Baker P, Machado P, Santos T, Sievert K, Backholer K, Hadjikakou M, et al. Ultra-processed foods and the nutrition transition: global, regional and national trends, food systems transformations and political economy drivers. Obes Rev. 2020;21(12) [cited 2021 May 11]. Available from: https://pubmed.ncbi.nlm.nih.gov/32761763/.
Baraldi LG, Martinez Steele E, Canella DS, Monteiro CA. Consumption of ultra-processed foods and associated sociodemographic factors in the USA between 2007 and 2012: evidence from a nationally representative cross-sectional study. BMJ Open. 2018;8(e020574):1–9 [cited 2020 Aug 3]. Available from: http://bmjopen.bmj.com/.
Google Scholar
Cediel G, Reyes M, Da Costa Louzada ML, Martinez Steele E, Monteiro CA, Corvalán C, et al. Ultra-processed foods and added sugars in the Chilean diet (2010). Public Health Nutr. 2018;21(1):125–33 [cited 2020 Aug 3]. Available from: https://pubmed.ncbi.nlm.nih.gov/28625223/.
Article
PubMed
Google Scholar
da Louzada MLC, Martins APB, Canella DS, Baraldi LG, Levy RB, Claro RM, et al. Ultra-processed foods and the nutritional dietary profile in Brazil. Rev Saude Publica. 2015;49(38):1–11 [cited 2020 Aug 3]. Available from: www.scielo.br/rsp.
Article
Google Scholar
Julia C, Martinez L, Allès B, Touvier M, Hercberg S, Méjean C, et al. Contribution of ultra-processed foods in the diet of adults from the French NutriNet-Santé study. Public Health Nutr. 2018;21(1):27–37 [cited 2020 Aug 3]. Available from: https://www.revistas.usp.br/rsp/article/view/189620. https://doi.org/10.11606/s1518-8787.2021055002473.
Moubarac J-CC, Batal M, Martins APB, Claro R, Levy RB, Cannon G, et al. Processed and ultra-processed food products: consumption trends in Canada from 1938 to 2011. Can J Diet Pract Res. 2014;75(1):15–21 [cited 2020 Aug 3]. Available from: https://dcjournal.ca/doi/10.3148/75.1.2014.15.
Article
PubMed
Google Scholar
Slimani N, Deharveng G, Southgate DAT, Biessy C, Chajès V, van Bakel MME, et al. Contribution of highly industrially processed foods to the nutrient intakes and patterns of middle-aged populations in the European Prospective Investigation into Cancer and Nutrition study. Eur J Clin Nutr. 2009;63(4):S206–25 [cited 2020 Jun 22]. Available from: https://www.nature.com/articles/ejcn200982.
Article
CAS
PubMed
Google Scholar
Latasa P, Louzada MLDC, Martinez Steele E, Monteiro CA. Added sugars and ultra-processed foods in Spanish households (1990–2010). Eur J Clin Nutr. 2018;72(10):1404–12 [cited 2020 Aug 3]. Available from: https://www.nature.com/articles/s41430-017-0039-0.
Article
CAS
PubMed
Google Scholar
Blanco-Rojo R, Sandoval-Insausti H, López-Garcia E, Graciani A, Ordovás JM, Banegas JR, et al. Consumption of ultra-processed foods and mortality: a national prospective cohort in Spain. Mayo Clin Proc. 2019;94(11):2178–88 [cited 2020 Apr 14]. Available from: https://doi.org/10.1016/j.mayocp.2019.03.035.
Article
PubMed
Google Scholar
Juul F, Hemmingsson E. Trends in consumption of ultra-processed foods and obesity in Sweden between 1960 and 2010. Public Health Nutr. 2015;18(17):3096–107 [cited 2020 Aug 12]. Available from: https://pubmed.ncbi.nlm.nih.gov/25804833/.
Article
PubMed
Google Scholar
Gibney MJ. Ultra-processed foods: definitions and policy issues. Curr Dev Nutr. 2018;3(2) [cited 2021 May 11]. Available from: https://academic.oup.com/cdn/.
Monteiro CA, Cannon G, Moubarac J-C, Bertazzi Levy R, Laura Louzada MC, Constante JP. The UN decade of nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 2018;21(1):5–17.
Article
PubMed
Google Scholar
Monteiro CA, Cannon G, Lawrence M, da Louzada MLC, Pereira Machado P. Ultra-processed foods, diet quality, and health using the NOVA classification system. FAO. 2019. Food and Agriculture Organization of the United Nations (FAO) Headquarters: Viale delle Terme di Caracalla 00153 Rome, Italy. https://www.fao.org/fsnforum/resources/fsn-resources/ultra-processed-foods-diet-quality-and-health-using-nova-classification.
