A Systematic Review of Brief Dietary Questionnaires Suitable for Clinical Use in the Prevention and Management of Obesity, Cardiovascular Disease and Type 2 Diabetes

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 (o35 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, Po0.001 to 0.79, Po0.002. 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 (2015) 69, 977–1003; doi:10.1038/ejcn.2015.6; published online 25 February 2015

INTRODUCTION changes, based upon the patient’s current dietary habits, can be 5 The World Health Organization estimates that in 2008, 18.3 million discussed and food-based dietary goals set. For dietary tools to deaths worldwide were due to cardiovascular disease and type 2 be useful in clinical practice, they need to be interpretable with diabetes.1 In 2010, unhealthy dietary habits, including low fruit minimal nutrition knowledge, quick to complete and easy to and vegetable consumption, high salt intake and low whole-grain score. They must provide immediate guidance on healthy dietary and fish consumption, combined with physical inactivity, are changes or allow clinicians to quickly identify patients who may estimated to account for 10% of the global burden of disease. benefit from more intensive dietary counselling. Dietary screening 3,6,7 Assisting people with dietary modification is, therefore, a key tools have been designed to assess specific foods or nutrients, dietary behaviours associated with obesity8 or cardiovascular challenge for health professionals. – In clinical care, dietary assessment is important for providing disease,9 11 adherence to specific diets12,13 or as specific aids in individualised dietary advice2 and is essential for evaluating the dietary counselling with a prompt sheet provided to guide success of interventions aimed at improving dietary habits, such discussion.14,15 They take the form of short food frequency as cardiac rehabilitation programs.3 Dietitians typically use food questionnaires (FFQs), with16 or without17 portion estimates, diaries and take diet histories to obtain an overview of a patient's behavioural questionnaires18 or a combination of FFQ and usual diet, with dietary advice then given based on this behavioural questions.7 They are unable to give estimates of assessment. This process is time-consuming and interpretation absolute intake but can classify individuals as high, medium or low requires specialist skills.2 However, a highly detailed assessment of consumers of nutrients or foods of interest, allowing dietary nutrient intake is not always necessary in a clinical setting. It is advice to be targeted to an individual. Questionnaires have also often enough to review an individual’s dietary habits to determine been developed to rapidly evaluate the success of dietary the potential benefit of changing specific dietary behaviours and interventions, for example, to measure the effect of advice to foods/food groups.4 increase fruit and vegetable intake19 or follow a lipid-lowering Brief dietary screening tools have been developed to assist with diet.20 These are responsive to change and can provide outcome dietary assessment in clinical practice. These tools take the form of data to determine whether an intervention has succeeded in a brief questionnaire that can be self-completed prior to, or improving dietary habits. Brief questionnaires are of interest to administered during, a consultation. The answers allow health dietary researchers,21 but the current review focuses on instru- professionals and patients to quickly identify whether a diet is ments that might be applicable in a clinical setting to obtain a appropriate or whether there are areas of concern. Dietary picture of an individual’s diet.

1Centre 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: [email protected] Received 4 March 2014; revised 4 December 2014; accepted 5 January 2015; published online 25 February 2015 A systematic review of brief dietary questionnaires CY England et al 978 A review of brief dietary assessment tools for potential clinical a 25-item questionnaire,28 10 min for 31-item,29 20-item9 and 16- use was published in 2000,22 but many additional tools have been item10 questionnaires and 5–10 min for a 29-item questionnaire.5 developed since then and there is a need for an update. More Taking these measures into account, it was estimated that recently, the US National Cancer Institute published an online questionnaires of up to 35 items could feasibly be completed in registry of validated brief dietary assessment instruments.23 15 min. Tools designed to be administered by a practitioner or Although the registry provides an overview of the tools, it does completed independently by the patient were both included. not facilitate comparisons and provides no summarised informa- Tools that assessed micronutrient intakes, protein intake, tion about applicability to clinical practice. malnutrition screening tools or those aimed at identifying Our aims were to: (i) identify and describe available brief dietary hazardous drinking were excluded. Questionnaires for single food screening tools that can be used in clinical practice for the groups, such as oily fish and pulses and fruit and vegetable prevention and management of obesity, cardiovascular disease questionnaires containing over 10 items, were considered to be of and type 2 diabetes in adults; (ii) examine the acceptability, limited use in clinical practice and were excluded. Studies were reliability and/or relative validity of the tools; and (iii) summarise excluded if they only reported the use of a questionnaire during the data so that clinicians can quickly assess which tool is most an intervention or observational study, or described tools that suitable for use with their patient group. Details are also provided were not tested for either reliability or relative validity. Owing to about the availability of the tools and whether there are costs the limitations of time and cost, studies not published in English associated with their use. 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 METHODS inclusion and exclusion criteria is available in the Supplementary Search strategy Information (Appendix 1). Electronic databases MEDLINE, EMBASE, PsycINFO, AMED (Ovid versions) and CINAHL (EBSCOhost version) to June 2013 (week 26) Data extraction were searched using MeSH terms and text words. Search terms The data extraction form was developed by all authors and piloted were based around general terms for nutritional and dietary with four studies. One author (CE) extracted data from all studies. assessment, and were designed to identify brief questionnaires. Data from 25% of studies were also extracted by an independent Terms included nutrition assessment, diet screen, food ques- reviewer for cross-checking. tionnaire, 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 Study characteristics abstracts. Full text articles were retrieved if abstracts appeared to The following data were extracted: study design, study setting, meet the inclusion criteria. Additional studies were identified from sample size, population and country. Age, gender, socio-economic reference lists and screened similarly. Studies were initially status, education, disease state and ethnicity may all impact on assessed for inclusion by one author (CE). Where it was unclear the results of a relative validation study.32 As such, the sample whether a study or questionnaire met the inclusion criteria a profiles were categorised. second author (JT) screened the reports. Questionnaire characteristics Inclusion and exclusion criteria Data were collected on the number of items, type of questions, Dietary habits or foods relevant to adults at risk for cardiovascular scoring system and the language of the tool, the method of disease, overweight, obesity or type 2 diabetes were derived from administration and whether the tool was designed for a specific national and international guidelines.24–26 Risk increases with high population or for use in a particular setting. consumption of energy-dense foods, trans-fats, saturated fats, sodium and alcohol, and decreases with high consumption of Questionnaire items fi fi high bre foods, fruit and vegetables, sh and low glycaemic Data were extracted on item generation as it is important to know index foods. Dietary patterns emphasising high fibre foods, low fat fi whether a questionnaire has been tailored to the population of dairy, poultry, sh, non-tropical vegetable oils and nuts, whereas interest.4 Data were extracted on whether a questionnaire had limiting red and processed meats and high fat or sugar foods and been tested for acceptability (face validity, ease of use or an drinks, are advised. Questionnaires assessing components of the assessment of usefulness) and readability. 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 self- Reliability and relative validity reported measure of dietary intake (dietary reference). In common Results were extracted from test-retest reliability studies determin- with the previous review,22 sample size was not considered. On ing whether tools were consistent over two or more the basis of the clinical expertise of two authors (CE, RA), tools administrations,33 and from internal reliability studies determining were deemed to be practical for clinical settings if they were brief, whether items measuring the same dietary characteristic were available in paper format or freely accessible on the Internet, could consistent within a tool.34 Data from relative validity studies were be scored at administration without specialist computer software extracted. In true validation studies, a new measure is compared and were capable of providing immediate feedback to patients and with an accurate measurement of the truth, but this is very practitioners on an individual level. Questionnaires were defined as difficult for habitual diet.35 The gold standard for dietary intake is a ‘brief’ if they were estimated to take no more than 15 min to recovery biomarker such as doubly labelled water, for energy complete. Mean allocated appointment times for new patients in intake, or urinary nitrogen for protein.36 These are expensive to primary care have been reported as being between 16 and 32 min administer, only available for a limited number of nutrients and and complete physicals as 12–36 min.27 Consequently, question- inappropriate for brief questionnaires that do not measure the naires taking more than 15 min to complete were judged as not whole diet. Even direct observation is unsuitable as a true measure feasible for use in clinical practice. However, most studies did not of habitual diet in free living individuals owing to the need for estimate completion time. Preliminary work, prior to conducting 24-h, possibly covert, surveillance. Consequently, short dietary the full review, identified mean completion times of 15 min for assessment tools are evaluated against imperfect reference

2565 records identified through 7 additional records identified database searching through hand searching references

1802 records after duplicates removed 1680 records excluded

75 full-text articles excluded: 122 full-text articles • Questionnaire is too long = 25 assessed for eligibility • 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 and 0.72 for vegetables.41 Test-retest reliability studies for long example, food diaries, a longer FFQ or 24-h recalls; a concentration FFQs quote coefficients of 0.50–0.70 for energy, fat and selected biomarker such as plasma vitamin levels,37 or biomarkers of pre- micronutrients.41 clinical disease38 such as blood lipids or anthropometric measures. The practice of only examining the correlations between scores None of these are true measures of habitual intake. Dietary to determine test-retest reliability or validity has been criticised, measures are subject to measurement error, which vary depend- and it has been recommended that the Bland Altman method be ing upon the method. For example, those reliant on memory, such used in conjunction.33 Details of the statistical tests used were as FFQs, are subject to recall bias, whereas food records can summarised. 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 RESULTS measure correlate with errors in the new measure, for example, if A total of 1802 separate records were identified, 1795 via the both methods are subject to recall bias, relative validity of the new electronic databases and a further 7 from hand searching references. measure could be overestimated.35 Concentration biomarkers and One hundred and twenty-two full text papers were screened and 47 biomarkers of pre-clinical disease are affected by metabolic and met the inclusion criteria (Figure 1). The development and testing of lifestyle factors. For example, levels of plasma β-carotene are not 35 tools were described in these papers, although 2, the Block Fat, only determined by dietary intake but also by fat intake, body Fruit and Vegetable Screeners (B-F&FV)6 and the Hispanic Fat, Fruit mass index (BMI), low-density lipoprotein levels and smoking.37 and Vegetable Screeners (H-F&FV),42 can be split into two distinct However, these biomarkers can provide additional evidence of sets of questions that provide scores for different aspects of the diet. accuracy of a questionnaire when used in conjunction with other In addition, two different versions of two tools, the Rapid Eating reference measures. Assessment for Patients (REAP29 and REAP-S14)andtheFood Internal reliability is typically tested using Cronbach’s α, which Behaviour Checklist (FBC-T10 and FBC-V43), are currently available, assesses how closely items correlate with each other.34 Values of and the FBC-V has been translated into Spanish (FBC-SV) and 40.70 indicate high internal reliability, although strong correla- evaluated32,44 One, the Fat Related Diet Habits Questionnaire tion between items in a dietary questionnaire may not be required (FRDHQ), appears to have been used in several different versions. if each item is designed to assess different aspects of the diet.39 Papers describing the relative validity testing of the 20-item and Test-retest reliability and relative validity are commonly tested at 24-item questionnaires are detailed here21,45–47 although 21-48 the individual level using correlation statistics.35 The use of mean and 23-item49 versions have been used in interventions. The values alone can only assess these at the group level.40 Correlation current version, available online, contains 25 distinct items (http:// coefficients of ⩾ 0.4 for the nutrient of interest are considered to sharedresources.fhcrc.org/documents/fat-related-questionnaire). be adequate for FFQs when compared with another dietary For the purposes of this review, B-F&FV and H-F&FV were regarded reference measure.4 Correlations of ⩽ 0.4 are more usual when as single tools, REAP and REAP-S and FBC-T and FBC-V were FFQs are compared with a biomarker.37 Studies calibrating long regarded as distinct tools, with FBC-SV as a subsidiary to FBC-V. All FFQs against other dietary assessment methods, such as food the versions of FRDHQ were regarded as one tool. diaries, have reported coefficients between − 0.16 and 0.86 for Table 1 summarises the study and tool characteristics. Over half total fat in grams (mean 0.51), − 0.01 and 0.71 for fruit and 0.16 (n = 20) were developed and tested in the USA or Canada with the

