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A systematic review of methods to assess intake of sugar-sweetened beverages among healthy European adults and children: a DEDIPAC (DEterminants of DIet and Physical Activity) study

Published online by Cambridge University Press:  21 October 2016

Fiona Riordan*
Affiliation:
Department of Epidemiology and Public Health, University College Cork, Western Road, Cork, Republic of Ireland
Kathleen Ryan
Affiliation:
School of Applied Psychology, University College Cork, Cork, Republic of Ireland
Ivan J Perry
Affiliation:
Department of Epidemiology and Public Health, University College Cork, Western Road, Cork, Republic of Ireland
Matthias B Schulze
Affiliation:
Department of Molecular Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbrücke, Germany
Lene Frost Andersen
Affiliation:
Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
Anouk Geelen
Affiliation:
Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands
Pieter van’t Veer
Affiliation:
Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands
Simone Eussen
Affiliation:
Department of Epidemiology of the Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
Martien van Dongen
Affiliation:
Department of Epidemiology of the Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
Nicole Wijckmans-Duysens
Affiliation:
Department of Epidemiology of the Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
Janas M Harrington
Affiliation:
Department of Epidemiology and Public Health, University College Cork, Western Road, Cork, Republic of Ireland
*
*Corresponding author: Email fiona.riordan@ucc.ie
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Abstract

Objective

Research indicates that intake of sugar-sweetened beverages (SSB) may be associated with negative health consequences. However, differences between assessment methods can affect the comparability of intake data across studies. The current review aimed to identify methods used to assess SSB intake among children and adults in pan-European studies and to inform the development of the DEDIPAC (DEterminants of DIet and Physical Activity) toolbox of methods suitable for use in future European studies.

Design

A literature search was conducted using three electronic databases and by hand-searching reference lists. English-language studies of any design which assessed SSB consumption were included in the review.

Setting

Studies involving two or more European countries were included in the review.

Subjects

Healthy, free-living children and adults.

Results

The review identified twenty-three pan-European studies which assessed intake of SSB. The FFQ was the most commonly used (n 24), followed by the 24 h recall (n 6) and diet records (n 1). There were several differences between the identified FFQ, including the definition of SSB used. In total, seven instruments that were tested for validity were selected as potentially suitable to assess SSB intake among adults (n 1), adolescents (n 3) and children (n 3).

Conclusions

The current review highlights the need for instruments to use an agreed definition of SSB. Methods that were tested for validity and used in pan-European populations encompassing a range of countries were identified. These methods should be considered for use by future studies focused on evaluating consumption of SSB.

Type
Research Papers
Copyright
Copyright © The Authors 2016 

A poor-quality diet is associated with non-communicable diseases( Reference Hanson, Rutten and Wouters 1 Reference Walda, Tabak and Smit 4 ) and there is a growing body of research indicating that the consumption of sugar-sweetened beverages (SSB) may be associated with negative health consequences, including the development of metabolic syndrome and higher blood pressure( Reference Dhingra, Sullivan and Jacques 5 ), an increased risk of diabetes( Reference Malik, Popkin and Bray 6 ), increased body weight( Reference Vartanian, Schwartz and Brownell 7 ) and obesity( Reference Malik, Pan and Willett 8 ). One of the recommendations made by the WHO Global Strategy on Diet and Physical Activity is the limiting of sugar and salt intake( Reference Waxman 9 ). SSB include drinks that are sweetened with sugar, other calorific sweeteners and corn syrups, as well as encompassing carbonated and non-carbonated drinks. In recent years there has been a global increase in the consumption of SSB( Reference Popkin and Nielsen 10 , Reference Popkin and Hawkes 11 ), which are characterised by their low nutritional content and failure to provide a feeling of fullness( 12 ).

Recent studies suggest that levels of overweight and obesity are increasing in Europe( Reference Finucane, Stevens and Cowan 13 , 14 ). However, while evidence suggests that reducing the intake of SSB would lead to a significant reduction in the incidence of obesity as well as other chronic illness such as diabetes (type 2)( Reference Hu 15 , Reference Imamura, O’Connor and Ye 16 ), the link between obesity and intake of SSB is one that continues to be examined, with mixed results( Reference Malik, Pan and Willett 8 , Reference Gibson 17 Reference Caprio 22 ). Many reasons for this inconsistency have been indicated, including methodological differences between studies and differing characteristics of assessment instruments, such as differences in the units of serving size, frequency categories and the definitions of SSB used( Reference Gibson 17 ). Using standardised instruments and assessment methods across European populations has the potential to strengthen the investigation of associations between SSB and health outcomes such as obesity and to facilitate the collection of valid and comparable dietary intake data, along with the tracking of regional trends( Reference Blanquer, Garcia-Alvarez and Ribas-Barba 23 ).

There has been increasing focus on the standardisation and harmonisation of food classification systems and food composition databases between European countries (e.g. the International Food Data Systems Project, the Eurofoods initiative, the Food-Linked Agro-Industrial Research programme, COST Action 99, TRANSFAIR study, EUROFIR, etc.)( Reference Blanquer, Garcia-Alvarez and Ribas-Barba 23 Reference Riboli and Kaaks 29 ). The IDAMES (Innovative Dietary Assessment Methods in Epidemiological Studies and Public Health) project has evaluated new methods of dietary intake assessment in Europe( 30 ), developing the European Food Propensity Questionnaire for use in European countries. Although the European Food Safety Authority indicates that a computerised method (e.g. EPIC-SOFT or similar) should be used for collection of standardised dietary intake data at the European level( 31 , 32 ), standards have not yet been developed for the assessment of dietary intake, including intake of SSB, as part of aetiological studies. Thematic Area 1 of the DEDIPAC (Determinants of Diet and Physical Activity) project( 33 ), a pillar of the EU Joint Programme Initiative ‘Healthy Diet for a Healthy Life’, in part aims to address this gap by determining the most effective, harmonised methods of dietary intake assessment and preparing a toolkit of the most useful measurement tools of dietary intake that can be used extensively across Europe( 33 , Reference Lakerveld, van der Ploeg and Kroeze 34 ). The aim of the current systematic literature review was to identify suitable assessment methods that may potentially be used to measure intake of SSB in European children and adults in pan-European studies. These methods will later be assessed for their effectiveness as part of their inclusion in the DEDIPAC toolkit.

Methods

Data sources and study selection

The current review adheres to the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement. The protocol for the review can be accessed from PROSPERO (CRD42014012890)( 36 ). A systematic literature search was conducted for pan-European studies that assessed the intake of SSB. A definition of SSB that encompasses drinks with pre-added sugar, including soft drinks and energy drinks (carbonated and non-carbonated drinks) and cordials/squashes, was used; that is, the definition excluded drinks where sugar is added by the consumer (e.g. coffee and tea) and diet soft drinks. Two authors, F.R. and K.R., independently conducted a search of PubMed, EMBASE and Web of Science databases, using combinations of search terms for SSB such as ‘carbonated drink/s’, ‘soft drink/s’, ‘fizzy drink/s’, ‘energy drink/s’, ‘sugar-sweetened beverage/s’ and ‘soda/s’, along with keywords for dietary intake including ‘diet’, ‘eating’, ‘consumption’ and ‘intake’, and search terms for European countries (see online supplementary material, Supplemental Table 1). All searches were limited to literature published from 1990 through to 9 June 2014 (Fig. 1).

Fig. 1 Flow diagram showing study selection process for the current review (SSB, sugar-sweetened beverages; F&V, fruits and vegetables)

Titles and abstracts of the sourced articles were independently screened by F.R. and K.R. If in doubt regarding inclusion, the article was retained for full-text review. Any disagreement during the full-text review stage was resolved through consultation with a third author, J.M.H. Articles were included if they assessed the intake of SSB within two or more European countries (EU countries as defined by the Council of Europe)( 36 ). The aim of the review was to identify instruments that are suitable to assess SSB intake in the general, healthy adult or child populations. Therefore, study participants were required to be free-living, healthy populations of any age. Hospital-based populations, along with studies which focused on a specific disease subgroup (e.g. diabetic patients) or any fixed societal subgroups (e.g. pregnant women), were excluded. If studies included or compared two cohorts, one of which was a healthy general population, they were included. Intervention studies were eligible provided intake of SSB was measured at baseline. Similarly, case–control studies were included if intake was assessed in population-based controls. Studies were included only if they assessed intake of SSB at the level of the individual; that is, studies which assessed household-level consumption of SSB were excluded (Fig. 1).

Reference lists of all included papers, along with relevant meta-analyses and literature reviews, were reviewed for further publications not identified by the original search. Databases were also searched using the names of individual European projects listed in the DEDIPAC Inventory of Relevant European Studies, a compilation of studies which is an ongoing part of DEDIPAC. Authors were contacted to obtain full versions of the relevant instruments or questionnaires and some articles, and the Endnote library of a concurrently occurring systematic literature review on methods to assess intake of fruits and vegetables (F&V)( Reference Riordan, Ryan and Perry 37 ) was reviewed for further studies.

Data extraction and quality assessment

Data extraction was carried out using the same approach as outlined in the F&V review( 38 ); that is, extracting details on study design, number and names of European countries involved, sample size (total and number for each country), age range of the included population, the method used and its description (including frequency categories for FFQ, total number of items/items that referred to SSB, details of nutrient intake assessment, details of portion estimation), mode of administration, and details on the validity or reproducibility testing. Originally sourced articles describing the methods in the most detail were selected for inclusion, with further information obtained from articles sourced from reference lists. One reviewer (F.R.) extracted the data for each study, which was confirmed by the other reviewer (K.R.).

As with the review of methods to assess F&V intake( Reference Riordan, Ryan and Perry 37 ), a comprehensive quality appraisal of each included article was not conducted as part of the current review; however, relevant validation studies were referenced where possible and data were extracted from these studies by M.v.D., S.E. and N.W.-D. To determine which instruments would be appropriate to use in pan-European studies, two criteria were applied: (i) the instrument was tested for validity and/or reproducibility; and (ii) the instrument was used in more than two countries simultaneously that represent a range of European regions. A range implied that at least one country from at least three of the Southern, Northern, Eastern and Western European regions, as defined by the United Nations, were included( 38 ).