Chen X, Zhang Z, Yang H, Qiu P, Wang H, Wang F, et al. Consumption of ultra-processed foods and health outcomes: a systematic review of epidemiological studies. Nutr J. 2020;19(86):1–10 [cited 2021 Mar 1]. Available from: https://doi.org/10.1186/s12937-020-00604-1.
CAS
Google Scholar
Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Méjean C, Andrianasolo RM, et al. Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé). BMJ. 2019;365(ll1451):1–13.
Google Scholar
Smaira FI, Mazzolani BC, Peçanha T, dos Santos KM, Rezende DAN, Araujo ME, et al. Ultra-processed food consumption associates with higher cardiovascular risk in rheumatoid arthritis. Clin Rheumatol. 2020;39(5):1423–8 [cited 2020 Aug 12]. Available from: https://pubmed.ncbi.nlm.nih.gov/31902026/.
Article
PubMed
Google Scholar
Schnabel L, Kesse-Guyot E, Allès B, Touvier M, Srour B, Hercberg S, et al. Association between ultraprocessed food consumption and risk of mortality among middle-aged adults in France. JAMA Intern Med. 2019;179(4):490–8.
Article
PubMed
PubMed Central
Google Scholar
da Louzada MLC, Baraldi LG, Steele EM, Martins APB, Canella DS, Moubarac JC, et al. Consumption of ultra-processed foods and obesity in Brazilian adolescents and adults. Prev Med (Baltim). 2015;81:9–15 Available from: https://doi.org/10.1016/j.ypmed.2015.07.018.
Article
Google Scholar
Popkin BM, Barquera S, Corvalan C, Hofman KJ, Monteiro C, Ng SW, et al. Towards unified and impactful policies to reduce ultra-processed food consumption and promote healthier eating. Lancet Diabetes Endocrinol. 2021; [cited 2021 Apr 19]. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2213858721000784.
Martinez-Perez C, San-Cristobal R, Guallar-Castillon P, Martínez-González MÁ, Salas-Salvadó J, Corella D, et al. Use of different food classification systems to assess the association between ultra-processed food consumption and cardiometabolic health in an elderly population with metabolic syndrome (PREDIMED-plus cohort). Nutrients. 2021;13(7):2471 [cited 2021 Jul 26]. Available from: https://www.mdpi.com/2072-6643/13/7/2471/htm.
Article
CAS
PubMed
PubMed Central
Google Scholar
Crino M, Barakat T, Trevena H, Neal B. Systematic review and comparison of classification frameworks describing the degree of food processing. Nutr Food Technol. 2017;3(1):1–12. [cited 2020 Aug 3]. Available from: https://doi.org/10.16966/2470-6086.138.
Juul F, Vaidean G, Lin Y, Deierlein AL, Parekh N. Ultra-processed foods and incident cardiovascular disease in the Framingham offspring study. J Am Coll Cardiol. 2021;77(12):1520–31 [cited 2021 Apr 6]. Available from: https://pubmed.ncbi.nlm.nih.gov/33766258/.
Article
PubMed
Google Scholar
Canella DS, Levy RB, Martins APB, Claro RM, Moubarac J-CC, Baraldi LG, et al. Ultra-processed food products and obesity in Brazilian households (2008–2009). Votruba SB, editor. PLoS One. 2014;9(3):e92752 [cited 2020 Aug 3]. Available from: https://pubmed.ncbi.nlm.nih.gov/24667658/.
Article
PubMed
PubMed Central
Google Scholar
de Mendonça RD, Lopes ACS, Pimenta AM, Gea A, Martinez-Gonzalez MA, Bes-Rastrollo M. Ultra-processed food consumption and the incidence of hypertension in a Mediterranean cohort: the Seguimiento Universidad de Navarra project. Am J Hypertens. 2017;30(4):358–66 [cited 2020 Aug 3]. Available from: https://academic.oup.com/ajh/article-abstract/30/4/358/2645510.
PubMed
Google Scholar
de Mendonça RD, Pimenta AM, Gea A, de la Fuente-Arrillaga C, Martinez-Gonzalez MA, Lopes ACS, et al. Ultraprocessed food consumption and risk of overweight and obesity: the University of Navarra Follow-Up (SUN) cohort study. Am J Clin Nutr. 2016;104(5):1433–40 [cited 2020 Aug 3]. Available from: https://academic.oup.com/ajcn/article/104/5/1433/4564389.
Article
PubMed
Google Scholar
Fiolet T, Srour B, Sellem L, Kesse-Guyot E, Allès B, Méjean C, et al. Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ. 2018;360:322. [cited 2020 Aug 3]. Available from: https://doi.org/10.1136/bmj.k322.