Table 1. Tool and study characteristics

Healthy eating Australian Diet Quality Tool i) Validationb i) Cardiac rehab NR i) 13.5% women; age = 61.2 (10.8); English 1 total score derived by summing responses questionnaires dietary brief of review systematic A (DQT) (n = 37) patients NR BMI = 28.7 (4.1) 13 questions (food Higher scores indicate more desirable habits O'Reilly and McCann50 ii) Acceptabilityc ii) Health ii) NR frequency and behavioural) Cut-offs used to define diets as healthy/unhealthy Australia (n = 33) professionals Portions described by 5 subscale scores (‘F+V’‘saturated fat’ and total fat’, (n = 25), cardiac household measures ‘omega 3 s’‘fibre’ and ‘salt’) can also be calculated rehab patients 11 min to complete by summing appropriate responses (n = 8) Unspecified timescale Bailey Elderly Food Screener 1) i) Item 1) General 1) 99% White American 1) i) 54.7% women; age = 73.0 (5.0); 80% English 1 total score derived by summing responses (B-Elder) generation; community 2) 98% White American high school education 25 questions (food Higher scores indicate more desirable habits 1) Bailey et al.51 validation 2) General ii) ‘similar’ gender distribution to other frequency and behavioural) Cut-offs used to define diets as healthy/unhealthy =

– USA (n 179) community samples Portions are not described 28 03©21 amla ulsesLimited Publishers Macmillan 2015 © 1003 2) Bailey et al. ii) Acceptability 2) 59.7% women; age = 78.5 (4.0); 82.0% 15 min to complete USA (n = 17) high school education Diet over last month 2) Validation (n = 206) England CY

Food Behaviour Checklist 1) Item 1) General 1) 46% African American; 1) 100% women; age = 32.9 (8.9); mean English 7 subscale scores are calculated (‘Fruit and (FBC) Text version (FBC-T) generation; community 23% Hispanic; 21% White of 12 years education; low SES 16 questions (food vegetables’, ‘Milk’, ‘fat and cholesterol’, ‘diet quality’ 1) Murphy et al.10 internal 2) General American; 3% Native 2) Test–retest sample was a subset of frequency and behavioural) and ‘food security’) by summing responses in that

USA reliability; community American; 7% other validation sample; internal reliability Portions are not described category and dividing by the number of questions al et 2) Townsend et al.52 validation 2) NR redone 10 min to complete Higher scores indicate more desirable habits USA (n = 100) Unspeciﬁed timescale 2) Item generation; test– retest; internal reliability (n = 44) Food Behaviour Checklist, 1) i) Acceptability 1) i) General 1) i) ‘English speaking, non- 1) i) 84% women; low SES English As FBC-T visually enhanced version (n = 43) community Hispanic black, non- ii) No details Includes photographs (FBC-V) ii) Acceptability ii) University; Hispanic white, and 16 questions (food 1) Townsend et al.43 (n = 15) work site Hispanic clients’ frequency and behavioural) USA ii) Academic nutrition staff Portions are not described (‘professionals’)(n = 6); No completion time Nutrition educators estimated (‘paraprofessionals’) Unspeciﬁed timescale (n = 10)

Spanish translation of FBC-V 2) Acceptability 2) General 2) 95% Hispanic 2) n = 20 (100% women) (face validity) Spanish (USA) translation of As FBC-T (FBC-SV) (n = 20) community 3) Hispanic 3) i) 100% women; low income FBC-V 2) Banna et al.32 3) i) Test–retest 3) General ii) 100% women; age = 36; BMI = 31.1 USA (n = 71) community (6.7); low income 3) Banna and Townsend44 ii) Validation iii) Validation and reliability sample USA (n = 82) combined for internal reliability iii) Internal reliability (n = 153)

Healthy Eating Vital Signs 1) i) Acceptability 1) Primary care 1) i) 79% White American 1) i) 55.4% women); age = 42.6 (12.1) English Individual answers are considered separately and (HEVS) (n = 48) 2) Primary care ii) 80% White American ii) 58.2% women; age = 38.4 (11.7); 14 questions (food no scores are calculated 1) Greenwood et al.8 ii) Validation clinic staff 2) 54% White American; BMI = 27.7 (7.2); mean number of years frequency and behavioural) USA (n = 261) 25% Hispanic of schooling = 15.7 (3.4) Soft drink portions 2) Greenwood et al.53 2) Validation; 2) 93.3% women; age = 38.3 (9.6); 68% described as cans USA internal reliability BMI425; 100%4high school education 1 min to complete (n = 60) Both 1 day recall (yesterday) and typical recall with no timescale 05McilnPbihr iie uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Limited Publishers Macmillan 2015 ©

Table. 1. (Continued )

Latino Dietary Behaviors Item generation; Primary care 100% Hispanic 76.6% women; age = 55.2 (11.2); Spanish (USA and Central 1 total score derived by summing responses Questionnaire (LDBQ)d internal BMI = 34.8 (7.0); about 75%ohigh America) Higher scores indicate more desirable habits Fernandez et al.54 reliability; school education; 50% household 13 questions (food USA validation income o$10 000 frequency and behavioural) (n = 252) Portions are not described No completion time estimated Unspeciﬁed timescale

PrimeScreen Test–retest; Primary care 63% White American, 31% 56.9% women; age = 48.0 (range, 19–65); English In clinic individual answers are coded using trafﬁc Rifas-Shirman et al.55 validation; African American BMI = 27.3 (range, 15.5–57.7); 59% 15 food-based questions light codes (red, yellow, green) for food items USA acceptability college graduates; 59% executive or (food frequency and (It is possible to calculate nutrient intakes for (n = 160) professional behavioural)+8 questions research purposes but this requires population on vitamin/mineral speciﬁc nutrient and food consumption databases) supplements Portions are not described 5 min to complete Diet over last year

Rapid Eating Assessment for i) Acceptability i) Physicians and i) NR i) NR English In clinic individual answers are considered Patients (REAP) (n = 61) medical ii) NR ii) NR 31 questions (behavioural) separately and a ‘physician key’ is provided to Gans et al.29 ii) Validation students iii) 50% ‘people of colour’ iii) 62% female; age = 32 (range, 20–60); Portions described by guide discussion and advice USA (n = 44) ii) iv) 94% White American 96% some college education; 76% weight iii) Acceptability Undergraduates household incomeo$76 000 10 min to complete (n = 31) iii) Work site; iv) 57.4% women; age = 43.2 (12.5); 57% Unspeciﬁed timescale

iv) Test–retest; students completed high school; median income questionnaires dietary England brief CY of review systematic A validation iv) General range $51000-$60 000 (n = 94) community

Rapid Eating Assessment for Validation Undergraduates 65% White American, 21% 44.5% women; age = 24.2 (3.8); English As REAP Patients short form (REAP-S) (n = 49) Asian BMI = 23.4 (5.0); some college 16 questions (behavioural) tal et Segal-Isaacson et al.14 Portions described by USA weight No completion time estimated Unspeciﬁed timescale Short Diet Quality Screener Validation General NR n = 102 (49% women); age = 58.6 (12.1); Spanish (European) 1 total score derived by summing responses (sDQS) (n = 102) community BMI = 27.6 (4.2); 62.7%4primary school 18 questions (food Higher scores indicate greater adherence to Schroder et al.13 education frequency) healthy eating Spain Portions described in household measures No completion time estimated Diet over last year Mediterranean diet Brief Mediterranean Diet Developed in the same population as the sDQS Spanish (European) 1 total score derived by summing responses Screenerd (bMDSC) 15 questions (food Higher scores indicate greater adherence to Schroder et al.13 frequency) Mediterranean diet Spain Portions described in household measures No completion time estimated Diet over last year – 1003 981 982 uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European

Table. 1. (Continued )

Tool name Purpose of studya Recruitment Ethnicity Sample characteristics Tool characteristics Test scoring and outcome Author(ref) (number of setting (age (years) and BMI (kg/m2) reported as (language, number of Country participants) mean values (standard deviation, when questions, portion estimates, available) unless otherwise indicated) time to complete, timescale) ytmtcrve fbifdeayquestionnaires dietary brief of review systematic A Mediterranean Diet Validation Primary care NR 57.2% women; age = 67; BMI = 30 Spanish (European) 1 total score derived by summing responses Adherence Scored (MEDAS) (n = 7146) 14 questions (food Higher scores indicate greater adherence to Schroder et al.56 frequency and behavioural) Mediterranean diet Spain Portions described in household measures No completion time estimated Unspeciﬁed timescale Total fat Dutch fat consumption i) Test–retest General NR i) 52.1%; age range = 18–93 Dutch 1 total score derived by summing responses d = = – – questionnaire (D-Fat1) (n 639) community ii) 55.8% women; age range 21 68 25 questions (food Lower scores indicate lower fat diet

03©21 amla ulsesLimited Publishers Macmillan 2015 © 1003 Van Assema et al.57 ii) Validation frequency and behavioural) The Netherlands (n = 52) Portions described in household measures YEngland CY No completion time estimated Diet over last 6 months

Fat-Related Diet Habits 1) Test–retest; 1) Primary care 1) NR 1) 100% women; age = 51.5 (4.3); English 1 total score derived by summing responses Questionnaire/Kristal’s Food internal 2) Work site 2) NR BMI = 24.5 (3.5); 60.0% completed 20/25 questions 5 behavioural subscales can be calculated by al et Habits Questionnaire (FRDHQ) reliability; 3) i) Work site 3) NR college; 57.2% household income (behavioural) summing responses in that category and dividing 20 item version validation ii) Primary care 4) 82% White American 4$40 000 Interview administered and by the number of questions 1) Kristal et al.11 (n = 97) 4) Work site 2) 100% men; age = 41.0 (9.8); BMI = 28.5 self- administered versions Lower scores indicate lower fat habits USA 2) Internal (4.4); 100% manual workers; mean of both older 20 item and 2) Birkett and Boulet46 reliability; number of years education = 12.4 (3.3); newer 25 item available Canada validation 56.9% household income4CA$40 000 Portions are not described 3) Glasgow et al.21 (n = 354) 3) i) ‘majority blue collar’ No completion time USA 3) i) Validation ii) 60% women; age = 63 estimated 24-item version (n = 1022) iii) Test–retest samples were subsets of Diet over last month 4) Spoon et al.47 ii) Validation validation sample USA (n = 105) 4) i) 40.0% women; age = 40.7 (10.6); iii) Test–retest BMI = 27.1 (27.1); 24% completed (n = 89/39) college; 29%o$12 000 4) i) Internal ii) Test–retest sample was a subset of reliability internal reliability sample (n = 178) iii) Validation was a different subset of ii) Test–retest internal reliability sample (n = 42) iii) Validation (n = 32) Short Fat Questionnaire (SFQ) i) Acceptability NR NR i) NR English 1 total score derived by summing responses Dobson et al.56 ii) Validation ii) General ii) NR 17 questions (food Lower scores indicate lower fat diet Australia (n = 90) community; iii) Test–retest sample is a subset of the frequency and behavioural) iii) Test–retest work site validation sample Portions are not described (n = 25) 3 min to complete Unspeciﬁed timescale 05McilnPbihr iie uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Limited Publishers Macmillan 2015 ©

Table. 1. (Continued )

Sister Talk Food Habits (short Internal Primary care 100% African American 100% women (49 participants English 1 total score derived by summing responses then form) (SisterTalk-S) reliability; completed Sister Talk at phase 1 and 2 30 questions (food dividing by the number of non-missing questions Anderson et al.3 validation but test–retest not calculated) frequency) Lower scores indicate lower fat habits USA (n = 95) Portions are not described No completion time estimate Diet over the last 3 months

Starting the Conversation i) Validation; Primary care NR i) 49.7% women; age = 58.4 (9.2); English 1 total score derived by summing responses (STC) internal reliability BMI = 34.8 (6.5); 19.1% high school or 8 questions (food frequency Lower scores indicate more desirable habits Paxton et al.39 (n = 372) less; 47.3% household income and behavioural) USA ii) Test–retest o$49 999 Portions are not described (n = 114) ii) Test–retest was a subsample of No completion time validation study estimate Diet over the ‘past few months’ Specific dietary fats and/or dietary cholesterol Dietary Fat Quality i) Validation Primary care 51% White American; 48% i) 100% women; age = 51.0 (0.7); English 1 total score derived by summing responses Assessment (DFA) (n = 120) African American BMI = 38 (0.4); 65% low SES; 60% high 20 questions (food Higher scores indicate lower fat diet Kraschnewski et al.60 ii) Test–retest school education or less frequency) USA (n = 96) ii) Test–retest sample was a subset of the Portions described as validation sample undefined ‘servings’ 6 min to complete Unspecified timescale

Heart Disease Prevention i) Test–retest Work site NR i) 100% men English 1 total score derived by summing responses questionnaires dietary England brief CY of review systematic A Project Screener (HDPPS) (n = 22) ii)100% men 10 questions (food Lower scores indicate lower fat diets Heller et al.61 ii) Validation frequency and behavioural) UK (n = 68) Portions described by weight

