuel-Hodge CD
et al. Development and evaluation of a brief questionnaire to assess dietary fat
quality in low-income overweight women in the Southern United States. J Nutr
Educ Behav 2013; 45: 355–361.
61 Heller RF, Tunstall Pedoe HD, Rose G. A simple method of assessing the effect of
dietary advice to reduce plasma cholesterol. Prev Med 1981; 10: 364–370.
62 Taylor A, Wong H, Wish K, Carrow J, Bell D, Bindeman J et al. Validation of the
MEDFICTS dietary questionnaire: A clinical tool to assess adherence to American
Heart Association dietary fat intake guidelines. Nutr J 2003; 2: 4.
63 Teal CR, Baham DL, Gor BJ, Jones LA. Is the MEDFICTS rapid dietary fat screener
valid for premenopausal African-American women? J Am Diet Assoc 2007; 107:
773–781.
64 Mochari H, Gao Q, Mosca L. Validation of the MEDFICTS dietary assessment
questionnaire in a diverse population. J Am Diet Assoc 2008; 108: 817–822.
65 Francis H, Stevenson R. Validity and test–retest reliability of a short dietary
questionnaire to assess intake of saturated fat and free sugars: a
preliminary study. J Hum Nutr Diet 2013; 26: 234–242.
66 Godin G, Belanger-Gravel A, Paradis A-m, Vohl M-C, Perusse L. A simple method to
assess fruit and vegetable intake among obese and non-obese individuals. Can J
Public Health 2008; 99: 494–498.
67 Kristal AR, Vizenor NC, Patterson RE, Neuhouser ML, Shattuck AL, McLerran D.
Precision and Bias of Food Frequency-based Measures of Fruit and Vegetable
Intakes. Cancer Epidemiol Biomarkers Prev 2000; 9: 939–944.
68 Mainvil LA, Horwath CC, McKenzie JE, Lawson R. Validation of brief instruments
to measure adult fruit and vegetable consumption. Appetite 2011; 56:
111–117.
69 Francis H, Stevenson R. Validity and test–retest reliability of a short dietary
questionnaire to assess intake of saturated fat and free sugars: a
preliminary study. J Hum Nutr Diet 2012; 26: 234–242.
70 Kim DJ, Holowaty EJ. Brief, validated survey instruments for the measurement
of fruit and vegetable intakes in adults: a review. Prev Med 2003; 36:
440–447.
71 Atkinson G, Nevill A. Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sports Med 1998; 26: 217–238.
72 Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat
Methods Med Res 1999; 8: 135–160.
73 Whitton C, Nicholson SK, Roberts C, Prynne CJ, Pot GK, Olson A et al. National Diet
and Nutrition Survey: UK food consumption and nutrient intakes from the first
year of the rolling programme and comparisons with previous surveys. Br J Nutr
2011; 106: 1899–1914.
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