Schnabel L, Buscail C, Sabate JM, Bouchoucha M, Kesse-Guyot E, Allès B, et al. Association between ultra-processed food consumption and functional gastrointestinal disorders: results from the French NutriNet-Santé Cohort. Am J Gastroenterol. 2018;113(8):1217–28 [cited 2020 Aug 12]. Available from: https://pubmed.ncbi.nlm.nih.gov/29904158/.
Article
PubMed
Google Scholar
Fardet A, Rock E, Bassama J, Bohuon P, Prabhasankar P, Monteiro C, et al. Current food classifications in epidemiological studies do not enable solid nutritional recommendations for preventing diet-related chronic diseases: the impact of food processing. Adv Nutr. 2015;6(6):629–38.
Article
CAS
PubMed
PubMed Central
Google Scholar
Tavares LF, Fonseca SC, Rosa MLG, Yokoo EM. Relationship between ultra-processed foods and metabolic syndrome in adolescents from a Brazilian Family Doctor Program. Public Health Nutr. 2012;15(1):82–7 [cited 2021 Nov 2]. Available from: https://pubmed.ncbi.nlm.nih.gov/21752314/.
Article
PubMed
Google Scholar
Machado PP, Steele EM, Levy RB, Sui Z, Rangan A, Woods J, et al. Ultra-processed foods and recommended intake levels of nutrients linked to non-communicable diseases in Australia: evidence from a nationally representative cross-sectional study. BMJ Open. 2019;9(e029544) [cited 2021 May 11]. Available from: http://bmjopen.bmj.com/.
Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome. Circulation. 2009;120(16):1640–5.
Article
CAS
Google Scholar
Martínez-González MA, Buil-Cosiales P, Corella D, Bulló M, Fitó M, Vioque J, et al. Cohort profile: design and methods of the PREDIMED-Plus randomized trial. Int J Epidemiol. 2019;48(2):387–8 [cited 2020 Jul 28]. Available from: https://academic.oup.com/ije/article-abstract/48/2/387/5202210.
Article
PubMed
Google Scholar
Lachat C, Hawwash D, Ocké MC, Berg C, Forsum E, Hörnell A, et al. Strengthening the reporting of observational studies in epidemiology—nutritional epidemiology (STROBE-nut): an extension of the STROBE statement. PLoS Med. 2016;13(6):e1002036 [cited 2021 May 31]. Available from: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002036.
Article
PubMed
PubMed Central
Google Scholar
Molina L, Sarmiento M, Peñafiel J, Donaire D, Garcia-Aymerich J, Gomez M, et al. Validation of the Regicor short physical activity questionnaire for the adult population. PLoS One. 2017;12(1):168148 [cited 2021 Jun 14]. Available from: www.isciii.es.
Article
Google Scholar
Topolski TD, LoGerfo J, Patrick DL, Williams B, Walwick J, Patrick MB. The rapid assessment of physical activity (RAPA) among older adults. Prev Chronic Dis. 2006;3(4):1–8.
Google Scholar
Martínez-González MA, López-Fontana C, Varo JJ, Sánchez-Villegas A, Martinez JA. Validation of the Spanish version of the physical activity questionnaire used in the nurses’ health study and the health professionals’ follow-up study. Public Health Nutr. 2005;8(7):920–7 [cited 2021 Mar 22]. Available from: https://pubmed.ncbi.nlm.nih.gov/16277809/.
Article
PubMed
Google Scholar
Wallston KA, Wallston BS, DeVellis R. Development of the multidimensional health locus of control (MHLC) scales. Health Educ Monogr. 1978;6:160–70 Available from: https://nursing.vanderbilt.edu/projects/wallstonk/pdf/A16.pdf.
Article
CAS
PubMed
Google Scholar
Martin-Moreno JM, Boyle P, Gorgojo L, Maisonneuve P, Fernandez-rodriguez JC, Salvini S, et al. Development and validation of a food frequency questionnaire in Spain. Int J Epidemiol. 1993;22(3):512–9 [cited 2020 Aug 19]. Available from: https://academic.oup.com/ije/article/22/3/512/674681.
Article
CAS
PubMed
Google Scholar
Fernández-Ballart JD, Lluís Piñol J, Zazpe I, Corella D, Carrasco P, Toledo E, et al. Relative validity of a semi-quantitative food-frequency questionnaire in an elderly Mediterranean population of Spain. Br J Nutr. 2010;103:1808–16 [cited 2020 Aug 19]. Available from: https://doi.org/10.1017/S0007114509993837.
Article
PubMed
Google Scholar
De La Fuente-Arrillaga C, Vá Zquez Ruiz Z, Bes-Rastrollo M, Sampson L, Martinez-González MA. Reproducibility of an FFQ validated in Spain. Public Health Nutr. 2009;13(9):1364–72.