No completion time al et estimate Unspecified timescale MEDFICTS (Meats, Eggs, Dairy, 1) i) Validation 1) i) Primary 1) NR 1) i) NR English 1 total score derived by summing responses Fried foods, fat In baked (n = 22) care 2) 65.9% White American ii) NR 20 questions (food Lower scores indicate greater adherence to diet goods, Convenience foods, ii) Validation ii) Primary care 3) 100% African American iii) NR frequency) Cut-offs used to define diets as adherent/non- fats added at the Table and (n = 26) iii) Pre-existing 4) 64.4% White American; 2) 20.1% women; age = 42.0 (2.0); Portions described by adherent Snacks) iii) Validation food diaries 24% Hispanic BMI = 27.0 (4.0)78.4% college educated weight and people are 1) Kris-Etherton et al.12 (n = 16) 2) Armed forces 3) n = 184 (100% women); age = 36.7 asked to indicate ‘small’, 2) Taylor et al.62 2) Validation 3) Primary care (5.3); BMI = 30.7 (6.9) ‘medium’ or ‘large’ servings 3) Teal et al.63 (n = 164) 4) Primary care 4) 65.9% women; 96.4% high school or No completion time 4) Mochari and Gao64 3) Validation greater estimated USA (n = 184) Unspecified timescale 4) Validation (n = 501)

NLSChol Questionnaired i) Acceptability Primary care NR i) 45% women; age = 60.9 (15.5); French 1 total score derived by summing responses Beliard et al.58 (n = 131) BMI = 26.9 (6.5) 11 questions (food Lower scores indicate greater adherence to diet France ii) Test–retest ii) NR frequency) Cut-offs used to deﬁne diets as adherent/non- (n = 20) iii) 39.7% women; age = 58.0 (16.0); Portions described by adherent iii) Validation BMI = 27.0 (8.0) weight (n = 58) iv) 56.9% women; age = 56.0 (12.0) 5 min to complete iv) Internal Unspeciﬁed timescale

– reliability 1003 (n = 1048) 983 984 uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European

Table. 1. (Continued )

Northwest Lipid Research Test–retest; Work site 90% White American 37.4% women; age = 42.3; BMI = 27.7; English 1 total score derived by summing responses Clinic Fat Intake Score (NWFIS) validation 95.5% high school or greater 12 questions (food Lower scores indicate lower fat diet questionnaires dietary brief of review systematic A Retzlaff et al.20 (n = 310) frequency and behavioural) Cut-offs used to deﬁne diets as high/low in fat and USA Portions described by cholesterol weight 3 min to complete Diet over the last month

Rate Your Plate (RYP) Validation Primary care 23.5% Portuguese heritage 57.8% women; age = 38.1 (13.1); English 1 total score derived by summing responses Gans et al.15 (n = 102) BMI = 26.5 (5.9); 83.3% completed high 23 questions (food Higher scores indicate healthier choices USA school frequency and behavioural) Cut-offs used to deﬁne diets as healthy/unhealthy Portions described by

– weight

03©21 amla ulsesLimited Publishers Macmillan 2015 © 1003 No completion time estimated Unspecified timescale Total and saturated fat and free sugar England CY Dietary Fat and Free Sugar i) Validation; Undergraduates 75% Australian i) 60% women; age = 21.3 (5.8); English 1 total score derived by summing responses Short Questionnaire (DFFQA) internal reliability BMI = 23.4 (3.4); 100%4high school 26 questions (food 3 subscales can be calculated by summing Francis and Stevenson69 (n = 40) education frequency) responses for that category Australia ii) Test–retest ii) Test–retest sample was a subset of Portions are not described Lower scores indicate lower sugar/fat (n = 29) validation sample: 62% women 5 min to complete Suggested cut-off used to define diets as high/low al et Unspecified timescale in undesirable foods

Dietary fats and fibre Dietary Instrument for Validation Work site ‘majority white’ 38% women; age = 44.8 (range, 17–62); English 3 subscale scores (‘total fat’, ‘total fibre’, Nutrition Education (DINE) (n = 206) BMI = 25.7; 66% skilled manual workers 29 questions (food ‘unsaturated fat’) are derived by summing relevant Roe et al.5 frequency) items UK Portions described by Lower scores for fat and unsaturated fat indicate household measures, low fat diet volumes and undefined Higher scores for fibre indicate high fibre diet ‘servings’ Cut-offs used to identify diets as high/low in fat/ 5–10 min to complete fibre Unspecified timescale Fat and Fibre Barometer (FFB) i) Test–retest General NR i) 47.8% women; higher than average English 1 total score derived by summing responses Wright and Scott9 (n = 115) community/ education 20 questions (food Higher scores indicate lower fat/higher fibre diet Australia ii) Validation work site ii) Validation sample was a subset of frequency and behavioural) People are encouraged to consider changes in (n = 98) test–retest sample; 52.0% women Portions described by questions where they scored 3 or less household measures or undescribed 10 min to complete Unspecified timescale Fat and Fibre Diet Behaviour i) Item i) ‘Convenience i) NR i) 1 focus group and 2 convenience English Total fat score derived by summing relevant Questionnaire (FFDBQ) generation samples’ ii) 93% White American samples of approximately 100 each; 29 questions (behavioural) responses Shannon et al.18 (n ⩾ 200) ii) Primary care ii) 68.0% women; age = 51.0; 50% Portions are not described 5 fat subscale scores can also be calculated USA ii) Validation college educated No completion time Lower scores for fat indicate lower fat diet (n = 1795) iii) Test–retest sample was a subset of estimated Total fibre score derived by summing relevant iii) Test–retest validation sample Diet over the last 3 months responses (n = 943) 3 fibre subscale scores can also be calculated Higher scores for fibre indicate higher fibre diet 05McilnPbihr iie uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Limited Publishers Macmillan 2015 ©

Table. 1. (Continued )

Norwegian SmartDiet i) Test–retest; Primary care; NR i) 60.4% women; age = 51 (range, 28–52); Norwegian 1 total score derived by summing responses Questionnaired (N-Smart) acceptability work site BMI = 26.5 (4.8) 15 questions (food Lower scores indicate less healthy choices Svilaas et al.7 (n = 111) ii) Validation study was a subsample of frequency and behavioural) Cut-offs used to deﬁne diets as healthy/unhealthy Norway ii) Validation the test–retest sample; 61.4% women Portions described by (n = 101) weight 9 min to complete Unspeciﬁed timescale

Total fat and fruit and vegetables Block Fat, Fruit and Vegetable Validation Work site 65% White American; 22% 64.4% women; age = 41 English Fat and fruit and vegetable subscale scores derived Screeners (n = 208) Asian/Paciﬁc Islander; 7% 17 questions on fat (food by summing relevant items (B-F&FV) Hispanic; 3% African frequency) Lower scores for fat indicate lower fat diet (These questionnaires can be American 7 questions on fruit and Higher scores for fruit and vegetables indicate used separately) vegetables (food frequency) higher consumption Block et al.6 Portions are not described Cut-offs used to identify diets as high/low in fat/ USA 5 min to complete fruit and vegetables Diet over the last year

Hispanic Fat and Fruit and i) Item General 100% Hispanic (‘primarily’ i) NR Spanish (USA and Central Fat screener Vegetable Screeners (H-F&FV) generation community Mexican and Mexican ii) 51.0% women; 38% aged o30years; America) 1 total score derived by summing responses (These questionnaires can be (n = 70) Americans; 91% born in 42%oeighth grade education 16 questions on fat (food Lower scores indicate lower fat diets used separately) ii) Acceptability Mexico) ii) 58.0% women; age = 36.5 (14.5) frequency) Cut-offs used to identify diets as high/low fat Wakimoto et al.42 (n = ‘almost’ 300) 7 questions on fruit and Fruit and vegetable screener USA ii) Test–retest vegetables (food frequency) Estimated number of fruit and vegetables/day (n = 93) Portions are not described calculated by summing responses and dividing by

5 min to complete 7 questionnaires dietary England brief CY of review systematic A Diet over the last months

Fruit and/or vegetables Canadian Fruit and Veg Validation General NR 56.2% women; age = 37.2 (11.5); French Estimated number of fruit and vegetables/day d Questionnaire (CFV-Q) (n = 350) community BMI = 27.7 (5.6) 6 questions (food calculated by summing servings per week and al et Godin et al.66 frequency) dividing by 7 Canada Portions described as cups/ volumes No completion time estimated Diet over the last week Dutch fruit and vegetable i) Validation General NR i) 100% women; age = 41.0 (range, Dutch Estimated number of fruit and vegetables/day questionnaired (D-F&V) (n = 157) community 29–40); BMI = 24.0 (range, 18.7–35.9); 8 questions (food calculated by summing responses and dividing by Bogers et al.19 ii) Test–retest 95% intermediate to high education frequency) 7 The Netherlands (n = 73) level Portions described by ii) Test–retest sample was a subset of the household measures validation sample 2 min completion time Diet over the last month

Five a day screener (5-F&V) 1) Validation 1) General 1) NR 1) 53.0% women; aged 450 English Estimated number of fruit and vegetables/day 1) Thompson et al.17 (n = 436) community 2) 89% White American 2) 56.9% women; age = 42.0 (range, 7 questions (food calculated by summing responses 2) Kristal et al.67 2) Validation 2) Work site 20–67); 55% had 16 or more years of frequency) USA (n = 260) education Portions are not described No completion time estimated Diet over the last month – 1003 985 A systematic review of brief dietary questionnaires CY England et al 986 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 calibration against a reference measure.

= 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 Estimated number of fruitcalculated and by vegetables/day summing responses7 and dividing by Estimated number of servingscalculated of by fruit summing per responses day Test scoring and outcome fruit intake alone.68 With the exception of questionnaires specific Validation b 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 frequencyofconsumptionofspecific foods.3,5,6,12,13,42,58,60,69 ed timescale fi All of the fruit and vegetable questionnaires were in this form.16,17,19,66,68 Four exclusively asked about food behavi- ‘ English 5 questions (food frequency) Portions are not described No completion time estimated Diet over the last month Tool characteristics (language, number of questions, portion estimates, time to complete, timescale) Dutch 10 questions (food frequency) Portions are described by household measures No completion time estimated Unspeci ours, for example, In the past month how often did you eat fish or chicken instead of red meat?’ or, ‘In an average 14,18,29,45 68); week, how often do you skip breakfast?’ The remain- – ing 16 contained a mixture of FFQ and behavioural questions.7–10,15,20,28,39,44,50,54–57,59,61 ’

) reported as 8,10,14,29,44,52,55 2 All except six were scored numerically, with a total score or subscales for separate nutrients or fruit and 45 (range, 21 38.1 (8.1); 45% = 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. low level of education ‘ Item generation high school education; low SES Item generation was described for 27 tools, with 8 employing o 50% women; age 50% (age (years) and BMI (kg/m mean values (standard deviation,available) when unless otherwise indicated) 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 c, Asian, Middle ﬁ guidelines5,10,29,53,55,56,58 and four were developed using an expert

Translated into English. 9,10,45,53 d 80% European or Other ethnicity; 11% Maori; 9% Paci Eastern, Latin American, African NR 51.0% women; age Ethnicity Sample characteristics panel. 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. Unemployment training programme General community Recruitment setting Four employed cognitive interviewing,29,32,43,51,68 three used a 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 32,43,52 100) 49) evaluated for reading comprehension. The FBC-T and FBC- = = Roman numerals indicate where different samples or sub-samples were used during different phases of tool development. a n n fi Purpose of study Validation ( Validation ( (number of participants) SV were of low reading dif culty and the colour version of the

d FBC-SV was ‘very easy’.