Results

Description of the included studies

The initial search identified 1949 papers, of which 1290 remained once duplicates were removed. After title and abstract screening, 1188 papers were excluded (Fig. 1). Full-text papers were sourced and reviewed for 102 papers, of which forty-eight were ultimately retained. These articles were grouped according to the major European project to which they belonged (n 44) or as ‘Other’ if they did not belong to a project (n 4; see Fig. 1 for breakdown of papers). From these forty-eight articles, sixteen articles were selected which best described the background to the project or the method used; one to three articles were typically selected per project, with the exception of the ToyBox study where articles obtained from authors were used in favour of the sourced article. Reviewing the reference lists yielded eighteen further articles in which the methods were described( Reference Riboli and Kaaks 29 , Reference Ahrens, Bammann and Siani 39 Reference Maes, Cook and Ottovaere 55 ).

Fourteen further articles were obtained through correspondence with authors; and ten articles were obtained from the F&V Endnote library, which identified seven additional studies assessing the intake of SSB, namely CNSHS (Cross National Student Health Survey)( Reference El Ansari, Stock and Mikolajczyk 56 , Reference Mikolajczyk, El Ansari and Maxwell 57 ), HAPIEE (Health, Alcohol and Psychosocial factors in Eastern Europe)( Reference Boylan, Welch and Pikhart 58 , Reference Peasey, Bobak and Kubinova 59 ), Finbalt Health Monitor( Reference Prattala, Paalanen and Grinberga 60 ), MEDIS (MEDiterranean Islands Study)( Reference Tyrovolas, Psaltopoulou and Pounis 61 ), MGSD (Mediterranean Group for the Study of Diabetes)( Reference Karamanos, Thanopoulou and Angelico 62 ), ISAAC (International Study of Asthma and Allergies in Childhood)( Reference Weiland, Bjorksten and Brunekreef 63 , Reference Nagel, Weinmayr and Kleiner 64 ) and the Finnish and Russian Karelia study( Reference Paalanen, Prattala and Palosuo 65 ). Unpublished details on the instruments used as part of the I.Family Project( 66 ), successor to the IDEFICS (Identification and prevention of Dietary-and lifestyle-induced health EFfects In Children and infantS) study, were obtained through contact with the IDEFICS group. Articles on the background and validity testing as part of the Food4Me project, published after the search dates, were also added to the review (n 3). The characteristics of the included studies( Reference Riboli and Kaaks 29 , Reference Ahrens, Bammann and Siani 39 , Reference Bel-Serrat, Mouratidou and Pala 40 , Reference de Groot and van Staveren 42 Reference Boylan, Welch and Pikhart 58 , Reference Karamanos, Thanopoulou and Angelico 62 Reference Patterson, Warnberg and Kearney 119 ) are described in Table 1.

Table 1 Summary of all studies identified to assess sugar-sweetened beverages (SSB): design, population studied, dietary assessment instruments used and details of testing for validity and/or reproducibility. Studies were selected to be potentially suitable to assess SSB intake based on (i) the instrument was tested for validity and/or reproducibility and (ii) the instrument was used in more than two countries simultaneously which represent a range of European regions; and are indicated by ticks in the last column. Where validation or reliability data was not available for SSB specifically, this is highlighted in bold font

CNSHS, Cross National Student Health Survey; ENERGY, EuropeaN Energy balance Research to prevent excessive weight Gain among Youth; EPIC, European Prospective Investigation into Cancer and Nutrition; ESCAREL, European Study in Non-Carious Cervical Lesions; HAPIEE, Health, Alcohol and Psychosocial factors in Eastern Europe; MEDIS, MEDiterranean Islands Study; MGSD, Mediterranean Group for the Study of Diabetes; SENECA, Survey in Europe on Nutrition and the Elderly; a Concerted Action; HBSC, Health Behaviour in School-aged Children; HELENA, Healthy Lifestyle in Europe by Nutrition in Adolescence; TEMPEST, ‘Temptations to Eat Moderated by Personal and Environmental Self-regulatory Tools’; EYHS, European Youth Heart Study; IDEFICS, Identification and prevention of Dietary-and lifestyle-induced health EFfects In Children and infantS; ISAAC, International Study of Asthma and Allergies in Childhood; NR, not reported; 24-HDR, 24 h recall; PCQ, Primary Caregiver’s Questionnaire; CEHQ, Children’s Eating Habits Questionnaire; EFCOVAL, European Food Consumption Validation; YANA-C, Young Adolescents’ Nutrition Assessment on Computer.

* Funded by the Wellcome Trust programme grant entitled ‘Determinants of Cardiovascular Diseases in Eastern Europe: A multi-centre cohort study’ (reference number 064947/Z/01/Z) and developed by Martin Bobak, Anne Peasey, Hynek Pikhart (UCL), Ruzena Kubinova, Lubomíra Milla Novosibirsk, Sofia Malyutina, Oksana Bragina (Prague), Andrzej Pajak, Aleksandra Gilis-Januszewska (Krakow).

Validity or reproducibility of the instrument was not reported in the article and no reference to validation or reproducibility studies was provided.

Original instrument was obtained for review.

§ ‘Other beverages’ includes everything except milk, alcoholic beverages, tea and coffee.

As with the F&V review( Reference Riordan, Ryan and Perry 37 ), the term ‘study’ refers to a larger project and not individual analyses/publications arising the same project and using the same methodology. In total, sixty-one articles on twenty-three studies were included in the current review: original search (n 16), from reference lists (n 18), from the concurrent F&V review (n 10), from authors (n 14) and added subsequently (n 3). In total, twelve large pan-European studies were identified which assessed intake of SSB( Reference Riboli and Kaaks 29 , Reference Ahrens, Bammann and Siani 39 , Reference Moreno, De Henauw and Gonzalez-Gross 44 , Reference van Staveren, de Groot and Burema 50 , Reference van Stralen, te Velde and Singh 51 , Reference Karamanos, Thanopoulou and Angelico 62 , Reference Nagel, Weinmayr and Kleiner 64 , 66 , Reference Zaborskis, Moceviciene and Iannotti 67 , Reference Manios, Androutsos and Katsarou 80 , Reference Forster, Fallaize and Gallagher 105 , Reference West, Sanz and Lussi 110 ) along with eleven smaller studies which typically were conducted in two to four countries( Reference El Ansari, Stock and Mikolajczyk 56 , Reference Boylan, Welch and Pikhart 58 , Reference Prattala, Paalanen and Grinberga 60 , Reference Tyrovolas, Psaltopoulou and Pounis 61 , Reference Paalanen, Prattala and Palosuo 65 , Reference Larsson, Klock and Astrom 71 Reference Luszczynska, de Wit and de Vet 73 , Reference Cinar and Murtomaa 76 , Reference Zheng, Rangan and Olsen 78 , Reference Kolarzyk, Shpakou and Kleszczewska 107 ). Twelve studies assessed the intake of SSB in children, aged 3–6 years( Reference Mouratidou, Miguel and Androutsos 82 , Reference Gonzalez-Gil, Mouratidou and Cardon 83 ), 2–9 years( Reference Ahrens, Bammann and Siani 39 , Reference Bel-Serrat, Mouratidou and Pala 40 , Reference Olafsdottir, Berg and Eiben 79 ), 8–12 years( Reference Weiland, Bjorksten and Brunekreef 63 , Reference Nagel, Weinmayr and Kleiner 64 ) or 10–12 years( Reference van Stralen, te Velde and Singh 51 , Reference Cinar and Murtomaa 76 , Reference Brug, van Stralen and Te Velde 77 ), or adolescents( Reference Ahrens, Bammann and Siani 39 , Reference Moreno, De Henauw and Gonzalez-Gross 44 , 66 , Reference Zaborskis, Moceviciene and Iannotti 67 , Reference Larsson, Klock and Astrom 71 Reference Luszczynska, de Wit and de Vet 73 , Reference Cinar and Murtomaa 76 , Reference Zheng, Rangan and Olsen 78 ). Fourteen studies assessed intake among adults only( Reference Riboli and Kaaks 29 , Reference de Groot and van Staveren 42 , Reference El Ansari, Stock and Mikolajczyk 56 , Reference Peasey, Bobak and Kubinova 59 Reference Karamanos, Thanopoulou and Angelico 62 , Reference Paalanen, Prattala and Palosuo 65 , Reference Szczepanska, Deka and Calyniuk 72 , Reference Celis-Morales, Livingstone and Marsaux 103 , Reference Kolarzyk, Shpakou and Kleszczewska 107 , Reference West, Sanz and Lussi 110 ), two of which assessed student populations( Reference El Ansari, Stock and Mikolajczyk 56 , Reference Kolarzyk, Shpakou and Kleszczewska 107 ). Three studies, the ENERGY (EuropeaN Energy balance Research to prevent excessive weight Gain among Youth), ToyBox and the I.Family projects, assessed both children and their parent or guardian( Reference van Stralen, te Velde and Singh 51 , 66 , Reference Manios, Androutsos and Katsarou 80 Reference Pil, Putman and Cardon 90 ). A further eight articles were sourced in which validity and/or reproducibility testing for the identified methods was described( Reference Crispim, Geelen and Souverein 91 Reference Willett, Sampson and Stampfer 94 , Reference Nes, van Staveren and Zajkas 96 , Reference Lytle, Nichaman and Obarzanek 99 , Reference Vandevijvere, Geelen and Gonzalez-Gross 120 ).