Reliability and relative validity

16 Table 2 summarises the results of reliability and relative validity studies. Just over half the tools (n = 18) were tested for et al. 68 7,9,18–20,29,39,42,44,52,55,57–61,69

(Continued ) test-retest reliability, with one being 21,45,47 et al. tested in three different samples. Test-retest time varied (ref) from several hours7 to 1 year18,19,57 and different studies Tool name Author Country New Zealand (SD-F&V) Van Assema The Netherlands Short Dutch questionnaire to measure fruit and vegetables Mainvil Fruit Habits Questionnaire (M-FrHQ) Mainvil

Acceptability encompasses face validity, clarity and ease of use. employed different statistical tests, although correlations were Abbreviation: NR, notc reported. Table. 1. most often used (14 tools).7,9,18–20,29,39,42,44,45,52,55,57,59 Test-retest

European Journal of Clinical Nutrition (2015) 977 – 1003 © 2015 Macmillan Publishers Limited A systematic review of brief dietary questionnaires CY England et al 987 correlation coefficients for total scores ranged from 0.5921 to 0.95.7 variation makes direct comparison between tools difficult, and Four studies did not calculate a total score but used individual as a consequence, it is not possible to state that one tool is items, group classifications or derived nutrient intakes from the superior for a particular nutrient or population. However, screener as test-retest variables.52,55,58,60 One study61 was correlation coefficients for relative validity against food diaries evaluated exclusively at the group level. Internal reliability was and biomarkers and those for reliability studies are similar to those tested in nine tools3,8,39,44,54,58,69 with two employing more than obtained in studies that evaluate longer FFQs against food diaries. one sample.10,45–47,52 Values for Cronbach’s α were reported from This indicates that these brief dietary screening tools can be 0.47 54 to 0.83.47All tools were examined for relative validity at the expected to produce a fair approximation of dietary habits and individual level against a reference measure except one.42 consequently could be of use in clinical practice for the dietary A number of different reference measures, with a range of management of cardiovascular disease, obesity and type 2 different times between tests, different test variables and different diabetes. It is worth noting, however, that few tools reported 28 55,62–64,66,68 statistical tests were used to determine relative validity. No study sensitivity, specificity or predictive values and only 3,18–20,39,54 employed a recovery biomarker. Nine tools were compared with six (17%) have assessed sensitivity to change over time; an FFQ that had previously reported relative validity against food therefore, their utility in an intervention setting is unclear. 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 Strengths and limitations of the review One was compared with a different brief questionnaire that had 39 The strengths of this review are the application of a systematic been previously tested for relative validity against 24 h recalls. search strategy and systematic data extraction techniques. Dietary Nine tools were compared with more than one reference assessment tools developed since the review by Calfas et al.22 in 8,10,12,20,21,28,29,45–47,52,53,56,62–64,69 measure; and three were com- 2000 and validated tools that are not listed in the NIC registry 12,21,29,45–47,62–64 pared with more than one dietary reference. have been identified and described. Tools that were not included 10,28,56,58 Alongside a dietary reference, four were compared with in study reports were obtained online or from the original authors 28,53,56,69 biomarkers of preclinical disease, four with anthropo- to ensure they met the inclusion criteria. The results are presented 10,28 metric measures, and two with concentration biomarkers. Two so that clinicians and researchers can select available tools that are did not use a dietary reference measure but compared change in most suitable for their purposes. 3 total score with change in BMI and change in total score with The review has some important limitations. The piloting 19 change in plasma carotenoids and plasma vitamin C. The and use of dietary screening tools in practice has not been variation in study designs makes direct comparisons between examined, which means it is not possible to determine whether tools problematic, but total score (or fat score) from 11 use of a tool has a positive effect on patient behaviour. – – tools5,9,12,15,18,20,21,29,45 47,54,59,62 65 were reported to have been The inclusion and exclusion criteria were developed for this compared with % energy from total fat from food diaries or FFQs. review and assessment of whether a tool would be useful in Correlation coefficients ranged from 0.2446 to 0.79.12 Total scores clinical practice was derived from the expert opinion of only two from two of these tools were compared with % energy from total clinicians. Other reviewers or clinicians may disagree with the fat from a dietary reference in more than one population: the criteria and may have included or excluded different brief tools. FRDHQ reported correlation coefficients ranging from 0.2446 to 0. Calfas et al.22 judged that tools suitable for use in primary care 6045 and MEDFICTS from 0.3063 to 0.79.12 would take 15 min to complete or be around 50-items long but Table 3 gives an ‘at a glance’ summary of the characteristics of provided no justification for this. The current review based an each tool, the evaluation studies and provides information on estimate of completion time on preliminary data obtained from access. brief dietary questionnaires. We excluded tools assessing single food groups because there is limited clinical benefitina detailed assessment of one food group, with the exception DISCUSSION of fruit and vegetable intake. However, fruit and vegetable Main findings questionnaires of greater than 10 items were excluded because This systematic review identified 35 tools with potential applica- increased patient burden reduces feasibility in clinical practice. tion to dietary assessment in clinical settings. Around half assess Only peer-reviewed studies published in English were included. dietary fat intake, with or without other nutrients, a third assess There may be evaluated tools that are used in clinical practice in the overall diet for healthy eating or adherence to the other countries, or that have not been peer-reviewed that have fi Mediterranean diet, and the remainder assess fruit and vegetable not been identi ed here. However, owing to the heterogeneity of intake. More tools have been developed and evaluated in the USA studies, this would be unlikely to change the broader conclusions than in any other country. of this review. Fewer than half the tools reported evaluations for clarity of language and acceptability with users. Owing to the variation in Comparison with other studies methodology, it is not possible to determine whether the tools The review by Calfas et al.22 used wider inclusion criteria than this that were evaluated for acceptability show greater reliability or current review and did not consider whether a tool could be easily relative validity than those that were not. However, best practice scored in practice. They identified 14 dietary assessment tools, of 41 in FFQ design involves pre-testing. which 7 are included in the present review.5,6,11,12,15,20,55 All All tools, except one, were tested for relative validity against measured dietary fat, making comparisons between tools more one or more reference measures, although there was a wide straightforward. Four were evaluated for test-retest reliability, with variation in the design of studies, the variables used and the correlation coefficients ranging from 0.67 to 0.91. The 11 validated statistical tests employed. Three quarters were tested against a tools were either validated against a food diary or a longer FFQ, different dietary reference measure, with over a quarter using a and correlation coefficients for % energy from fat ranged from FFQ or a different brief questionnaire. As the majority of brief 0.30 to 0.80. These ranges are similar to coefficients reported in questionnaires were themselves FFQs, or included many food the current review. frequency questions, errors between the tools and the FFQs may In 2003, Kim et al.70 reviewed tools reported as validated, have been correlated and the relative validity of these ques- containing up to 16 items, and designed to assess fruit and tionnaires overestimated. Around half were evaluated for test- vegetable intake. They identified 10 instruments, of which 1 is retest reliability with similar variation in study design. This included in the current review.17 The remainder were excluded in

© 2015 Macmillan Publishers Limited European Journal of Clinical Nutrition (2015) 977 – 1003 988 uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Table 2. Summary of reliability and relative validity

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests

Healthy eating questionnaires dietary brief of review systematic A Australian Diet NR NR NR NR NR 4 day unweighed Completed Total DQT score; Pearson's correlation ● Total DQT score with %E sfa, r = − 0.50; ﬁbre (g) r = 0.56; Quality Tool food diary at home DQT subscales; omega-3(mg) r = 0.33 (Po0.05). (DQT)50 within the Nutrients from ● Fibre subscale with ﬁbre (g), r = 0.42; fat subscale with same two food diaries % E sfa, r = 0.49; omega-3 subscale with weeks omega-3 (mg), r = 0.37 (Po0.05). ● No correlations for TF, vitamin C or salt subscales.

Bailey Elderly NR NR NR NR NR 1) 4 × 24 h recall, 4–6 weeks Factor scores Pearson's correlation 1) 2 patterns identiﬁed: pattern 1 = ‘prudent dietary score’; Food Screener Anthropometrics, (dietary patterns), pattern 2 = ‘Western dietary score’. (B-Elder) 28,51 Concentration MAR and

– biomarkers, nutrients from Pattern 1 correlations:

03©21 amla ulsesLimited Publishers Macmillan 2015 © 1003 Biomarkers of recalls; ● With nutrients: MAR, r = 0.37; sfa (g), r = − 0.25; CHO (g), preclinical disease biomarkers r = 0.19; ﬁbre (g) r = 0.45, Po0.001; protein (g) r = 0.25; omega-3 s (g), r = 0.16; Po0.05;TF (g), r = − 0.20 YEngland CY With biomarkers: HDL-C, r = 0.17; TGs, r = − 0.15; WC r = − 0.18, Po0.001

Pattern 2 correlations: ● With nutrients: Sugar (g), r = 0.2; protein (g), r = − 0.26; tal et fibre (g), r = − 0.20, Po0.05.With biomarkers: Serum B12 (mg), r = − 0.19 NR NR NR NR NR 2) 4 × 24 h recall, 4–6 weeks 3 categories of 2) Anthropometrics, risk from screener ● At risk group reported significantly higher consumption Dietary index, —at risk, possible of TF, sfa, transF and lower consumption fibre and Concentration risk, not-at-risk; protein. HEI and MAR were lowest in at risk group biomarkers Nutrients, MAR (corrections made for multiple comparisons, Po0.05) and HEI-2005 ● Calculated sensitivity = 83%; specificity = 75% from recalls accuracy = 79%; positive predictive value = 75%.

Food Behaviour 1) NR NR NR NR 1) Subscale 1) 3 × 24 h recall, Same visit Subscales and Spearman's correlation Multiple comparisons made with subscales/individual Checklist—text 2) 3 weeks Individual Spearman's From r = 0.35, ranges, α = 0.28 Concentration As above individual items items and nutrients, HEI score, food groups, serum version (FBC- items correlation Po0.05 (do you eat (fat and biomarkers from screener carotenoids T)10,52 more than one type cholesterol) to 2) As above Plasma of fruit/day) to 0.83, 0.79 (fruit and carotenoids; HEI Subscales Po0.001 (do you vegetables) score, nutrients ● %E TF, r = − 0.25, Po0.01 (‘diet quality’) drink regular soft (Spearman’s) and food groups ● HEI, from r = 0.20, Po0.05 (‘fat and cholesterol’, ‘food drinks). rho = 0.85 (food from recall security’) to from r = 0.32, Po0.001 (‘diet quality’). security) As above ● Serum carotenoids, r = 0.28, Po0.05 (‘fat and 2) Subscale cholesterol’)tor = 0.48, Po0.001 (‘diet quality’) ranges, α = 0.61 (diet Individual items quality) to 0.80 ● Nutrient/food groups, from r = 0.20, Po0.05 (‘one kind (F+V) 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, Po0.001 ('use nutrition labels' with vitamin A).

● Serum carotenoids, from r = 0.27 Po0.05 (‘fruit and vegetables as snacks’)tor = 0.48 Po0.001 (‘do you eat low-fat instead of high-fat foods’) ● 17 items did not correlate and were removed. 05McilnPbihr iie uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Limited Publishers Macmillan 2015 © Table. 2. (Continued )

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests

Food Behaviour 3) 3 weeks Total score ICC, Spearman's Total score, r = 0.71, Total score, 3) 3 × 24 h recall Unclear but Subscales and Spearman's correlation Multiple comparisons made with subscales/individual items Checklist— individual correlation Po0.001; Subscales α = 0.75 FBC individual items and nutrients/cups of F+V visual version, items from r = 0.48 (food Subscales from, completed from screener Spanish security) to 0.78, α = 0.49 (diet at the same Nutrients and Behavioural subscales translation Po0.001 (dairy/ quality) to time as cups of F+V from ● ‘dairy/calcium’, from r = 0.25, Po0.05 (vitamin A (RE)) (FBC-SV)44 calcium) α = 0.80 (F+V) second or recalls to r = 0.43, Po0.001 (calcium (mg)); Individual items, 2 item third recall ● ‘food security’ with USDA food security scale r = 0.42, from r = 0.35, subscales r = Po0.001; Po0.01 (more than 0.42 (dairy) and ● ‘diet quality’, from r = − 0.23, Po0.05 (MyPyramid 2 servings r = 0.26 grains oz) to r = − 0.33, Po0.01 (% E transF); vegetables at a main (sweetened ● ‘fast food’ (higher score represents lower intake) with meal) to 0.79, beverages) vitamin A and B-12, r = 0.23, Po0.05; Po0.0001 (rate (n = 154) ● ‘sweetened beverages’ (higher score represents lower eating habits). intake) from r = − 0.33 (% E TF) to r = − 0.41, Po0.001 ICC total scale = 0.75 (total sugar (g)). Subscales from 0.26 ● No correlation for F+V subscale (sweetened beverages) to 0.80 (F Individual items +V). ● Nutrients, from r = − 0.21, Po0.05 (‘red meat or pork Individual items yesterday’ (higher score represents lower intake) with from 0.34 (servings Dchol, mg), to r = 0.43, Po0.001 (‘drink milk’ with of fruit and more vitamin D) than 2 servings vegetables) to 0.81 (drink milk). Healthy Eating 1) NR NR NR NR NR 1) Same visit Subscales from Linear regression analysis ● A 0.61 increase in BMI was associated with an additional

Vital Signs Anthropometrics HEVS; sugary drink in a typical day (0.17–1.04, Po0.001) but questionnaires dietary England brief CY of review systematic A 8,53 (HEVS) BMI 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, Po0.001) with no association in 1- day recall. tal et 2) NR NR NR NR 2) Total score, Block Food 1 week Individual items Pearson's correlation ● 'Multiple items' on HEVS with FFQ items/nutrients, from α = 0.49 Frequency from HEVS r = 0.30 (1- day and typical ‘eating fast food’ with Questionnaire Items and TF (g); 1- day and typical F+V questions with ﬁbre (g), nutrients from Po0.05) to r = 0.5 (1-day ‘eating fast food’ with FFQ transF (g), Po0.001).