Dietary assessment methods

Types of methods

Several methods were used to assess dietary intake of SSB in the identified studies, but most used FFQ (n 24)( Reference Riboli, Hunt and Slimani 47 , Reference van Stralen, te Velde and Singh 51 , Reference Vereecken, De Bourdeaudhuij and Maes 54 , Reference El Ansari, Stock and Mikolajczyk 56 , Reference Peasey, Bobak and Kubinova 59 Reference Paalanen, Prattala and Palosuo 65 , Reference Zaborskis, Moceviciene and Iannotti 67 , 69 , Reference Larsson, Klock and Astrom 71 Reference Luszczynska, de Wit and de Vet 73 , Reference Cinar and Murtomaa 76 , Reference Mouratidou, Miguel and Androutsos 82 , Reference Huybrechts, Bornhorst and Pala 101 , Reference Celis-Morales, Livingstone and Marsaux 103 , Reference Kolarzyk, Shpakou and Kleszczewska 107 , Reference West, Sanz and Lussi 110 , Reference Bel-Serrat, Mouratidou and Santaliestra-Pasias 121 ). Other methods identified through the review were 24h recalls (24-HDR; n 6)( Reference Riddoch, Edwards and Page 48 , Reference van Stralen, te Velde and Singh 51 , Reference Vereecken, Covents and Matthys 52 , Reference Zheng, Rangan and Olsen 78 , Reference Bel-Serrat, Fernandez Alvira and Pala 113 , Reference Slimani, Fahey and Welch 122 ) and diet records/diet diaries( Reference Haveman-Nies, De Groot and Van Staveren 102 ). Most studies assessed intake of SSB using a single method, although four – ENERGY, IDEFICS and I.Family Project, and HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) – used and described a second assessment methodology that supplemented or tested the study’s primary method for validity. FFQ along with 24-HDR were used in the ENERGY, IDEFICS and HELENA projects. EPIC (European Prospective Investigation into Cancer and Nutrition) used a highly standardised 24-HDR, EPIC-SOFT, in a representative sub-sample from each cohort, which served as a common reference measurement across the different study populations, to calibrate and account for differences in the country-specific FFQ used as part of the study( Reference van Staveren, de Groot and Burema 50 , Reference Margetts and Pietinen 92 , Reference Lanfer, Hebestreit and Ahrens 112 ). Since a common FFQ instrument was not used across all countries in EPIC, only the EPIC-SOFT instrument is discussed in the present review. Similarly, as the EYHS (European Youth Heart Study) FFQ was reported as part of the Danish component of the study, so only the 24-HDR, preceded by the 1d record, is discussed herein.

According to the two selection criteria (Table 1), several study instruments appeared appropriate to assess intake of SSB in future pan-European studies. Instruments that had been used among adult populations and that fulfilled the criteria included EPIC-SOFT, the Food4Me FFQ, the SENECA (Survey in Europe on Nutrition and the Elderly; a Concerted Action) modified dietary history method, the ToyBox Primary Caregiver’s Questionnaire and the ENERGY parent questionnaire. Three instruments used to assess intake among adolescents, namely HELENA-DIAT (Dietary Assessment Tool), the HELENA online FFQ and the HBSC (Health Behaviour in School-aged Children) FFQ, fulfilled the criteria, as did the IDEFICS 24-HDR (SACINA) and Children’s Eating Habits Questionnaire (CEHQ-FFQ), the ENERGY Children’s Questionnaire (FFQ and pre-coded 24-HDR) and the ToyBox Children’s Questionnaire (FFQ), all of which were used among children. The I.Family instrument was based on those developed for the IDEFICS study. The 24-HDR preceded by the 1d qualitative food record used in the EYHS was tested for validity among children from the USA but not in a European population( Reference Lytle, Nichaman and Obarzanek 99 ). The 24-HDR was compared with observational data on consumption collected by parents and teachers. The instruments selected according to the two criteria are indicated by ticks in Table 1. However, in order to make the review more comprehensive, details on all the identified methods are provided.

Validation

From the studies that were tested for validity or reproducibility and fulfilled the first criterion (Table 1), validity and reliability of FFQ was assessed using FFQ( Reference Forster, Fallaize and Gallagher 105 ), food records( Reference Vereecken, De Bourdeaudhuij and Maes 54 , Reference Vereecken and Maes 97 , Reference Huybrechts, De Backer and De Bacquer 100 , Reference Fallaize, Forster and Macready 104 ), 24-HDR( Reference Bel-Serrat, Mouratidou and Pala 40 , Reference Vereecken, De Bourdeaudhuij and Maes 54 ) or interviews( Reference Vereecken, Covents and Sichert-Hellert 53 , Reference Singh, Vik and Chinapaw 98 ) as reference methods. In eleven studies, validity was assessed by crude correlations (n 7)( Reference Bel-Serrat, Mouratidou and Pala 40 , Reference Vereecken, Covents and Matthys 52 Reference Vereecken, De Bourdeaudhuij and Maes 54 , Reference Vereecken and Maes 97 , Reference Huybrechts, De Backer and De Bacquer 100 , Reference Fallaize, Forster and Macready 104 ), energy-adjusted correlations (n 1)( Reference Forster, Fallaize and Gallagher 105 ), de-attenuated correlation coefficients (n 2)( Reference Bel-Serrat, Mouratidou and Pala 40 , Reference Huybrechts, De Backer and De Bacquer 100 ), mean or median differences in SSB consumption (n 7)( Reference Bel-Serrat, Mouratidou and Pala 40 , Reference Vereecken, Covents and Matthys 52 Reference Vereecken, De Bourdeaudhuij and Maes 54 , Reference Vereecken and Maes 97 , Reference Huybrechts, De Backer and De Bacquer 100 , Reference Forster, Fallaize and Gallagher 105 ), exact level of agreement of SSB consumption (n 8)( Reference Bel-Serrat, Mouratidou and Pala 40 , Reference Vereecken, Covents and Sichert-Hellert 53 , Reference Vereecken, De Bourdeaudhuij and Maes 54 , Reference Vereecken and Maes 97 , Reference Huybrechts, De Backer and De Bacquer 100 , Reference Fallaize, Forster and Macready 104 , Reference Forster, Fallaize and Gallagher 105 ), Bland–Altman plots (n 4)( Reference Bel-Serrat, Mouratidou and Pala 40 , Reference Vereecken, Covents and Sichert-Hellert 53 , Reference Vereecken, De Bourdeaudhuij and Maes 54 , Reference Forster, Fallaize and Gallagher 105 ), intraclass correlation coefficients( Reference Singh, Vik and Chinapaw 98 ) or weighted kappa (n 2)( Reference Vereecken, De Bourdeaudhuij and Maes 54 , Reference Huybrechts, De Backer and De Bacquer 100 ) between the FFQ and reference instrument. In seven studies, reliability of SSB consumption was assessed by correlations (n 5)( Reference Vereecken, De Bourdeaudhuij and Maes 54 , Reference Gonzalez-Gil, Mouratidou and Cardon 83 , Reference Vereecken and Maes 97 , Reference Huybrechts, De Backer and De Bacquer 100 , Reference Lanfer, Hebestreit and Ahrens 112 ), mean/median differences (n 3)( Reference Vereecken, De Bourdeaudhuij and Maes 54 , Reference Huybrechts, De Backer and De Bacquer 100 , Reference Lanfer, Hebestreit and Ahrens 112 ), weighted kappa (n 2)( Reference Vereecken and Maes 97 , Reference Lanfer, Hebestreit and Ahrens 112 ) or intraclass correlation coefficients( Reference Singh, Vik and Chinapaw 98 ) between subsequent administrations of the FFQ. Details on the validation and/or reproducibility are provided in Table 1 and, where available, extracted results for the statistical assessments are provided in the online supplementary material, Supplemental Table 2.

Validation data specifically on SSB were available for only six instruments, three among adolescents, the HBSC FFQ, HELENA-DIAT and HELENA FFQ, and three among children, the ENERGY Children’s Questionnaire, IDEFICS FFQ and ToyBox Children’s Questionnaire. The Food4Me FFQ, for use among adult populations, provided validation data only for ‘Other beverages’ grouped together, described as including all beverages except milk, alcoholic beverages, tea and coffee( Reference Fallaize, Forster and Macready 104 ) and including fruit juices, carbonated beverages and squash( Reference Forster, Fallaize and Gallagher 105 ). These instruments are summarised in Table 2.

Table 2 Summary of the selected instruments which were tested for validity (n 7) for assessment of sugar-sweetened beverages (SSB): design, age group, countries, mode of administration, definition of SSB used and portion estimation

HBSC, Health Behaviour in School-aged Children; HELENA, Healthy Lifestyle in Europe by Nutrition in Adolescence; 24-HDR, 24 h recall; IDEFICS, Identification and prevention of Dietary- and lifestyle-induced health effects In Children and infants; CEHQ, Children’s Eating Habits Questionnaire; ENERGY, EuropeaN Energy balance Research to prevent excessive weight Gain among Youth; self-admin., self-administered.

* Original instrument obtained for review.

Although data were not specific to SSB, the Food4Me FFQ had moderate agreement (0·4–0·6) for ‘other beverages’ with a 4d diet record using Spearman’s crude correlation (r=0·66)( Reference Fallaize, Forster and Macready 104 ) and good agreement (>0·6) with the EPIC-Norfolk FFQ using energy-adjusted coefficients (r=0·79)( Reference Forster, Fallaize and Gallagher 105 ). In terms of instruments for adolescents, the HBSC FFQ tested for validity against a 7d diet record had moderate agreement for ‘soft drinks’ using Spearman’s crude correlation (r=0·46). HELENA-DIAT had moderate to good agreement with a 1d food record (r=0·42) and 24-HDR (r=0·65)( Reference Vereecken, Covents and Matthys 52 ), and the HELENA FFQ had good agreement (r=0·79) when tested for validity against four 24-HDR( Reference Vereecken, De Bourdeaudhuij and Maes 54 ). In terms of instruments to be used among children, the construct validity of the ENERGY questionnaire was tested, with moderate (55 %) exact level of agreement for ‘soft drinks’ between the self-completed questionnaire compared with the questionnaire completed by interview( Reference Singh, Vik and Chinapaw 98 ). The ToyBox instrument had moderate to good agreement with a 3d diet record for ‘sugared drinks’ (r=0·57)( Reference Huybrechts, De Backer and De Bacquer 100 ), while the IDEFICS CEHQ-FFQ tested for validity against a 24-HDR had low agreement (<0·4) for ‘soft drinks’ using Pearson’s crude correlations among children aged <6 years (r=0·14) and 6–9 years (r=0·21)( Reference Bel-Serrat, Fernandez Alvira and Pala 113 ) (see online supplementary material, Supplemental Table 2).

FFQ

Table 3 summarises the characteristics of the identified FFQ. These instruments are already described in detail as part of the concurrent review on F&V intake( Reference Riordan, Ryan and Perry 37 ), with the exception of the FFQ used by Cinar and Murtomaa( Reference Cinar and Murtomaa 76 ) and Kolarzyk et al. ( Reference Kolarzyk, Shpakou and Kleszczewska 107 ). Therefore, the results focus on aspects which are specific to the assessment of SSB; that is, definitions and portion measurement, of which there were notable differences between the instruments identified. FFQ were used to assess dietary intake, identify determinants of dietary intake or test diet–disease associations and identify disease risk factors.

Table 3 Summary of the FFQ (n 24) identified for the assessment of sugar-sweetened beverages (SSB): number of items, instrument purpose, population, definition of SSB, reference period, mode, frequency categories and portion estimation

CNSHS, Cross National Student Health Survey; ENERGY, EuropeaN Energy balance Research to prevent excessive weight Gain among Youth; ESCAREL, European Study in Non-Carious Cervical Lesions; HAPIEE, Health, Alcohol and Psychosocial factors in Eastern Europe; MEDIS, MEDiterranean Islands Study; MGSD, Mediterranean Group for the Study of Diabetes; HBSC, Health Behaviour in School-aged Children; HELENA, Healthy Lifestyle in Europe by Nutrition in Adolescence; TEMPEST, ‘Temptations to Eat Moderated by Personal and Environmental Self-regulatory Tools’; IDEFICS, Identification and prevention of Dietary- and lifestyle-induced health EFfects In Children and infantS; ISAAC, International Study of Asthma and Allergies in Childhood; EBRB, energy balance-related behaviours; F&V, fruits and vegetables; NR, not reported; self-admin., self-administered.

* Original instrument was obtained for review.

Information on the Food4Me instrument was obtained through contact with study authors.

Range of items and definitions

SSB were referred to as ‘soft drinks’ (HELENA, Finbalt Health Monitor, ENERGY Parent Questionnaire, CNSHS and MEDIS), ‘soft drinks with sugar’ (Finnish FFQ of the Finnish and Russian Karelia project, EYHS), ‘juice or soft drinks’ (Russian FFQ of the Finnish and Russian Karelia project), ‘fizzy drinks’ (ISAAC, HAPIEE, ENERGY Children’s Questionnaire), ‘fizzy soft drinks’ (Food4Me), ‘carbonated drinks (Fanta, Sprite, Coke, Pepsi)’ (Szczepanska et al. ( Reference Szczepanska, Deka and Calyniuk 72 )), sweet drinks’ (Kolarzyk et al. ( Reference Kolarzyk, Shpakou and Kleszczewska 107 )) and ‘Cola-Cola, Pepsi or other sugary drinks’ (MGSD). Where two items referred to SSB on an FFQ, typically one referred to soft drinks, and another referred to squashes or cordials (MGSD, Food4Me) or pre-packed juice (ToyBox).

Few FFQ distinguished between pure fruit juice, and cordials or squashes, with the exception of the ENERGY and Food4Me studies. Some FFQ contained an item that captured intake of a sugarless or low-calorie equivalent (EHYS, HAPIEE, IDEFICS, I.Family, Food4Me, Finnish and Russian Karelia study, and Larsson et al. ( Reference Larsson, Klock and Astrom 71 )).

Portion size

Several FFQ assessed the frequency of consumption only and did not record portion size( Reference Prattala, Paalanen and Grinberga 60 , Reference Weiland, Bjorksten and Brunekreef 63 , Reference Nagel, Weinmayr and Kleiner 64 , Reference Zaborskis, Moceviciene and Iannotti 67 , Reference Larsson, Klock and Astrom 71 Reference Luszczynska, de Wit and de Vet 73 , Reference Cinar and Murtomaa 76 , Reference Kolarzyk, Shpakou and Kleszczewska 107 , Reference West, Sanz and Lussi 110 ). Of the semi-quantitative FFQ that did assess portion size, many did so in-line using a standard measure and asking participants to specify the number of glasses of SSB consumed( Reference van Stralen, te Velde and Singh 51 , Reference Vereecken, De Bourdeaudhuij and Maes 54 , Reference Boylan, Welch and Pikhart 58 , Reference Karamanos, Thanopoulou and Angelico 62 , Reference Luszczynska, de Wit and de Vet 73 , Reference Mouratidou, Miguel and Androutsos 82 ), in some cases specifying volume. The MGSD FFQ gave a standard portion as 300 ml or 1 can, whereas in the HAPIEE FFQ this was 2 dl. The EHYS FFQ informed participants that one glass approximated to 1 small glass bottle or 2 glasses approximated to a ½ litre bottle.

The ENERGY and ToyBox children’s questionnaires provided the greatest detail on portion sizes. The former questionnaire asked participants to report the number of glasses or small bottles, cans and/or bottles, and specified volumes for each. The ToyBox questionnaire asked participants to select the average portion size ranging from ‘100 ml or less’ to ‘1000 ml or more’, and provided the volumes of typical containers. As part of the ToyBox FFQ, a photographic food guide was also provided to assist with portion size estimation. The Food4Me FFQ asked participants to select from a range of photographs which were linked electronically to portion sizes (in grams).

Dietary recalls

The characteristics of the identified 24-HDR are summarised in Table 4. The majority of the 24-HDR were used to determine estimates of dietary intake, comparing estimates across regions or over time. These instruments are already described in detail as part of the concurrent review on F&V intake( Reference Riordan, Ryan and Perry 37 ); therefore only details on portion measurement are reported here.

Table 4 Summary of the 24 h recalls (24-HDR; n 6) identified for the assessment of sugar-sweetened beverages (SSB): population, instrument purpose, mode, structure, prompts and portion estimation

EPIC, European Prospective Investigation into Cancer and Nutrition; HELENA, Healthy Lifestyle in Europe by Nutrition in Adolescence; EYHS, European Youth Heart Study; ENERGY, EuropeaN Energy balance Research to prevent excessive weight Gain among Youth; IDEFICS, Identification and prevention of Dietary- and lifestyle-induced health EFfects In Children and infants; EBRB, energy-balance-related behaviours; self-admin., self-administered; F&V, fruits and vegetables.

* Original instrument obtained for review.

On the ENERGY 24-HDR, participants could select 200 ml, 350 ml or 500 ml by way of selecting a glass or small bottle, a can or a large bottle, respectively( Reference van Stralen, te Velde and Singh 51 ). Portion size was also assessed using the HELENA-DIAT, SACINA and EPIC-SOFT instruments. EPIC-SOFT used six quantification methods including photographs and standard measures, both of which were used by the HELENA-DIAT tool. The IDEFICS SACINA tool measured SSB portion by glass using photographs of six different glass sizes. The EYHS 24-HDR interview was accompanied by different-sized drinking glasses and photographs to aid portion estimation.

Diet records/diet diaries

Only one diet record was identified, the self-completed 3d estimated record which was used in the SENECA study. This instrument is already described in detail in the concurrent review of instruments to assess F&V intake( Reference Riordan, Ryan and Perry 37 ). The purpose of the study was to examine cross-cultural differences in nutrition and lifestyle factors( Reference de Groot, Hautvast and van Staveren 43 ) and cross-cultural variations and changes in intake over time( Reference Schroll, Moreiras-Varela and Schlettwein-Gsell 123 ). The population sampled was adults aged 70–75 years. The 3d record was used in conjunction with a frequency checklist of foods that was adapted to local food customs and the order in which they typically appear, and used during the follow-up interview to verify the record. For example, SSB were listed on the Dutch checklist as ‘lemonades with sugar’( Reference de Groot and van Staveren 42 , Reference Haveman-Nies, De Groot and Van Staveren 102 ).

Discussion

The aim of the current review was to identify the methods used to assess intake of SSB in pan-European studies. The main dietary assessment methods were the FFQ, 24-HDR and diet record/diet diary. The review identified twelve instruments to assess intake of SSB in children or adolescents in the age range of 2–12 years, seven among children and six among adolescents. Fourteen instruments were identified that assessed intake of SSB among adults, three of which assessed parents or caregivers. Of the identified FFQ, thirteen could be used among adult populations, six among adolescents and six among children. A few key differences were identified between the methods, some of which have been reported previously( Reference Cade, Thompson and Burley 124 , Reference Thompson and Subar 125 ). For example, in terms of the FFQ, differences included: the definition of SSB used; the number and range of frequency categories; the time period covered by the FFQ; and the approach to determining portion size. Such differences, in particular, how SSB are defined, should be resolved if future instruments are to be standardised across Europe.

The present review is the first to systematically identify and describe instruments used to assess intake of SSB in pan-European studies. Although a growing body of research points to a possible association between the consumption of SSB and obesity, there is currently a lack of standardised instruments available for use in pan-European studies when measuring and monitoring the intake of SSB. A large number of instruments were identified through the review. Similar to the approach used for the concurrent review of methods to assess F&V intake( Reference Riordan, Ryan and Perry 37 ), to reduce the number of instruments, identify potential instruments for use in future pan-European studies measuring SSB intake and determine those to be included in the DEDIPAC toolbox, two selection criteria were applied: (i) the instrument was tested for validity and/or reproducibility; and (ii) the instrument was used in more than two countries simultaneously which represented a range of European regions.