Latino Dietary NR NR NR NR Baseline total 3 × 24 h recall Unclear but Total score, Correlation Baseline: Behaviors score α = 0.47; both subscale scores Independent samples t- ● Total LDBQ score with sodium (mg) r = − 0.25, and Questionnaire 12 m α = 0.48. dietary and change test energy (kcal) r = − 0.34, Po0.01 (no other correlations 54 (LDBQ) Healthy dietary measures scores from with nutrients) change collected at LDBQ; ● Subscales, from r = 0.15, Po0.05 (‘fat consumption’ subscale had baseline Nutrients, proxy with %E transF) to − 0.39, Po0.01 (‘healthy dietary the strongest and 'behavioural' change’ with energy, kcal) baseline and 12 months items and change ● Clinical measures, r = − 0.16, Po0.05 (HbA1c) to r = 0.24, 12 month scores from Po0.01 (diastolic blood pressure). internal recalls (baseline consistency and 12 months) 12 months: (α = 0.60 and Baseline clinical ● Total LDBQ score, from r = − 0.16, Po0.05(%E transF) to α = 0.58) measures r = 0.37, Po0.01 (%E protein)

● Subscales, r = − 0.15 (‘artiﬁcial sweeteners’ with % E sfa) to r = − 0.43, (‘healthy changes’ with %E TF), Po0.01. ● Sensitivity to change: LDBQ showed greater change – =

1003 over time in the intervention group (n 67; 7.10 (5.53)) compared with control group (n = 75; 3.36 (5.12)), Po0.001. 989 990 uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Table. 2. (Continued )

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests ytmtcrve fbifdeayquestionnaires dietary brief of review systematic A PrimeScreen55 2 weeks Nutrients and Spearman's For foods and food NR Willet's SFFQ 2-4 weeks Nutrients and Pearson's correlation, ● Food groups, from r = 0.36 (‘other vegetables’) to 0.82 food group correlation groups r = 0.50 food groups Spearman's correlation (‘whole eggs’). consumption (other vegetables) to calculated from ● Nutrients, from r = 0.43 (iron) to 0.74 (vitamin E with derived from 0.87 (adding salt) both Primescreen supplements). PrimeScreen (no P values); for and FFQ ● Plasma levels, PrimeScreen nutrients with vitamin E, nutrients r = 0.59 Plasma r = 0.33; beta-carotene, r = 0.43; lutein/zeaxanthin, (lutein/zeaxanthin) carotenoids and r = 0.43 (for the SFFQ these were 0.19, 0.43, 0.34 to 0.86 (vitamin A plasma vitamin respectively). with supplements) levels ● Specificity for o3/day F+V = 67%; Sensitivity for 5/day (no P values). No F+V = 73%. Sensitivity for 410% E sfa was 81% and difference across specificity 66%. (no P values given) demographic strata. – 03©21 amla ulsesLimited Publishers Macmillan 2015 © 1003 Rapid Eating 1 week Total score; Correlation Total score, r = 0.86, NR Food diaries Sample 2: Study 1: REAP Correlation Study 1 Assessment for individual Po0.001; For (unknown), 1 week total score and ● Total scores, r = 0.49, P = 0.007 Patients items individual items Women's Health (sample 1 subscales ● Subscales: from r = 0.31, P = 0.04 (variety subscales) to England CY (REAP)29 from r = 0.79 (type Initiative FFQ not stated) Total HEI and HEI r = 0.55, Po0.001 (fat subscales) of ice cream) to 0.33 subscales from (servings of fruit and diaries. Study 2 vegetables) Study 2: Modified ● REAP energy subscale with energy (kcal), r = − 0.44, o (P 0.001) REAP subscales; Po0.001 al et foods/nutrients from FFQ ● Other subscales, from r = 0.30, P = 0.024 (fruit servings) to r = − 0.62, Po0.001 (alcohol)

Rapid Eating NR NR NR NR NR Block 1998 semi Unclear REAP Food Pearson's correlation ● Food groups, from r = − 0.38 (added fat servings) to Assessment for quantitative food groups; r = 0.51 (fruit servings), Po0.001. Patients short frequency Food groups, TF, ● REAP-S food servings with FFQ nutrients, from r = − 0.20, form questionnaire fibre (g),Dchol P = 0.034 (‘fish, poultry and meat servings’ with Dchol 14 (REAP-S) (mg) sugar (g) (mg)) to 0.52, Po0.001 (‘vegetable servings’ with from FFQ fibre (g)).

Short Diet NR NR NR NR NR 10 × 24 h recall 24 hrecalls Total score from Pearson's correlation, ● DQI, r = 0.61 (no P value) Quality 41 year. sDQS (DQI); ICC, ● sDQS DQI mean vs 24 h DQI mean = 39.3 (2.8) vs Screener (bMDSC DQI from recalls LOA 35.5 (2.8), difference 3.82 (95% CI, 3.33, 4.31). LOA = (sDQS)13 first, then Mann Whitney U 96–126%. ICC = 0.32. Developed in 1 week Gross misclassification ● 48.5% participants classified in the same tertile, 3.9% the same later sDQS) in the opposite tertile. population as the Brief Mediterranean Diet Screener (bMDSC) Adherence to the Mediterranean diet Brief NR NR NR NR NR 10 × 24 h recall 24 h recalls Total and Pearson's correlation, ICC ● bMDSC mMDS with 24 h mMDS, r = 0.40 Mediterranean 41 year. subscales LOA ● bMDSC ANTOX-S with 24 h ANTOX-S, r = 0.45 (no Diet Screener bMDSC (ANTOX-S and Mann Whitney U P values) (bMDSC)13 first, then mMDS) from Gross misclassification ● bMDSC mMDS mean = 18.3 vs 24 h mMDS mean = 20.7, Developed in 1 week bMDSC; ANTOX-S difference = -2.44 (95% CI − 3.01, − 1.82). Mean the same later sDQS and mMDS scores differences for the ANTOX-S was zero. population as derived from ● For the mMDS 44% participants classified in the same the Short Diet recalls tertile, 11% in the opposite tertile. Quality ● LOA = 61– 118%, ICC = 0.30. Screener ● For the ANTOX-S 50% in the same tertile with 9% in the (sDQS) opposite tertile. LOA = 59–144%, ICC = 0.45. 05McilnPbihr iie uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Limited Publishers Macmillan 2015 © Table. 2. (Continued )

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests

Mediterranean NR NR NR NR NR Anthropometrics, Unclear— PREDIMED score Pearson's correlation ● Total PREDIMED scores, r = 0.52, Po0.001. Diet Adherence Biomarkers of baseline and individual General linear modelling ● Absolute ICC = 0.52 for men, 0.51 for women, Po0.001. Score preclinical items from ICC, LOA ● Associations found for CVD risk factors and MEDAS for (MEDAS)56 disease, MEDAS Kappa statistics BMI (β = − 0.146, Po0.001) and waist circumference PREDIMED FFQ PREDIMED score (β = − 0.562, Po0.001) with smaller associations for from FFQ, lipids and fasting blood glucose. individual items and nutrient Individual items intakes from FFQ; ● Associations between nutrients/foods on the FFQ and anthropometrics PREDIMED quintiles as derived by MEDAS were in the biomarkers 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 (Po0.001).

● 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.

Total Fat questionnaires dietary England brief CY of review systematic A Dutch fat 1 year Total score; Pearson's r = 0.71 (no P value); NR 7 day un-weighed 1 week— Total Dutch fat Pearson's correlation ● Dutch Fat score with TF (g), r = 0.59 (no P value) consumption tertiles correlation 3.9% of participants food diary 1 month score Unweighted kappa ● Kappa = 0.42 with 2 categories and 0.25 with 3. questionnaire Gross were classified in TF (g) from statistics ● Gross misclassification = 15.4% (D-Fat1)57 misclassification opposite fat diaries Gross misclassification consumption tertiles. al et

Fat Related Diet 1) Total FRDHQ Correlation Total FRDHQ score, 1) 1) Adjusted correlation ● Total FRDHQ score with %E TF, r = − 0.60 Po0.001 Habits 3 months score; r = 0.87; for Total FRDHQ 8 day food diaries After the Total FRDHQ Questionnaire/ Behavioural subscales from r = score, α = 0.62; (unknown), 1st food score and Subscales Kristal’s Food subscales 0.67 (replace high subscales range Modiﬁed Block/ diary was behavioural ● %E TF, from r = − 0.29, Po0.01 (‘avoid meat’)tor = 0.50, α = Habits fat foods with 0.54 National Cancer returned subscales from Po0.001, (‘avoid fat as seasoning’). Questionnaire – naturally low fat (replace high Institute FFQ KFHQ; 20 items foods) to 0.90 (avoid fat foods with %E TF from diet ● (FRDHQ)11 fat as a seasoning), naturally low records and FFQ; Linear relationship with %E TF and 'avoid fat as (no P values). fat foods) to seasoning', 'substitution of high fat foods with 0.76 (avoid manufactured low fat alternatives' and 'replace high fat meat) 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. Fat Related Diet 2) NR NR NR 2) 2) Total FRDHQ Adjusted correlation ● Total FRDHQ score with %E TF, r = − 0.24, Po0.001. Habits NR Total FRDHQ Ontario Health score and Subscales – Questionnaire/ score, α = 0.73; Survey Food subscales; % E TF ● = − o ‘

1003 %E TF, r 0.12, P 0.05 ( substitute low-fat for high Kristal’s Food subscales Frequency and total energy fat’)to− 0.24, Po0.001 (‘avoid fat as a seasoning’). Habits range, α = 0.13 Questionnaire (kcal) from FFQ 991 992 uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European

Table. 2. (Continued )

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests ytmtcrve fbifdeayquestionnaires dietary brief of review systematic A Questionnaire – (replace high ● No significant correlations for energy with total score or 20 items fat foods) to subscales. (FRDHQ)46,21 0.53 (make ● Confirmatory factor analysis showed discrepancy between modifications the hypothesised structure and the actual responses to meat prep). (likelihood ratio (160) = 256.98, P = 0.001. Some items When used as a were not related to hypothesised factor e.g., loadings behavioural of less than 0.3 for replace high fat food subscale. checklist α = ● When the tool was used as a behavioural checklist 0.70. total score with %E TF, r = − 0.27 (Po0.001). (Item-scale correlations also tested) – ● 03©21 amla ulsesLimited Publishers Macmillan 2015 © 1003 3) i) NR NR NR NR 3) i) 2 years (B- Total score from Correlation Total FRDHQ score with %E TF r = 0.48, Po0.01 NR Block/National FHQ) FRDHQ; ● Total FRDHQ score with B-FS score, r = 0.61, Po0.01 Cancer Institute Same visit %E TF from ● Total FRDHQ score with BMI, r = 0.1, Po0.01 FFQ (B-FFQ) (B-FHS) B-FFQ; ● No correlation with TChol England CY Block fat screener Total score from (B-FS) B-FS BMI; TChol 3) ii) Total score Correlation All participants NR 3) ii) After return Total score from Correlation ● Total FRDHQ score with energy (kcal), r = 0.27, Po0.01 = o ‘ ’ = ● = o 3 months r 0.59, P 0.01; UC 4 day food diaries of food FRDHQ; Responsiveness mean Total FRDHQ score with %E TF, r 0.44, P 0.01 al et only r = 0.56, diaries Energy (kcal), % E difference between UC ● Total FRDHQ score with TChol, r = 0.19, Po0.05 Po0.01 TF, kcal from (n = 39) and intervention ● Total FRDHQ score with HbA1c, r = 0.32, Po0.01 diary; (n = 50) ● Total FRDHQ score with BMI, r = 0.22, Po0.05 BMI, HbA1c (%), TChol (mmol/l) at baseline Follow up scores, Baseline: adjusted for ● UC mean FRDHQ score = 2.14 baseline used to calculate ● responsiveness to Intervention mean FRDHQ score = 2.16 change 3 months ● UC mean FRDHQ score = 2.16