According to these selection criteria, five instruments were considered appropriate to assess SSB among adults in pan-European studies, namely those used by the EPIC, Food4Me, SENECA, ToyBox and ENERGY studies. However, only the Food4Me FFQ was tested for validity for intake of SSB (but grouped together with other drinks as ‘other beverages’) using 4d diet records( Reference Fallaize, Forster and Macready 104 ). The HELENA-DIAT( Reference Vereecken, Covents and Sichert-Hellert 53 ), HELENA online FFQ( Reference Vereecken, De Bourdeaudhuij and Maes 54 ) and HBSC FFQ( Reference Vereecken and Maes 97 ) appeared appropriate to assess intake among adolescents and demonstrated moderate to good agreement with 1d records and 24-HDR, four 24-HDR and 7d diet records, respectively. Only three of the four instruments selected to be used among children were tested for validity for SSB (IDEFICS FFQ, ENERGY and ToyBox)( Reference Singh, Vik and Chinapaw 98 , Reference Huybrechts, De Backer and De Bacquer 100 , Reference Bel-Serrat, Fernandez Alvira and Pala 113 ) and the ENERGY questionnaire was tested only for construct validity. The IDEFICS and ToyBox instruments demonstrated low agreement with 24-HDR and moderate to good agreement with a 3d diet record, respectively. It is important to note that these instruments were all tested for validity against other self-report (and potentially error-prone) methods, namely FFQ, food records, 24-HDR or interviews.

The two selection criteria, along with the summary of validation data, indicate the methods which may be appropriate to use in pan-European studies. However, as already outlined in the review of methods to assess F&V intake( Reference Riordan, Ryan and Perry 37 ), ideally, an instrument should be tested for validity in the population in which it will be used and the purpose for which the instrument is intended should be taken into account. For example, most FFQ identified by the review were used to examine determinants of dietary intake or examine the diet–disease associations, in contrast to the 24-HDR and diet records which were used mainly to assess intake for cross-cultural comparisons, or over time. FFQ are typically designed to be population-specific, to capture dietary customs and foods( Reference Thompson and Subar 125 ), and so the FFQ may not be the ideal instrument to use across several countries.

However, the current review has indicated how questions relating to SSB may best be structured, even across country-specific FFQ. For example, self-administered instruments should define SSB and provide examples to aid respondent comprehension. Furthermore, more than one item may be necessary to assess SSB; that is, the use of a single term such as ‘soft drinks’ to capture SSB may not be sufficient to fully assess intake of SSB as that term does not differentiate soft drinks, diet soft drinks and squashes. An agreement on a standardised way to assess SSB intake across instruments, including a requirement on assessing portion size in a systematic manner (i.e. clarifying units for participants, e.g. beaker=250 ml), would be a key step in harmonising the data collection in different regions. The feasibility of using the instrument is also an important consideration. Of the seven instruments that were tested for validity, three were self-administered, paper-based FFQ (IDEFICS, ToyBox, HBSC) which may require less resources (i.e. Internet/computer, personnel and time resources) than online or computer-based self-administered FFQ (HELENA, Food4Me) or 24-HDR (HELENA-DIAT). As discussed in the paper on methods to assess F&V intake( Reference Riordan, Ryan and Perry 37 ), suitability of the instrument, based on the purpose of the study, must be weighed against feasibility.

When reflecting on the best approach to assess SSB intake, balancing purpose against feasibility is particularly relevant. A 24-HDR or diet record can offer a more detailed and potentially more accurate( Reference Burrows, Martin and Collins 126 ) account of an individual’s SSB intake (particularly if records are maintained throughout the day by respondents, and/or prompted appropriately e.g. as part of HELENA-DIAT). Furthermore, as recalls or records capture SSB intake in the context of overall consumption for the day, they also offer the potential to explore intake in the light of other dietary components and dietary/meal patterns throughout the day; such as the association of consumption of sugar-rich foods with skipped/missed meals or as a marker of poor diet quality( Reference Sjoberg, Hallberg and Hoglund 127 ). However, given food intake has the potential to vary from day to day, it is generally accepted that where assessing an individual’s usual intake is of interest, a single 24-HDR is not appropriate. As SSB ideally should be consumed on an occasional basis, it is possible that an assessment over a limited time period may not reliably reflect usual intake. Much of the current research around SSB has a strong policy focus, tracking global or country-level consumption frequency and relating this to wider health concerns such as obesity or type 2 diabetes( Reference Popkin and Hawkes 11 , Reference Imamura, O’Connor and Ye 16 , Reference Malik, Popkin and Bray 128 ). FFQ, particularly if made more comparable across regions (e.g. through standardising frequency categories and definitions of SSB), are valid for this purpose, and do not incur the same respondent burden and expense as the multiple 24-HDR or records which would be required to approximate an individual’s usual intake. However, given the different opportunities offered by the different methodologies, it can be argued that to obtain a broader understanding of patterns of SSB consumption overall, both FFQ and diet records should be utilised. For example, the questionnaire used by the ENERGY study included elements of the FFQ and 24-HDR for the purposes of assessing SSB intake among children( Reference van Stralen, te Velde and Singh 51 ).

The current review is strengthened by the use of a comprehensive, broad search strategy, supplemented by hand-searching reference lists. Instruments were sourced through contact with study authors and reviewing the results of the review on methods to assess F&V intake. However, there remains the possibility that we did not identify all relevant articles. It is important to note that where a copy of the original instrument or article could not be accessed, the instrument description may be limited. The review is limited to articles published up to June 2014 and we cannot exclude the possibility that new instruments to assess SSB may have become available since the review was completed. Although we would expect more recent instruments to be similar to those identified by our review (i.e. predominantly FFQ and 24-HDR), it is possible that further online tools may have been developed. The advent of tools such as the Food4Me and HELENA FFQ( Reference Maes, Cook and Ottovaere 55 , Reference Tyrovolas, Psaltopoulou and Pounis 106 ), both which are administered online, suggests the move towards this approach, which also offers the opportunity for delivering personalised feedback messages on the basis of food intake data entered by participants.

While the review limited its focus to pan-European studies, as mentioned in the concurrent review on F&V intake( Reference Riordan, Ryan and Perry 37 ), this is not to assert that other instruments tested for validity as part of non-European studies would be unsuitable for assessing intakes across Europe. Beyond examining the outcomes of validation studies, as was the case when reviewing instruments to assess F&V intake( Reference Riordan, Ryan and Perry 37 ), the quality of the identified instruments was not assessed in the current review owing to the lack of an appraisal tool to rate dietary assessment instruments on the basis of their characteristics. As with the F&V review( Reference Riordan, Ryan and Perry 37 ), comparing the characteristics of the instruments identified in the current review could inform how quality standards around dietary assessment instruments might be developed. Although the quality of each instrument could not be fully assessed, the review has provided a shortlist of potential instruments for use in future pan-European studies through selecting instruments that had been used across more than two European countries and those tested for validity for SSB intake. These results will contribute to the development of the DEDIPAC toolbox of dietary intake assessment methods, which should provide a basis for appraising and selecting suitable instruments to use in future pan-European studies.

Conclusion

The present review has identified a range of instruments to assess intake of SSB. Results indicate key differences between the identified instruments. In order to standardise and harmonise assessment methods between European countries and increase the accuracy with which intake of SSB is measured, it is essential that a clear and agreed definition of SSB be used: one which clearly explains what is captured by the term ‘soft drinks’, and which distinguishes between sugar-free or light drinks and sugared drinks, and between pure fruit juices and squashes. The review has indicated seven methods that were tested for validity and used in pan-European studies. These methods may be most suitable to assess the intake of SSB among adult, child or adolescent populations in future pan-European studies.

Acknowledgements

Financial support: The preparation of this paper was supported by the DEDIPAC Knowledge Hub. This work was supported by the Joint Programming Initiative ‘Healthy Diet for a Healthy Life’. The funding agency supporting this work was The Health Research Board (HRB), Ireland (DEDIPAC/2013/1). Conflicts of interest: None. Authorship: F.R. planned and conducted the review, and drafted and revised the paper. K.R. planned and conducted the review, and drafted the paper. I.J.P. drafted and revised the paper. M.B.S. contributed to the planning, and drafted and revised the paper. L.F.A. contributed to the planning, and drafted and revised the paper. A.G. drafted and revised the paper. P.v.V. drafted and revised the paper. S.E. conducted the review of validation data, and drafted and revised the paper. M.v.D. conducted the review of validation data, and drafted and revised the paper. N.W.-D. conducted the review of validation data, and drafted and revised the paper. J.M.H. contributed to the planning, and drafted and revised the paper. Ethics of human subject participation: Not applicable.

Supplementary material

To view supplementary material for this article, please visit http://dx.doi.org/10.1017/S1368980016002639

References

1. Hanson, C, Rutten, EP, Wouters, EF et al. (2013) Diet and vitamin D as risk factors for lung impairment and COPD. Transl Res 162, 219236.CrossRefGoogle ScholarPubMed
2. World Health Organization (2003) Diet, Nutrition and the Prevention of Chronic Diseases. Report of a Joint WHO/FAO Expert Consultation. WHO Technical Report Series no. 916. Geneva: WHO.Google Scholar
3. Aburto, NJ, Ziolkovska, A, Hooper, L et al. (2013) Effect of lower sodium intake on health: systematic review and meta-analyses. BMJ 346, f1326.CrossRefGoogle ScholarPubMed
4. Walda, IC, Tabak, C, Smit, HA et al. (2002) Diet and 20-year chronic obstructive pulmonary disease mortality in middle-aged men from three European countries. Eur J Clin Nutr 56, 638643.Google Scholar
5. Dhingra, R, Sullivan, L, Jacques, PF et al. (2007) Soft drink consumption and risk of developing cardiometabolic risk factors and the metabolic syndrome in middle-aged adults in the community. Circulation 116, 480488.Google Scholar
6. Malik, VS, Popkin, BM, Bray, GA et al. (2010) Sugar-sweetened beverages and risk of metabolic syndrome and type 2 diabetes: a meta-analysis. Diabetes Care 33, 24772483.CrossRefGoogle ScholarPubMed
7. Vartanian, LR, Schwartz, MB & Brownell, KD (2007) Effects of soft drink consumption on nutrition and health: a systematic review and meta-analysis. Am J Public Health 97, 667675.CrossRefGoogle ScholarPubMed
8. Malik, VS, Pan, A, Willett, WC et al. (2013) Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta-analysis. Am J Clin Nutr 98, 10841102.CrossRefGoogle ScholarPubMed
9. Waxman, A (2004) WHO global strategy on diet, physical activity and health. Food Nutr Bull 25, 292302.Google Scholar
10. Popkin, BM & Nielsen, SJ (2003) The sweetening of the world’s diet. Obes Res 11, 13251332.Google Scholar
11. Popkin, BM & Hawkes, C (2016) Sweetening of the global diet, particularly beverages: patterns, trends, and policy responses. Lancet Diabetes Endocrinol 4, 174186.Google Scholar
12. Irish Heart Foundation (2013) Pre-Budget Submission 2014. Dublin: Irish Heart Foundation.Google Scholar
13. Finucane, MM, Stevens, GA, Cowan, MJ et al. (2011) National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet 377, 557567.Google Scholar
14. Organisation for Economic Co-operation and Development (2012) Health at a Glance: Europe 2012. Paris: OECD Publishing.Google Scholar
15. Hu, FB (2013) Resolved: there is sufficient scientific evidence that decreasing sugar-sweetened beverage consumption will reduce the prevalence of obesity and obesity-related diseases. Obes Rev 14, 606619.CrossRefGoogle ScholarPubMed
16. Imamura, F, O’Connor, L, Ye, Z et al. (2015) Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction. BMJ 351, h3576.Google Scholar
17. Gibson, S (2008) Sugar-sweetened soft drinks and obesity: a systematic review of the evidence from observational studies and interventions. Nutr Res Rev 21, 134147.Google Scholar
18. Malik, VS, Schulze, MB & Hu, FB (2006) Intake of sugar-sweetened beverages and weight gain: a systematic review. Am J Clin Nutr 84, 274288.CrossRefGoogle ScholarPubMed
19. Trumbo, PR & Rivers, CR (2014) Systematic review of the evidence for an association between sugar-sweetened beverage consumption and risk of obesity. Nutr Rev 72, 566574.CrossRefGoogle ScholarPubMed
20. Ebbeling, CB, Feldman, HA, Chomitz, VR et al. (2012) A randomized trial of sugar-sweetened beverages and adolescent body weight. N Engl J Med 367, 14071416.Google Scholar
21. de Ruyter, JC, Olthof, MR, Seidell, JC et al. (2012) A trial of sugar-free or sugar-sweetened beverages and body weight in children. N Engl J Med 367, 13971406.Google Scholar
22. Caprio, S (2012) Calories from soft drinks – do they matter? N Engl J Med 367, 14621463.CrossRefGoogle ScholarPubMed
23. Blanquer, M, Garcia-Alvarez, A, Ribas-Barba, L et al. (2009) How to find information on national food and nutrient consumption surveys across Europe: systematic literature review and questionnaires to selected country experts are both good strategies. Br J Nutr 101, Suppl. 2, S37S50.Google Scholar
24. National and Kapodistrian University of Athens (2014) DAFNE. Data Food Networking. http://www.nut.uoa.gr/dafnesoftweb/. (accessed September 2014).Google Scholar
25. Castenmiller, J & West, CE (1995) Report of the Third Annual FLAIR-Eurofoods-Enfant Project Meeting, Vilamoura, Portugal, 1993. Wageningen: FLAIR-Eurofoods-Enfant Project (1994).Google Scholar
26. Working Group on Food Data Management and Interchange (2000) COST Action 99. Research Action on Food Consumption and Composition Data, Eurofoods Recommendations for Food Composition Database Management and Data Interchange. Luxembourg: Office for Official Publications of the European Communities.Google Scholar
27. Norfoods project (2014) A Nordic approach to Food Composition Data. http://www.fooddata.dk/norfoods/?Nordic_food_data (accessed September 2014).Google Scholar
28. EFCOSUM Group (2001) European Food Consumption Survey Method, Final Report. TNO Report no. V3766. Zeist: TNO Nutrition and Food Research.Google Scholar
29. Riboli, E & Kaaks, R (1997) The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition. Int J Epidemiol 26, Suppl. 1, S6S14.Google Scholar
30. Innovative Dietary Assessment Methods in Epidemiological Studies and Public Health (2010) Dietary Assessment Methods: State of the Art Report. Potsdam-Rehbrücke: German Institute of Human Nutrition (DIfE).Google Scholar
31. European Food Safety Authority (2009) General principles for the collection of national food consumption data in the view of a pan-European dietary survey. EFSA J 7, 1435.Google Scholar
32. European Food Safety Authority (2014) Guidance on the EU Menu methodology. EFSA J 12, 3944.Google Scholar
33. Determinants of Diet and Physical Activity Knowledge Hub (2014) Home page. https://www.dedipac.eu/ (accessed September 2014).Google Scholar
34. Lakerveld, J, van der Ploeg, H, Kroeze, W et al. (2014) Towards the integration and development of a cross-European research network and infrastructure: the DEterminants of DIet and Physical ACtivity (DEDIPAC) Knowledge Hub. Int J Behav Nutr Phys Act 11, 143.CrossRefGoogle Scholar
35. Harrington, JM, Riordan, F & Ryan, K (2014) What are the assessment methods used to determine dietary intake of sugar-sweetened beverages in adults (>18 years) and children in European countries, according to pan-European studies involving two or more European countries? (Protocol). http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42014012890 (accessed September 2016).18+years)+and+children+in+European+countries,+according+to+pan-European+studies+involving+two+or+more+European+countries?+(Protocol).+http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42014012890+(accessed+September+2016).>Google Scholar
36. Council of Europe (2014) 47 Member States. http://www.coe.int/en/web/portal/47-members-states (accessed September 2014).Google Scholar
37. Riordan, F, Ryan, K, Perry, IJ et al. (2016) A systematic review of methods to assess intake of fruits and vegetables among healthy European adults and children: a DEDIPAC (DEterminants of DIet and Physical Activity) study. Public Health Nutr (Epublication ahead of print version).Google Scholar
38. United Nations (2014) Composition of macro geographical (continental) regions, geographical sub-regions, and selected economic and other groupings. http://unstats.un.org/unsd/methods/m49/m49regin.htm (accessed March 2014).Google Scholar
39. Ahrens, W, Bammann, K, Siani, A et al. (2011) The IDEFICS cohort: design, characteristics and participation in the baseline survey. Int J Obes (Lond) 35, Suppl. 1, S3S15.Google Scholar
40. Bel-Serrat, S, Mouratidou, T, Pala, V et al. (2014) Relative validity of the Children’s Eating Habits Questionnaire–food frequency section among young European children: the IDEFICS Study. Public Health Nutr 17, 266276.Google Scholar
41. Currie, C, Griebler, R, Inchley, J et al. (2010) Health Behaviour in School-Aged Children (HBSC) Study Protocol: Background, Methodology and Mandatory Items for the 2009/10 Survey. Edinburgh/Vienna: CAHRU/LBIHPR.Google Scholar
42. de Groot, CPGM & van Staveren, WA (1988) Nutrition and the Elderly. A European Collaborative Study in Cooperation with the World Health Organization (WHO-SPRA) and the International Union of Nutritional Sciences (IUNS) Committee on Geriatric Nutrition, Manual of Operations. EURO-NUT Report no. 11. Wageningen: EURONUT.Google Scholar
43. de Groot, LC, Hautvast, JG & van Staveren, WA (1992) Nutrition and health of elderly people in Europe: the EURONUT-SENECA Study. Nutr Rev 50, 185194.Google Scholar
44. Moreno, LA, De Henauw, S, Gonzalez-Gross, M et al. (2008) Design and implementation of the Healthy Lifestyle in Europe by Nutrition in Adolescence Cross-Sectional Study. Int J Obes (Lond) 32, Suppl. 5, S4S11.Google Scholar
45. Moreno, LA, Gonzalez-Gross, M, Kersting, M et al. (2008) Assessing, understanding and modifying nutritional status, eating habits and physical activity in European adolescents: the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) Study. Public Health Nutr 11, 288299.Google Scholar
46. Olsen, NJ, Andersen, LB, Wedderkopp, N et al. (2012) Intake of liquid and solid sucrose in relation to changes in body fatness over 6 years among 8- to 10-year-old children: the European Youth Heart Study. Obes Facts 5, 506512.Google Scholar
47. Riboli, E, Hunt, KJ, Slimani, N et al. (2002) European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr 5, 11131124.CrossRefGoogle Scholar
48. Riddoch, C, Edwards, D, Page, A et al. (2005) The European Youth Heart Study – cardiovascular disease risk factors in children: rationale, aims, study design, and validation of methods. J Phys Act Health 2, 115129.Google Scholar