● Intervention mean FRDHQ score = 1.97 ● FRDHQ responsiveness = 0.4

Fat Related Diet 4) Total FRDHQ Pearson's Total FRDHQ score, Total FRDHQ 4 day un-weighed baseline to Total FRDHQ Correlation Pre-intervention Habits 9 months score; correlation r = 0.74 Po0.01 score, α = 0.83 food diary 9 months score and ● Total FRDHQ score with energy (kcal), r = 0.43, Po0.05; Questionnaire/ Behavioural Subscales from, r = Subscales from, subscales from TF (g), r = 0.52 with % E TF, r = 0.47, Po0.01. Kristal’s Food subscales 0.48 (replace with α = 0.47 KFHQ; Energy Subscales Habits fruit) to 0.68 (avoid (replace fat) to (kcal), %E TF and ● r = 0.35 (‘avoid fat’ and ‘substitute fat’ with energy) to – o Questionnaire fat), (P 0.01) 0.76 (substitute TF (g) from r = 0.43 (‘modify meats’ with TF (g)), Po0.05. No subscale 24 items fat) diaries(pre and correlated with %E TF. (FRDHQ)47 post intervention) ● ‘Replace with fruit’ subscale did not correlate with the nutrient estimates. Post-intervention: ● Total FRDHQ score with TF (g) r = 0.46, Po0.01. No other correlation for total FRDHQ score. Subscales ● r = 0.21 (‘modify meats’ with energy), Po0.05–0.47 (‘substitute fat’ and TF (g)), Po0.01. 7–9 months Total score Pearson's r = 0.85 (95% CI, NR 179-item CSIRO Total score from Correlation correlation 0.69–0.93) FFQ SFQ Misclassiﬁcation 05McilnPbihr iie uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Limited Publishers Macmillan 2015 ©

Table. 2. (Continued )

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests

Short Fat Unstated, %E TF, % E sfa; ● Total score with %E TF, r = 0.55 (CI 0.39–0.68), %E sfa, Questionnaire possibly pufa:sfa from FFQ r = 0.67 (CI 0.54–0.77) and pufa:sfa, r = − 0.44 (CI − 0.60 56 (SFQ) same visit 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.

Sister Talk Food NR NR NR NR Total score, Anthropometrics, Same time Change in BMI Pearson's correlation Post intervention Habits (short α = 0.79 91 item SisterTalk Change in short Bootstrapping, ● Change in SisterTalk with change in BMI, r = 0.17 (95% form)3 FFQ Sister Talk CI 0.02, 0.39) During maintenance ● Change in SisterTalk with change in BMI, r = 0.28 (95% CI 0.02, 0.50), Po0.001.

● Total change in SisterTalk with total change in BMI, r = 0.35 (95% CI 0.08, 0.58), Po0.05 ● (Correlations were not significantly different between short and long Sister Talk FHQ.) ytmtcrve fbifdeayquestionnaires dietary England brief CY of review systematic A Starting the 4 months Total score; Pearson's Total score r = 0.66, (Pearson's) NCI Percentage Both tools STC total score Pearson's correlation ● Baseline STC total score and Pfat score, r = 0.39, Po0.05; Conversation individual correlation Po0.05 Individual energy from fat completed and change in ● Change in STC score and reduction in Pfat TF, r = 0.22, (STC)39 items Individual items items (Pfat) screener at the same STC total score Po0.05. from, r = 0.4 to r = correlated with visit Pfat score and 0.62 (no details), the summary reduction in TF Po0.05. (r = 0.39–0.59, from Pfat al et Po0.05). Specific dietary fats and/or dietary cholesterol Dietary Fat 2–4 weeks Dietary fats ICC ICC range = 0.48– NR Fred Hutchinson 2–4 weeks Total DFQA; Spearman's correlation ● Total DFQA score with PUFA:SFA ratio, r = 0.4, Po0.001. Quality quantified 0.59 for dietary fats Cancer Research Quantified fat Gross misclassification ● DFQA with FFQ fat estimates, r = 0.54 (Dchol, (g)) to Assessment from DFQA (no CIs given) Center FFQ and cholesterol r = 0.66 (sfa, (g)), Po0.001. (DFA)60 servings intakes from ● DFQA classified 39% (mufa, (g)) to 55% (sfa, (g)) of DFQA and FFQ participants into the same nutrient quartile as the FFQ pufa:sfa from FFQ and 80–87% into adjacent quartiles. 2% of participants were grossly misclassified for sfa, mufa, omega-3 s and dchol.

Heart Disease 3–4 months Total score Mean scores, ‘Only small NR 3 day food diaries Around the Total screener Correlation, Independent ● Total score with sfa (g) r = − 0.30, Po0.05 Prevention gross differences between (unknown) same time score; samples t-test ● Estimated mean sfa (g) for 34 men with scores of o15 Project misclassiﬁcation occasions (20 of the Mean sfa (g) from was 53.4 g vs 41.2 g for those with scores of 416 (n = 34), Screener men had a food diary Po0.001 (HDPPS)61 difference of 2 points or less) and the mean scores for the group were identical on each occasion’ (p365) – 1003 993 994 uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European

Table. 2. (Continued )

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests ytmtcrve fbifdeayquestionnaires dietary brief of review systematic A MEDFICTS12,62 1) NR NR NR NR 1) ‘recent’ Total score; %E TF, Pearson's correlation Sample 1 (n = 22) NR 3 × food diaries %E sfa and Dchol ● Total score with %E TF, r = 0.79 (Po0.002), %E sfa, (unknown) from diaries r = 0.60 (Po0.003), Dchol, r = 0.71 (P = 0.001) Total score; consumption of Sample 2 (n = 26) Step 1, 2 and 3 ● Total score with %E TF, r = 0.54 (P = 0.009), Dchol, diets from food r = 0.39 (P = 0.051). diaries as assessed by a dietitian ● 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 –

03©21 amla ulsesLimited Publishers Macmillan 2015 © 1003 consuming an average American diet’ p85

2) NR NR NR NR 2) Spearman's correlation ● Total score, r = 0.52, Po0.0001 (%E TF and %E sfa), NR Reduced Block Total score and Kappa statistics ROC Dchol, r = 0.55, Po0.0001. England CY FFQ diet classification curve analysis ● Identified as high fat diet: FFQ identified 76.2% vs (high fat, step 1, MEDFICTS identifying 17.7% of this group. step 2) from ● Recommended MEDFICTS cut-offs correctly identified Medficts 23.3% high fat diets and 19.2% Step 1 diets. No Energy (kcal), %E fi agreement for diet steps between FFQ and Med cts, al et TF,%E sfa and kappa = 0.036. Dchol from FFQ ● 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, Po0.001 for a high fat diet.

MEDFICTS63,64 3) NR NR NR NR 3) Spearman's correlation ● Total score with %E TF, r = 0.30, Po0.001 NR Arizona FFQ Mean of Total score from Chi squared ● Identified high fat diet: FFQ identified 71.2% vs 52 days Medficts %E TF; % Independent samples t- Medficts identified 50.5%. 59.8% identified on both tools. E sfa; Dchol from test ROC curve analysis ● Dichotomized Medficts score 430% energy from fat, FFQ chi squared = 8.19, Po0.01; 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).

4) NR NR NR NR 4) Same visit Total score and Pearson's correlation ● Total score with %E sfa, r = 0.52, %E TF, r = 0.31, NR Block 98 FFQ classified as Chi squared Dchol r = 0.54, Po0.0001. adherent/non- Kappa statistics ● Medficts categorised 44.9% of participants as adherent adherent to TLC to the TLC diet. FFQ categorised 4.2% as adherent. diet from ● Categorical agreement Medficts ● Overall, k = 0.08, Po0.001; o7% sfa; k = 0.13, Po0.001; %E TF, %E sfa and o30% TF, k = 0.16, Po0.001; o200 mg Dchol, k = 0.34, Dchol (mg) from Po0.001. FFQ ● 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%) Po0.001. Optimal cut-off pointo25 improved specificity to 82.5% and sensitivity of 76.2% overall but men and women were different with men having an optimal cut-offo37 (specificity of 80% and sensitivity of 78.3%) and women an optimal cut-offo20 (specificityo83.8%, sensitivityo75%). No difference seen for ethnicity for sensitivity or specificity. 05McilnPbihr iie uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Limited Publishers Macmillan 2015 ©

Table. 2. (Continued )

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests

NLSChol 30 days 3 groups ICC, Paired ICC = 0.89 (no Cis); Total score, Diet history 3 groups from Pearson's correlation ● Group classification, r = 0.3, P = 0.029 Questionnaire58 from NLSChol Wilcoxon rank Agreement in α = 0.69 NLSChol Kappa statistics ● Agreement of 72% between dietitian classification and 3 groups score classification = 85% 3 groups derived Bowker’s test of NLSChol score, kappa = 0.48 (0.10; 0.69). derived from Percentage (17 patients), 15% (3 from diet history. symmetry ● Bowker's test of symmetry was not significant. diet history. agreement in patients) moved up classification or down a group. Comparison of medians was not significant, P = 0.52.

Northwest 2–3 weeks; Total score Pearson's Retest after 2– NR 4 day un-weighed NR Total NWFIS and Pearson's correlation Baseline Lipid Research 6–8 weeks correlation 3 weeks r = 0.88 for food diary, change in FIS TF, ● Total NWFIS with %E TF, r = 0.49, %E sfa, r = 0.44, Clinic Fat Intake men, 0.90 for Biomarkers of sfa, Dchol Dchol mg/1000kcal, r = 0.46, Keys score, r = 0.46, RISCC, 20 Score (NWFIS) women (Po0.001) preclinical disease (adjusted and not r = 0.53 Po0.001. After 6–8 weeks r = adjusted for 18 months 0.76 for men and energy), Keys ● Total NWFIS with %E TF, r = 0.55, %E sfa, r = 0.64, 0.78 for women score, RISCC Po0.001 and Dchol mg/1000kcal, r = 0.30, Po0.01, (Po0.001) score, change in Keys score, r = 0.58 and RISCC, r = 0.56, Po0.001 nutrients, change in Keys score and change in RISCC ● Change in NWFIS with change in TF (g), r = 0.38 (men) score from diaries and r = 0.40 (women), in sfa (g), r = 0.42 (both), in Change in plasma Dchol (mg), r = 0.32 (men) and 0.52 (women), in Keys cholesterol score, r = 0.38 (men) and 0.48 (women), in RISCC, r = 0.39 (men) and 0.51 (women) Po0.001. ytmtcrve fbifdeayquestionnaires dietary England brief CY of review systematic A

Rate Your Plate NR NR NR NR NR Willett SFFQ Same visit Total score from Pearson's correlation ● Total score, r = − 0.28 (% E TF, less trimmed fat) to (RYP)15 RYP; dietary fats r = − 0.48 (% E sfa), Po0.05. and Dchol from FFQ tal et Total and saturated fat and free sugar Dietary Fat and 158 (10) Total score ICC ICC = 0.83 (95% CI Total score, Anthropometrics, Same visit Total DFFS score; Spearman's correlation ● DFFS with food diary nutrients, from r = 0.35 (energy), Free Sugar days 0.66–0.91) α = 0.76. Commonwealth for FFQ, DFFS subscales; Independent samples t- Po0.05 to r = 0.46 (% E sfa), Po0.01. Short Scientiﬁc and shortly Nutrients from test ● DFFS with FFQ nutrients, from r = 0.40 (energy) to Questionnaire Industrial after for FFQ r = 0.71 (% E sfa), Po0.01. (DFFQA)69 Research food diary ● Subscales with nutrients, from r = 0.33 (fat subscale with Organisation diary %E sfa), Po0.05 to r = 0.68 (fat-sugar subscale Food Frequency with FFQ %E sfat). Questionnaire ● For DFS scoreso60 mean %E TF = 28.56 vs DFS 4 day un-weighed score460 mean %E TF = 33.51 (3.87), Po0.01; DFS food diary scoreso60 mean %E free sugars = 7.41 (4.54) vs DFS460 mean %E free sugars = 11.39 (6.15), Po0.05.

Dietary fats and fibre Dietary NR NR NR NR NR 4 day un-weighed 5 days DINE fat score; Pearson's correlation ● DINE fat score, from r = 0.28 (%E TF) to 0.57 (sfa, (g)); Instrument for food diary DINE fibre score; Weighted kappa fibre score with fibre (g) r = 0.46; DINE unsaturated Nutrition Fat and fibre Percentage agreement of fat score with pufa:sfa ratio, r = 0.43, Po0.001. Education intake from classification ● Weighted kappa = 0.38 for TF (g) and 0.30 for fibre (g). 5 (DINE) diaries Gross misclassification ● 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) fi fi

– and 49% agreed with 10% misclassi cation for bre (g). 1003 995 996 uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European

Table. 2. (Continued )

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests ytmtcrve fbifdeayquestionnaires dietary brief of review systematic A Fat and Fibre 1) Total score Pearson's r = 0.92 (95% CI, 1) 1) 7–10 weeks FFB score; Pearson's correlation ● Men: FFB score with %E TF, r = − 0.33 (−0.05, − 0.56); Barometer 7–9 weeks correlation 0.89–0.94). No NR Geelong, meal TF (g), total fibre Weighted kappa Gross fibre (g/10MJ) r = 0.83 (0.71, 0.90). (FFB)9 difference between based FFQ (g), % E TF, fibre misclassification ● Women: FFB score with %E TF, r = − 0.75 (−0.60, − 0.85), men and women developed by the (g/10MJ) from fibre (g/10MJ) r = 0.58 (0.36, 0.74). Deakin Institute FFQ Weighted kappa ● Men: %E TF = 0.39 (0.18, 0.61), fibre (g/10MJ) = 0.59 (0.42, 0.76)

● Women: %E TF = 0.58 (0.41, 0.75), fibre (g/10MJ) = 0.27 (0.06, 0.48). Gross misclassification – ● 03©21 amla ulsesLimited Publishers Macmillan 2015 © 1003 Men: 15% for TF (g), 9% for %E TF, 6% for fibre (g), 0 for fibre g/10MJ YEngland CY ● Women: 12% for TF (g), 2% for %E TF and 10% for both fibre variables

Fat and Fibre 3 months FFDB Fat Spearman's 3 months: FFDB fat NR FFQ was Same time FFDB Fat score; Spearman's correlation ● FFDB fat score with % E TF, r = 0.53, Po0.001 Diet Behaviour 1 year score; FFDB correlation score, r = 0.79; FFDB developed for the FFDB fibre score, ● FFDB fibre score with fibre/1000kcal, r = 0.50, Po0.001;

Questionnaire fibre score; fibre score, r = 0.74, study and based subscale scores; with servings of F+V, r = 0.50, Po0.001. al et (FFDBQ)18 subscale Po0.001 on a pre-existing %E TF, fibre/ Subscales scores Subscales from r = FFQ evaluated 1000kcal; ● %E TF, from r = 0.2 (replace high-fat meats) to r = 0.43 0.60 (modify meals against diet servings of F+V (avoid fat as a flavouring), Po0.001. to be low in fat) to records from FFQ 0.74 (substitute for ● = fi low-fat foods), Fibre/1000kcal, from r 0.24 (substitute high bre for fi = o Po0.001. low bre) to r 0.43 (F+V), P 0.001. 1 year: FFDB fat score, r = 0.74; FFDB fibre score, r = 0.70 for fibre score, Po0.001 Subscales r = 0.53 (modify meals to be low in fat) to 0.66 (substitute high- fibre for low-fibre foods; Substitute especially manufactured low fat foods), Po0.001. Norweigian Same day Total score; Pearson's r = 0.95 (no P value); NR 7 day weighed 8 days Total score, foods Pearson's correlation ● Total scores, r = 0.73 SmartDiet individual correlation, mean agreement food diaries (screener and food groups LOA ● Correlations with nutrients was highest for sfa (g), Questionnaire items Percentage rate = 0.93 (range first) from screener Weighted kappa r = − 0.59 (N-Smart)7 agreement of 0.85 for vegetables Total calculated ● Kappa from 0.71 (0.56–0.86) for milk and 0.73 (0.60–0.86) classification, to 0.98 for milk); score, food, food for spreads to 0.42 (0.28–0.55) for fruit and vegetables. Weighted Weighted Kappa groups and ● Agreement ranged from 0.98 for milk to 0.38 for fish, kappa ranged from 0.75 nutrients from mean agreement = 0.73 (95% CI, 0.63–0.86) diary ● Distribution of difference between the food diaries (vegetables) to 0.97 and tool total score, mean = 1.9 (95% CI 1.4–2.5), (95% CI, 0.94–1.00) LOA = − 0.8–7.7 (cheese). 05McilnPbihr iie uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Limited Publishers Macmillan 2015 © Table. 2. (Continued )

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests

Total fat and fruit and vegetables Block Fat, Fruit NR NR NR NR NR 100-item Block Posted Meats/Snacks Spearman's correlation ● Meat/snack score, from r = 0.60 (Dchol, mg) to r = 0.72 and Vegetable FFQ together score and (sfa, g), Po0.0001 Screeners F+V scores from ● F+V score (without pulses), from r = 0.41 (magnesium, (B-F&FV)6 screener; fat mg) to r = 0.71 (fruit and vegetable servings), Po0.0001. nutrients, F+V ● F+V screener (including pulses), from r = 0.46 servings, ﬁbre (g) (magnesium, mg) to r = 0.62 (ﬁbre, g), P = 0.0001. and ● 89% of people low on F+V score were very low or micronutrients quite low on FFQ. 12% of people scored as needing from FFQ; advice on fat on the screener did not need advice according to FFQ.

Hispanic Fat 1 month F+V score; fat Pearson's r = 0.64 for F+V NR NR NR NR NR NR and Fruit and score correlation, score, 0.85 for fat Vegetable percentage score, Po0.001. screeners agreement 84% agreement for (H-F&FV)42 vitamin supplement use, Po0.001 Fruit and/or vegetables Canadian Fruit NR NR NR NR NR FFQ developed Same visit Servings F+V Pearson's correlation ● Servings, r = 0.66, Po0.001 (obese participants); r = 0.65, and Vegetable and tested by from FV-Q and ICC Po0.001 (non-obese). Questionnaire Goulet et al. at FFQ ROC curve analysis ● ICC for obese = 0.44, for non-obese = 0.46. (CFV-Q)66 Laval University ● Sensitivity in obese group = 88.5%, specificity = 63.6%. Positive and negative predictive values = 45% and 94%. ● Non-obese sensitivity = 80% and specificity = 66%, questionnaires dietary England brief CY of review systematic A 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). tal et Dutch fruit and 1 month; 1 F+V intake; Spearman's Retest at 1 month: NR Change in Same time F+V intake from Spearman's correlation Baseline vegetable year tertiles of correlation Total F+V, r = 0.80. concentration screener; mean ● Total F+V intake, from r = 0.23 (plasma B-carotene and questionnaire intake Percentage Individual variables, biomarkers changes in lutein) to r = 0.37 (plasma vitamin C), Po0.01. 19 (D-F&V) agreement of r = 0.49 (other fruits) screener score; After intervention classification to 0.82 (F+V juices). Mean change in ● Changes in total F+V consumption, from r = 0.26 (change Agreement plasma in B-carotene) to r = 0.39 (change in B-crytoxanthin), fi classi cation: carotenoids; Po0.01. vegetables = 59%; Mean change in 41% up or down a plasma vitamin C 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 1003 class; 2% down two classes. 997 998 uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European

Table. 2. (Continued )

Tool Test retest reliability Validity

Retest time Variables Test Results Internal Reference measure Time Variables Test Results reliability between tests ytmtcrve fbifdeayquestionnaires dietary brief of review systematic A

Five a day 1) NR NR NR NR 2 × 24 h recall 1 year Mean and Linear regression Men screener NR median servings analysis; Maximum ● β = 0.52; median (IQR) F+V servings from the recall (5- F&V) F+V/day likelihood estimates vs the screene r = 6.6 (3.6) vs 3.7 (2.6), Po0.001 Five a day (adjusted for Women screener within person ● β = 0.50; median (IQR) F+V servings from recall vs 17,67 (5- F&V) variation) from screene r = 5.5 vs 4.2 (no IQRs quoted), Po0.001. screener and Percentages eating 5 a day recalls. ● Men = 73% for recall vs 24% for the screener

● Women = 59% for recall vs 36% for the screener – 03©21 amla ulsesLimited Publishers Macmillan 2015 © 1003 2) NR NR NR NR 3 × 24 h recall NR Servings F+V, Pearson's correlation ● Total F+V servings, r = 0.50, Po0.001 NR including and not Paired sample t-test ● Individual items, from r = 0.27, Po0.01 (‘cooked,

including salad, excluding fried vegetables’)tor = 0.59, Po0.001 England CY potatoes, fried (‘fruit’). vegetables, and ● Total F+V servings, recall vs screene r = 4.1 (2.1) vs fruit juices from 3.3 (1.9), Po0.001. screener and ● Fruit alone recall vs screene r = 0.97 vs 0.84; juice recalls. alone recall vs screene r = 0.37 vs 0.56; total vegetable tal et recall vs screene r = 2.53 vs 1.80, Po0.001. No difference for fruit+juice.

Mainvil Fruit NR NR NR NR NR Diet history Same day Daily servings of Pearson’s correlation ● Daily servings, r = 0.57, Po0.001 (men, r = 0.67, Habits (diet fruit from LOA, Po0.001;women, r = 0.49, Po0.001 Questionnaire history last) M-FRHQ and diet Kappa statistics ● LOA = − 2.98–4.31, mean difference = 0.66 (CI: 0.32,1.02) (M-FRHQ)68 history Percentage of agreement ● kappa = 0.41, Po0.001 Gross misclassification ● 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%, Po0.001 ● Positive predictive value was 87% (64% sensitivity), negative predictive value was 66% (88% specificity)

Short Dutch NR NR NR NR NR 7 day un-weighed Mean daily Spearman's correlation, ● Total F+V, r = 0.43; total fruit, r = 0.51; total vegetables, questionnaire food diary intakes of fruit Percentage agreement of r = 0.35. to measure fruit and vegetables classification ● 36.8% misclassified for total fruit intake; 22.5% for total and from both vegetable intake. 7 people (14%) were classified as 16 vegetables questionnaires meeting recommendations for total F+V intake on screener who were not on FFQ. 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). 05McilnPbihr iie uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Limited Publishers Macmillan 2015 © Table 3. Summary of key characteristics of dietary assessment tools and evaluation studies

Tool name (date of most Number of Purposea D = Dietary advice (includes Administration Test- Relative Some relative Relative Relative Access and availability of score sheet recent evaluation) questions clear clinical guidance) M = dietary I = interview retest validity validity validity in validity in monitoring (limited clinical guidance) S = self reliability study correlation a clinical men (m) or C = sensitive to change T = Telephone study coefﬁcients sample women (w) 40.4 only

Australian Diet Quality 13 D S ✓1 ✓✓ Tool available in paper. Score sheet available on request from Tool50 authors. Free to use, acknowledgement needed Bailey Elderly Food 15 D S ✓2 Tool and detailed score sheet available in Bailey (2009) Screener28,51 Food Behaviour 16 M I ✓✓1 ✓✓w Tool and scoring derived from validation results in Townsend (2003) Checklist: Text version10 Food Behaviour 16 M I Visual tools, with instructions, are available from http://townsendlab. Checklist: Visual ucdavis.edu/PDF_ﬁles/UCCE/UCCE_FBC_InstructGuide.pdf version43 Food Behaviour 16 M I ✓✓1 ✓✓w Checklist: Visual version, Spanish44 Healthy Eating Vital 14 M I ✓2 ✓✓ Tool available and scoring described in Greenwood (2008) Signs 18,53 Latino Dietary 13 M/C I ✓1 ✓✓ Tool available and scoring described in Fernandez (2011) Behaviors Questionnaire54 PrimeScreen55 15 food 8 DS✓✓1 ✓✓ Tool and detailed score sheet available free, request from: vit/minb Department of Nutrition, Harvard School of Public Health (http:// www.hsph.harvard.edu/) REAP (2006) 31 D S ✓✓2 ✓ Tool and detailed score sheet available from: http://publichealth.