49. Slimani, N, Deharveng, G, Charrondiere, RU et al. (1999) Structure of the standardized computerized 24-h diet recall interview used as reference method in the 22 centers participating in the EPIC project. European Prospective Investigation into Cancer and Nutrition. Comput Methods Programs Biomed 58, 251266.Google Scholar
50. van Staveren, WA, de Groot, LC, Burema, J et al. (1995) Energy balance and health in SENECA participants. Survey in Europe on Nutrition and the Elderly, a Concerted Action. Proc Nutr Soc 54, 617629.CrossRefGoogle ScholarPubMed
51. van Stralen, MM, te Velde, SJ, Singh, AS et al. (2011) EuropeaN Energy balance Research to prevent excessive weight Gain among Youth (ENERGY) project: design and methodology of the ENERGY cross-sectional survey. BMC Public Health 11, 65.CrossRefGoogle ScholarPubMed
52. Vereecken, CA, Covents, M, Matthys, C et al. (2005) Young adolescents’ nutrition assessment on computer (YANA-C). Eur J Clin Nutr 59, 658667.CrossRefGoogle ScholarPubMed
53. Vereecken, CA, Covents, M, Sichert-Hellert, W et al. (2008) Development and evaluation of a self-administered computerized 24-h dietary recall method for adolescents in Europe. Int J Obes (Lond) 32, Suppl. 5, S26S34.Google Scholar
54. Vereecken, CA, De Bourdeaudhuij, I & Maes, L (2010) The HELENA online food frequency questionnaire: reproducibility and comparison with four 24-h recalls in Belgian-Flemish adolescents. Eur J Clin Nutr 64, 541548.Google Scholar
55. Maes, L, Cook, TL, Ottovaere, C et al. (2011) Pilot evaluation of the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) Food-O-Meter, a computer-tailored nutrition advice for adolescents: a study in six European cities. Public Health Nutr 14, 12921302.Google Scholar
56. El Ansari, W, Stock, C & Mikolajczyk, RT (2012) Relationships between food consumption and living arrangements among university students in four European countries – a cross-sectional study. Nutr J 11, 28.Google Scholar
57. Mikolajczyk, RT, El Ansari, W & Maxwell, AE (2009) Food consumption frequency and perceived stress and depressive symptoms among students in three European countries. Nutr J 8, 31.CrossRefGoogle ScholarPubMed
58. Boylan, S, Welch, A, Pikhart, H et al. (2009) Dietary habits in three Central and Eastern European countries: the HAPIEE study. BMC Public Health 9, 439.Google Scholar
59. Peasey, A, Bobak, M, Kubinova, R et al. (2006) Determinants of cardiovascular disease and other non-communicable diseases in Central and Eastern Europe: rationale and design of the HAPIEE study. BMC Public Health 6, 255.CrossRefGoogle ScholarPubMed
60. Prattala, R, Paalanen, L, Grinberga, D et al. (2007) Gender differences in the consumption of meat, fruit and vegetables are similar in Finland and the Baltic countries. Eur J Public Health 17, 520525.CrossRefGoogle ScholarPubMed
61. Tyrovolas, S, Psaltopoulou, T, Pounis, G et al. (2011) Nutrient intake in relation to central and overall obesity status among elderly people living in the Mediterranean islands: the MEDIS study. Nutr Metab Cardiovasc Dis 21, 438445.Google Scholar
62. Karamanos, B, Thanopoulou, A, Angelico, F et al. (2002) Nutritional habits in the Mediterranean Basin. The macronutrient composition of diet and its relation with the tradiational Mediterranean diet. Multi-centre study of the Mediterranean Group for the study of diabetes (MGSD). Eur J Clin Nutr 56, 983991.Google Scholar
63. Weiland, SK, Bjorksten, B, Brunekreef, B et al. (2004) Phase II of the International Study of Asthma and Allergies in Childhood (ISAAC II): rationale and methods. Eur Respir J 24, 406412.Google Scholar
64. Nagel, G, Weinmayr, G, Kleiner, A et al. (2010) Effect of diet on asthma and allergic sensitisation in the international study on allergies and asthma in childhood (ISAAC) phase two. Thorax 65, 516522.Google Scholar
65. Paalanen, L, Prattala, R, Palosuo, H et al. (2011) Socio-economic differences in the consumption of vegetables, fruit and berries in Russian and Finnish Karelia: 1992–2007. Eur J Public Health 21, 3542.Google Scholar
66. I.Family (2014) Home page. http://www.ifamilystudy.eu/ (accessed September 2014).Google Scholar
67. Zaborskis, A, Moceviciene, R & Iannotti, RJ (2014) The influence of chronological period of data collection on differences in reported dietary intake among school-aged children surveyed in 39 countries. J Nutr Educ Behav 46, 359369.Google Scholar
68. Duffey, KJ, Huybrechts, I, Mouratidou, T et al. (2012) Beverage consumption among European adolescents in the HELENA study. Eur J Clin Nutr 66, 244252.Google Scholar
69. Determinants of eating behaviour in European children, adolescents and their parents (I.Family) (2012–2017) Diet Questionnaire. EC FP7 Grant Agreement no. 266044.Google Scholar
70. Determinants of eating behaviour in European children, adolescents and their parents (I.Family) (2012–2017) SACANA. EC FP7 Grant Agreement no. 266044.Google Scholar
71. Larsson, CL, Klock, KS, Astrom, AN et al. (2001) Food habits of young Swedish and Norwegian vegetarians and omnivores. Public Health Nutr 4, 10051014.Google Scholar
72. Szczepanska, E, Deka, M & Calyniuk, B (2013) Studies to determine nutrition behaviour amongst middle school pupils living in the border areas of Poland and the Czech Republic. Rocz Panstw Zakl Hig 64, 191196.Google Scholar
73. Luszczynska, A, de Wit, JB, de Vet, E et al. (2013) At-home environment, out-of-home environment, snacks and sweetened beverages intake in preadolescence, early and mid-adolescence: the interplay between environment and self-regulation. J Youth Adolesc 42, 18731883.CrossRefGoogle ScholarPubMed
74. de Vet, E, de Wit, JB, Luszczynska, A et al. (2013) Access to excess: how do adolescents deal with unhealthy foods in their environment? Eur J Public Health 23, 752756.CrossRefGoogle ScholarPubMed
75. Stok, FM, de Vet, E, de Wit, JB et al. (2015) The proof is in the eating: subjective peer norms are associated with adolescents’ eating behaviour. Public Health Nutr 18, 10441051.CrossRefGoogle ScholarPubMed
76. Cinar, B & Murtomaa, H (2008) Clustering of obesity and dental health with lifestyle factors among Turkish and Finnish pre-adolescents. Obes Facts 1, 196202.Google Scholar
77. Brug, J, van Stralen, MM, Te Velde, SJ et al. (2012) Differences in weight status and energy-balance related behaviors among schoolchildren across Europe: the ENERGY-project. PLoS One 7, e34742.Google Scholar
78. Zheng, M, Rangan, A, Olsen, NJ et al. (2014) Sugar-sweetened beverages consumption in relation to changes in body fatness over 6 and 12 years among 9-year-old children: the European Youth Heart Study. Eur J Clin Nutr 68, 7783.Google Scholar
79. Olafsdottir, S, Berg, C, Eiben, G et al. (2014) Young children’s screen activities, sweet drink consumption and anthropometry: results from a prospective European study. Eur J Clin Nutr 68, 223228.Google Scholar
80. Manios, Y, Androutsos, O, Katsarou, C et al. (2014) Designing and implementing a kindergarten-based, family-involved intervention to prevent obesity in early childhood: the ToyBox-study. Obes Rev 15, Suppl. 3, 513.Google Scholar
81. Manios, Y & ToyBox-study, G (2014) Methodological procedures followed in a kindergarten-based, family-involved intervention implemented in six European countries to prevent obesity in early childhood: the ToyBox-study. Obes Rev 15, Suppl. 3, 14.Google Scholar
82. Mouratidou, T, Miguel, ML, Androutsos, O et al. (2014) Tools, harmonization and standardization procedures of the impact and outcome evaluation indices obtained during a kindergarten-based, family-involved intervention to prevent obesity in early childhood: the ToyBox-study. Obes Rev 15, Suppl. 3, 5360.CrossRefGoogle ScholarPubMed
83. Gonzalez-Gil, EM, Mouratidou, T, Cardon, G et al. (2014) Reliability of primary caregivers reports on lifestyle behaviours of European pre-school children: the ToyBox-study. Obes Rev 15, Suppl. 3, 6166.CrossRefGoogle ScholarPubMed
84. De Craemer, M, De Decker, E, De Bourdeaudhuij, I et al. (2014) Applying the intervention mapping protocol to develop a kindergarten-based, family-involved intervention to increase European preschool children’s physical activity levels: the ToyBox-study. Obes Rev 15, Suppl. 3, 1426.Google Scholar
85. Duvinage, K, Ibrügger, S, Kreichauf, S et al. (2014) Developing the intervention material to increase physical activity levels of European preschool children: the ToyBox-study. Obes Rev 15, Suppl. 3, 2739.Google Scholar
86. Payr, A, Birnbaum, J, Wildgruber, A et al. (2014) Concepts and strategies on how to train and motivate teachers to implement a kindergarten-based, family-involved intervention to prevent obesity in early childhood. The ToyBox-study. Obes Rev 15, Suppl. 3, 4047.Google Scholar
87. Androutsos, O, Katsarou, C, Payr, A et al. (2014) Designing and implementing teachers’ training sessions in a kindergarten-based, family-involved intervention to prevent obesity in early childhood. The ToyBox-study. Obes Rev 15, Suppl. 3, 4852.CrossRefGoogle Scholar
88. De Miguel-Etayo, P, Mesana, MI, Cardon, G et al. (2014) Reliability of anthropometric measurements in European preschool children: the ToyBox-study. Obes Rev 15, Suppl. 3, 6773.Google Scholar
89. Androutsos, O, Apostolidou, E, Iotova, V et al. (2014) Process evaluation design and tools used in a kindergarten-based, family-involved intervention to prevent obesity in early childhood. The ToyBox-study. Obes Rev 15, Suppl. 3, 7480.Google Scholar
90. Pil, L, Putman, K, Cardon, G et al. (2014) Establishing a method to estimate the cost-effectiveness of a kindergarten-based, family-involved intervention to prevent obesity in early childhood. The ToyBox-study. Obes Rev 15, Suppl. 3, 8189.Google Scholar
91. Crispim, SP, Geelen, A, Souverein, OW et al. (2011) Biomarker-based evaluation of two 24-h recalls for comparing usual fish, fruit and vegetable intakes across European centers in the EFCOVAL Study. Eur J Clin Nutr 65, Suppl. 1, S38S47.Google Scholar