brown.edu/ICHP/research-tools questionnaires dietary England brief CY of review systematic A REAP-S29 16 D S ✓1 ✓ Tool and detailed score sheet available from: http://www.einstein.yu. edu/centers/diabetes-research/research-areas/survey-instruments. aspx Short Diet Quality 18 M S ✓1 ✓ Tools available on request from authors. Scoring described in Screener13 Schroder (2011). al et Brief Mediterranean 15 M S ✓✓ Tools available on request from authors. Scoring derived from Diet Screener13 Schroder (2011). Mediterranean Diet 14 M I ✓✓ ✓ Tool available and scoring described in Martinez-Gonzalez (2012) Adherance Score56 Dutch fat consumption 25 M T ✓✓1 ✓ Tool available and scoring described in van Assema (1992) questionnaire (Dutch)57 Fat-Related Diet Habits 20/25 M S/I ✓✓5 ✓✓ Tool and detailed score sheet available from: http://sharedresources. Questionnaire/Kristal’s fhcrc.org/documents/fat-related-questionnaire. Rights from nasr@fhcrc. Food Habits org Questionnaire11,46,21,47 Short Fat 17 M S ✓✓1 ✓ Tool and detailed score sheet available in Dobson (1993) Questionnaire56 Sister Talk Food Habits 30 M/C S ✓1 ✓ Tool available and scoring derived from Anderson (2007). No (short form)3 response to a request for a full description of scoring. Starting the 8 M/C S ✓✓1 ✓ Tool and detailed score sheet available in Paxton (2011) Conversation39 Dietary Fat Quality 20 M S/I ✓✓1 ✓✓✓w Tool available and scoring described in Kraschnewski (2013) Assessment60 ✓✓1 ✓m – Heart Disease 10 M S Tool and detailed score sheet available in Heller (1981)

1003 Prevention Project Screener61 999 1000 uoenJunlo lnclNtiin(05 977 (2015) Nutrition Clinical of Journal European Table. 3. (Continued )

Medﬁcts12,62,63,64 20 D S ✓6 ✓✓ Tool and detailed score sheet available in Kris-Etherton (2001) ytmtcrve fbifdeayquestionnaires dietary brief of review systematic A NLSChol 11 D S ✓✓1 ✓✓ Tool and detailed score sheet available in Beliard (2012) Questionnaire58 Northwest Lipid 12 D/C S ✓✓1 ✓ Tool and detailed score sheet obtained directly from Alice Dowdy. Research Clinic Fat Tool is considered to be outdated (Dowdy, personal communication, Intake Score20 2013) Rate Your Plate15 23 D S ✓1 ✓✓ 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. Dietary Fat and Free 26 M S ✓✓1 ✓ Tool and description of scoring available in Francis (2012) –

DINE 29 D S/I Tool and detailed score sheet available. Copyright is held by The England CY Department of Primary Care at Oxford University and permission must be sought from the Department or from Liane Roe, lsr7@psu. edu. Fat and Fibre 20 D S ✓✓1 ✓ A version with detailed score sheet was developed for use in practice 9

Barometer by Seal and O'Keef, (creative commons licence) available from: http:// al et www.diabetesoutreach.org.au/7Steps/HealthyEating/docs/Healthy% 20EAting%20Fat%20%20Fibre%20Barometer.pdf Fat and Fibre Diet 29 M/C I ✓✓1 ✓✓ Tool and scoring derived from validation results in Shannon (1997) Behaviour Questionnaire18 Norweigian SmartDiet 15 D S ✓✓1 ✓✓ Tool available in and scoring described in Svilaas (2002). No response Questionnaire7 to request for further information re administrator copy Block fat and fruit and Fat 17 DS✓1 ✓ Tool and detailed score sheet available for research (pay to use). vegetable screener6 F&Vb 7 Available on-line to individuals and provides instant feedback. http:// nutritionquest.com/assessment/list-of-questionnaires-and-screeners/ Hispanic fat and fruit Fat 16 F&V DS✓ Tool and detailed score sheet available in Wakimoto (2006) and vegetable 7 screener42 Canadian Fruit and Veg 6D S ✓1 ✓ Tool and detailed score sheet available in Godin (2008) Questionnaire66 Dutch fruit and 8 M/C S ✓✓1 ✓w Tool available and scoring described in Bogers (2004) vegetable questionnaire19 Five a day screener/NCI 7M S ✓✓2 ✓ Tool and detailed score sheet available in Thonpson (2000) fruit and vegetable screener17,67 Mainvil fruit habits 5M S ✓1 ✓ Tool and detailed score sheet available on request from the authors questionnaire68 Short Dutch 10 M S ✓1 ✓ Tool available and scoring described in van Assema (2002) questionnaire to measure fruit and vegetables16 uTested 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. aClinical 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. A systematic review of brief dietary questionnaires CY England et al 1001

Table 4. A checklist for choosing a brief dietary questionnaire for clinical use

Purpose 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 Population What is the population of interest? Country Language Demographics Setting What is the setting? Clinical Community Other Administration How is the tool administered? Interview Self-administered Telephone On-line Other Reliability Has the tool undergone retest assessment? Validation 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? Acceptability Has the clarity of language been checked with users? Timescale What is the time period of interest (last week/last month/last year)? Use 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 Clinical implications algorithms were complex and unlikely to be used in clinical It is important that clinicians are clear about their purpose when practice. Tools were reported as validated against longer FFQs, selecting a tool for use. In clinical practice, dietary assessment is food diaries or 24-h recalls. Correlation coefficients for total fruit required to assist in the provision of dietary advice or to measure and vegetable intakes ranged from 0.29 to 0.80. As the tools the impact of dietary intervention.4 Brief dietary questionnaires measured the same aspect of the diet, comparisons were possible used for the former purpose are those that give clear guidance on and this review concluded that more detailed tools that asked moving to healthier dietary habits rather than obtaining a about portion sizes and the consumption of mixed vegetable detailed, quantitative assessment of an individual’s diet. Assess- dishes showed greater relative validity. Cade et al.41 also comment ment may be focussed on certain nutrients to be disease-specific that FFQs asking people to estimate their own portion sizes are or may be concerned with overall diet quality. Typical questions more reliable. Only one tool included in the current review asks from tools included in the current review include asking about the people to estimate their portion sizes by providing a multiple frequency of consumption of sweet foods or savoury snacks, with choice list of three different sizes.12 responses ranging from less than once a week to more than three All the studies previously reviewed used correlations alone to times a day. The answers can be used to target dietary advice to assess reliability and relative validity. This remains the most the individual. Tools suitable for measuring the impact of a dietary common method, and only five studies in the present review intervention must also be able to measure change. made use of the Bland–Altman method. Correlation coefficients This review provides evidence that tools developed and tested are not measures of absolute agreement but are instead measures in one population may not have the same relative validity in a of relative agreement, assessing whether an individual has different population. Equally, tools developed in different maintained their ranking relative to other participants. The intra- countries will include different food items, also affecting relative class correlation coefficient was used to evaluate four tools, but validity. It should be noted that English translations of tools this measure has also been criticised and data simulations have developed in Spanish, French, Norwegian or Dutch have not been shown that high correlations can be achieved with low absolute validated and that older tools may no longer be appropriate agreement.71 The Bland–Altman method assesses limits of because of shifts in food habits and processing.73 In common with agreement that define the range that 95% of the differences previous reviews,22,70 studies with small sample sizes were not between the measures lie within, and may include graphical excluded. Cade et al.41 report a wide range of sample sizes for presentation of the data. Clinical knowledge must be used to relative validation studies of long FFQs and found no difference in decide if the limits of agreement are acceptable.72 Of the studies reported correlation coefficients between studies with large that used the Bland–Altman method, one was published in 20027 sample sizes compared with small sample sizes. However, with and the remainder after 2010, with three studies conducted by the small sample sizes, confidence intervals are likely to be wide and same team.13,56,68 consequently sample sizes of around 100–200 are advised.40

© 2015 Macmillan Publishers Limited European Journal of Clinical Nutrition (2015) 977 – 1003 A systematic review of brief dietary questionnaires CY England et al 1002 Clinicians should consider the sample sizes of test-retest and 4 Thompson FE, Byers T. Dietary Assessment Resource Manual. J Nutr 1994; 124 (11 relative validation studies if tools are to be used ‘off the shelf’. Suppl): 2245s–2317s. Developers of future tools can enhance understanding 5 Roe L, Strong C, Whiteside C, Neil A, Mant D. Dietary intervention in primary care: of the development, relative validity and reliability of tools by 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 clearly describing: (i) how items were derived; (ii) the population of 18 – interest; (iii) the characteristics of the sample for reliability and and fruit and vegetable intake. Am J Prev Med 2000; :284 288. 7 Svilaas A, Strom EC, Svilaas T, Borgejordet A, Thoresen M, Ose L. Reproducibility relative validation studies; (iv) the results of these studies; and fi and validity of a short food questionnaire for the assessment of dietary habits. (v) whether strati cation by age, gender, ethnicity and socio- Nutr Metab Cardiovasc Dis 2002; 12:60–70. economic status affected results. Tools that are most helpful for 8 Greenwood JLJ, Lin J, Arguello D, Ball T, Shaw JM. Healthy eating vital sign: a new clinical use need to have a clearly described and simple scoring assessment tool for eating behaviors. ISRN Obes 2012; 2012:7. system, and ideally a copy presented in the paper or in an online 9 Wright JL, Scott JA. The Fat and Fibre Barometer, a short food behaviour ques- appendix for evaluation with clear information about copyright. tionnaire: reliability, relative validity and utility. Australian Journal of Nutrition & Table 4 provides a checklist to assist practitioners when choosing Dietetics 2000; 57:33–39. a brief dietary questionnaire for clinical use. 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 CONCLUSION selecting diets low in fat: reliability and validity of a behavioral approach to 90 – This review identified and summarised 35 short dietary assess- dietary assessment. J Am Diet Assoc 1990; : 214 220. 12 Kris-Etherton P, Eissenstat B, Jaax S, Srinath UMA, Scott L, Rader J et al. ment tools of potential use in clinical practice for the dietary Validation for MEDFICTS, a Dietary Assessment Instrument for Evaluating management of cardiovascular disease, obesity and type 2 Adherence to Total and Saturated Fat Recommendations of the National diabetes. In general, tools demonstrated adequate reliability Cholesterol Education Program Step 1 and Step 2 Diets. J Am Diet Assoc 2001; and/or relative validity, although around half have been devel- 101:81–86. oped and evaluated exclusively in US populations. It is not 13 Schroder H, Benitez Arciniega A, Soler C, Covas M-I, Baena-Diez JM, Marrugat J. possible to determine whether any one tool is clearly better than Validity of two short screeners for diet quality in time-limited settings. Public another for a given population or purpose owing to differences in Health Nutr 2012; 15: 618–626. the design of reliability and relative validity studies. If tools are to 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 be used in different countries or populations, they need to be 30 adapted and evaluated locally to ensure they are reliable and have Participants Short Version (REAP-S). Diabetes Educ 2004; : 774. 15 Gans KM, Sundaram SG, McPhillips JB, Hixson ML, Linnan L, Carleton RA. Rate your acceptable levels of relative validity. plate: An eating pattern assessment and educational tool used at cholesterol screening and education programs. J Nutr Educ 1993; 25:29–36. CONFLICT OF INTEREST 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 The authors declare no conflict of interest. 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 ACKNOWLEDGEMENTS daily number of servings of fruit and vegetables. Am J Clin Nutr 2000; 71: – We thank the developers of brief questionnaires who provided access to their 1503 1510. questionnaires for evaluation, their scoring algorithms and supplementary 18 Shannon J, Kristal AR, Curry SJ, Beresford SA. Application of a behavioral approach fi information on usage and copyright. We thank Amir Emadian for independent data to measuring dietary change: the fat- and ber-related diet behavior ques- 6 – extraction on 25% of the included papers. Clare England is supported by NIHR Clinical tionnaire. Cancer Epidemiol Biomarkers Prev 1997; :355 361. Doctoral Research Fellowship 10-017. The study was carried out at The University of 19 Bogers RP, Van Assema P, Kester ADM, Westerterp KR, Dagnelie PC. Bristol, Senate House, Tyndall Avenue, Bristol BS8 1TH. Reproducibility, validity, and responsiveness to change of a short questionnaire for measuring fruit and vegetable intake. Am J Epidemiol 2004; 159: 900–909. DECLARATION 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: This submission represents original work that has not been published previously and 181–185. it is not being considered for publication elsewhere. 21 Glasgow RE, Perry JD, Toobert DJ, Hollis JF. Brief assessments of dietary behavior in field settings. Addict Behav 1996; 21: 239–247. AUTHOR CONTRIBUTIONS 22 Calfas KJ, Zabinski MF, Rupp J. Practical nutrition assessment in primary care settings: a review. Am J Prev Med 2000; 18:289–299. The work contained in this article is part of the PhD of Clare England which is 23 National Cancer Institute. Register of Validated Short Dietary Assessment Instru- supervised by Drs’ Andrews, Jago and Thompson. 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