92. Margetts, BM & Pietinen, P (1997) European Prospective Investigation into Cancer and Nutrition: validity studies on dietary assessment methods. Int J Epidemiol 26, Suppl. 1, S1S5.Google Scholar
93. Brunner, E, Stallone, D, Juneja, M et al. (2001) Dietary assessment in Whitehall II: comparison of 7 d diet diary and food-frequency questionnaire and validity against biomarkers. Br J Nutr 86, 405414.Google Scholar
94. Willett, WC, Sampson, L, Stampfer, MJ et al. (1985) Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 122, 5165.Google Scholar
95. Tyrovolas, S, Pounis, G, Bountziouka, V et al. (2010) Repeatability and validation of a short, semi-quantitative food frequency questionnaire designed for older adults living in Mediterranean areas: the MEDIS-FFQ. J Nutr Elder 29, 311324.Google Scholar
96. Nes, M, van Staveren, WA, Zajkas, G et al. (1991) Validity of the dietary history method in elderly subjects. Euronut SENECA investigators. Eur J Clin Nutr 45, Suppl. 3, 97104.Google Scholar
97. Vereecken, CA & Maes, L (2003) A Belgian study on the reliability and relative validity of the Health Behaviour in School-Aged Children food-frequency questionnaire. Public Health Nutr 6, 581588.Google Scholar
98. Singh, AS, Vik, FN, Chinapaw, MJM et al. (2011) Test–retest reliability and construct validity of the ENERGY-child questionnaire on energy balance-related behaviours and their potential determinants: the ENERGY-project. Int J Behav Nutr Phys Act 8, 136.Google Scholar
99. Lytle, LA, Nichaman, MZ, Obarzanek, E et al. (1993) Validation of 24-hour recalls assisted by food records in third-grade children. The CATCH Collaborative Group. J Am Diet Assoc 93, 14311436.Google Scholar
100. Huybrechts, I, De Backer, G, De Bacquer, D et al. (2009) Relative validity and reproducibility of a food-frequency questionnaire for estimating food intakes among Flemish preschoolers. Int J Environ Res Public Health 6, 382399.Google Scholar
101. Huybrechts, I, Bornhorst, C, Pala, V et al. (2011) Evaluation of the Children’s Eating Habits Questionnaire used in the IDEFICS study by relating urinary calcium and potassium to milk consumption frequencies among European children. Int J Obes (Lond) 35, Suppl. 1, S69S78.Google Scholar
102. Haveman-Nies, A, De Groot, LCPGM & Van Staveren, WA (1998) Snack patterns of older Europeans. J Am Diet Assoc 98, 12971302.Google Scholar
103. Celis-Morales, C, Livingstone, KM, Marsaux, CF et al. (2015) Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries. Genes Nutr 10, 450.Google Scholar
104. Fallaize, R, Forster, H, Macready, AL et al. (2014) Online dietary intake estimation: reproducibility and validity of the Food4Me food frequency questionnaire against a 4-day weighed food record. J Med Internet Res 16, e190.Google Scholar
105. Forster, H, Fallaize, R, Gallagher, C et al. (2014) Online dietary intake estimation: the Food4Me food frequency questionnaire. J Med Internet Res 16, e150.Google Scholar
106. Tyrovolas, S, Psaltopoulou, T, Pounis, G et al. (2011) Nutrient intake in relation to central and overall obesity status among elderly people living in the Mediterranean islands: The MEDIS study. Nutr Metab Cardiovas Dis 21, 438445.Google Scholar
107. Kolarzyk, E, Shpakou, A, Kleszczewska, E et al. (2012) Nutritional status and food choices among frst year medical students. Cent Eur J Med 7, 396408.Google Scholar
108. Brug, J, Van Stralen, MM, Chinapaw, MJ et al. (2012) Differences in weight status and energy-balance related behaviours according to ethnic background among adolescents in seven countries in Europe: the ENERGY-project. Pediatr Obes 7, 399411.Google Scholar
109. Wirfalt, E, McTaggart, A, Pala, V et al. (2002) Food sources of carbohydrates in a European cohort of adults. Public Health Nutr 5, 11971215.Google Scholar
110. West, NX, Sanz, M, Lussi, A et al. (2013) Prevalence of dentine hypersensitivity and study of associated factors: a European population-based cross-sectional study. J Dent 41, 841851.CrossRefGoogle ScholarPubMed
111. Prattala, R, Paalanen, L, Grinberga, D et al. (2007) Gender differences in the consumption of meat, fruit and vegetables are similar in Finland and the Baltic countries. Eur J Public Health 17, 520525.Google Scholar
112. Lanfer, A, Hebestreit, A, Ahrens, W et al. (2011) Reproducibility of food consumption frequencies derived from the Children’s Eating Habits Questionnaire used in the IDEFICS study. Int J Obes (Lond) 35, Suppl. 1, S61S68.Google Scholar
113. Bel-Serrat, S, Fernandez Alvira, JM, Pala, V et al.2011) Relative validation of two dietary assessment methods: SACINA (24-h recall) and food frequency questionnaire. In 1st IDEFICS Symposium and Workshop Child Health in Europe – The IDEFICS Study: Towards a Better Understanding of Obesity; 2010, Nov 8–9; Zaragoza, Spain. Int J Obes (Lond) 35, Suppl. 1, S152 (abstract).Google Scholar
114. Bornhorst, C, Bel-Serrat, S, Pigeot, I et al. (2014) Validity of 24-h recalls in (pre-)school aged children: comparison of proxy-reported energy intakes with measured energy expenditure. Clin Nutr 33, 7984.CrossRefGoogle ScholarPubMed
115. Boylan, S, Lallukka, T, Lahelma, E et al. (2011) Socio-economic circumstances and food habits in Eastern, Central and Western European populations. Public Health Nutr 14, 678687.Google Scholar
116. Bel-Serrat, S, Mouratidou, T, Bornhorst, C et al. (2013) Food consumption and cardiovascular risk factors in European children: the IDEFICS study. Pediatr Obes 8, 225236.Google Scholar
117. Pala, V, Lissner, L, Hebestreit, A et al. (2013) Dietary patterns and longitudinal change in body mass in European children: a follow-up study on the IDEFICS multicenter cohort. Eur J Clin Nutr 67, 10421049.Google Scholar
118. Slimani, N, Kaaks, R, Ferrari, P et al. (2002) European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study: rationale, design and population characteristics. Public Health Nutr 5, 11251145.Google Scholar
119. Patterson, E, Warnberg, J, Kearney, J et al. (2009) The tracking of dietary intakes of children and adolescents in Sweden over six years: the European Youth Heart Study. Int J Behav Nutr Phys Act 6, 91.Google Scholar
120. Vandevijvere, S, Geelen, A, Gonzalez-Gross, M et al. (2013) Evaluation of food and nutrient intake assessment using concentration biomarkers in European adolescents from the Healthy Lifestyle in Europe by Nutrition in Adolescence study. Br J Nutr 109, 736747.Google Scholar
121. Bel-Serrat, S, Mouratidou, T, Santaliestra-Pasias, AM et al. (2013) Clustering of multiple lifestyle behaviours and its association to cardiovascular risk factors in children: the IDEFICS study. Eur J Clin Nutr 67, 848854.Google Scholar
122. Slimani, N, Fahey, M, Welch, AA et al. (2002) Diversity of dietary patterns observed in the European Prospective Investigation into Cancer and Nutrition (EPIC) project. Public Health Nutr 5, 13111328.Google Scholar
123. Schroll, K, Moreiras-Varela, O, Schlettwein-Gsell, D et al. (1997) Cross-cultural variations and changes in food-group intake among elderly women in Europe: results from the Survey in Europe on Nutrition and the Elderly a Concerted Action (SENECA). Am J Clin Nutr 65, 4 Suppl., S1282S1289.CrossRefGoogle ScholarPubMed
124. Cade, J, Thompson, R, Burley, V et al. (2002) Development, validation and utilisation of food-frequency questionnaires – a review. Public Health Nutr 5, 567587.Google Scholar
125. Thompson, FE & Subar, AF (2001) Dietary assessment methodology. In Nutrition in the Prevention and Treatment of Disease, pp. 330 [M Ferruzzi, AM Coulston and Carol Boushy, editors]. San Diego, CA: Academic Press.Google Scholar
126. Burrows, T, Martin, R & Collins, C (2010) A systematic review of the validity of dietary assessment methods in children when compared with method of doubly labeled water. J Am Diet Assoc 110, 15011510.Google Scholar
127. Sjoberg, A, Hallberg, L, Hoglund, D et al. (2003) Meal pattern, food choice, nutrient intake and lifestyle factors in the Goteborg Adolescence Study. Eur J Clin Nutr 57, 15691578.Google Scholar
128. Malik, VS, Popkin, BM, Bray, GA et al. (2010) Sugar sweetened beverages, obesity, type 2 diabetes and cardiovascular disease risk. Circulation 121, 13561364.Google Scholar
Figure 0

Fig. 1 Flow diagram showing study selection process for the current review (SSB, sugar-sweetened beverages; F&V, fruits and vegetables)

Figure 1

Table 1 Summary of all studies identified to assess sugar-sweetened beverages (SSB): design, population studied, dietary assessment instruments used and details of testing for validity and/or reproducibility. Studies were selected to be potentially suitable to assess SSB intake based on (i) the instrument was tested for validity and/or reproducibility and (ii) the instrument was used in more than two countries simultaneously which represent a range of European regions; and are indicated by ticks in the last column. Where validation or reliability data was not available for SSB specifically, this is highlighted in bold font

Figure 2

Table 2 Summary of the selected instruments which were tested for validity (n 7) for assessment of sugar-sweetened beverages (SSB): design, age group, countries, mode of administration, definition of SSB used and portion estimation

Figure 3

Table 3 Summary of the FFQ (n 24) identified for the assessment of sugar-sweetened beverages (SSB): number of items, instrument purpose, population, definition of SSB, reference period, mode, frequency categories and portion estimation

Figure 4

Table 4 Summary of the 24 h recalls (24-HDR; n 6) identified for the assessment of sugar-sweetened beverages (SSB): population, instrument purpose, mode, structure, prompts and portion estimation

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