Article
Google Scholar
Galilea-Zabalza I, Buil-Cosiales P, Salas-Salvadó J, Toledo E, Ortega-Azorín C, Díez-Espino J, et al. Mediterranean diet and quality of life: baseline cross-sectional analysis of the PREDIMED-PLUS trial. PLoS One. 2018;13(6):1–18 [cited 2020 Aug 7]. Available from: https://doi.org/10.1371/journal.pone.0198974.
Article
Google Scholar
Schröder H, Zomeño MD, Martínez-González MA, Salas-Salvadó J, Corella D, Vioque J, et al. Validity of the energy-restricted Mediterranean Diet Adherence Screener. Clin Nutr. 2021; [cited 2021 Jul 13]. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0261561421003265.
Monteiro CA, Levy RB, Claro RM, De Castro IRR, Cannon G. Increasing consumption of ultra-processed foods and likely impact on human health: evidence from Brazil. Public Health Nutr. 2011;14(1):5–13.
Article
PubMed
Google Scholar
Monteiro CA, Cannon G, Levy R, Moubarac J-C, Jaime P, Martins AP, et al. NOVA. The star shines bright (Food classification. Public health). World Nutr. 2016;7(1–3):28–38 Available from: https://worldnutritionjournal.org/index.php/wn/article/view/5.
Google Scholar
Chajès V, Biessy C, Byrnes G, Deharveng G, Saadatian-Elahi M, Jenab M, et al. Ecological-Level associations between highly processed food intakes and plasma phospholipid elaidic acid concentrations: results from a cross-sectional study within the European prospective investigation into Cancer and nutrition (EPIC). Nutr Cancer. 2011;63(8):1235–50 [cited 2020 Jul 27]. Available from: https://www.tandfonline.com/doi/abs/10.1080/01635581.2011.617530.
Article
PubMed
Google Scholar
Eicher-Miller HA, Fulgoni Iii VL, Keast DR. Processed food contributions to energy and nutrient intake differ among US children by race/ethnicity. Nutrients. 2015;7(12):10076–88 [cited 2020 Jun 23]. Available from: www.mdpi.com/journal/nutrients.
Article
CAS
PubMed
PubMed Central
Google Scholar
Eicher-Miller HA, Fulgoni VL, Keast DR. Contributions of processed foods to dietary intake in the US from 2003–2008: a report of the food and nutrition science solutions joint task force of the Academy of Nutrition and Dietetics, American Society for Nutrition, Institute of Food Technologists. J Nutr. 2012;142(11):2065S–72S [cited 2020 Jun 23]. Available from: /pmc/articles/PMC3593301/?report=abstract.
Article
CAS
PubMed
PubMed Central
Google Scholar
Poti JM, Mendez MA, Ng SW, Popkin BM. Is the degree of food processing and convenience linked with the nutritional quality of foods purchased by US households? Am J Clin Nutr. 2015;101(6):1251–62 [cited 2020 Jun 23]. Available from: https://pubmed.ncbi.nlm.nih.gov/25948666/.
Article
CAS
PubMed
PubMed Central
Google Scholar
R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2020. Available from: https://www.r-project.org/
Google Scholar
RStudio Team. RStudio: integrated development environment for R. Boston: RStudio, Inc; 2018. Available from: http://www.rstudio.com/
Google Scholar
Signorell A et mult. all. DescTools: tools for descriptive statistics. R package version 0.99.37. 2020. Available from: https://cran.r-project.org/package=DescTools.
Google Scholar
Revelle W. psych: procedures for personality and psychological research. R package version 2.0.7. Evanston: Northwestern University; 2020. Available from: https://cran.r-project.org/package=psych
Google Scholar
Yoshida K, Bartel A. tableone: create “Table 1” to describe baseline characteristics with or without propensity score weights. R package version 0.12.0. 2020. Available from: https://cran.r-project.org/package=tableone.
Google Scholar
Thiele C. cutpointr: determine and evaluate optimal cutpoints in binary classification tasks. R package version 1.0.32. 2020. Available from: https://cran.r-project.org/package=cutpointr%0A.
Google Scholar
Wei T, Simko V. R package “corrplot”: visualization of a correlation matrix (version 0.84). 2017. Available from: https://github.com/taiyun/corrplot.
Google Scholar
Meyer D, Zeileis A, Hornik K. vcd: visualizing categorical data. R package version 1.4-8. 2020. Available from: https://www.jstatsoft.org/v17/i03/.
Google Scholar
Kassambara A. rstatix: pipe-friendly framework for basic statistical tests. R package version 0.7.0. 2021. https://CRAN.R-project.org/package=rstatix.
Google Scholar
Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32–5 [cited 2021 Mar 23]. Available from: https://pubmed.ncbi.nlm.nih.gov/15405679/.
Article
CAS
PubMed
Google Scholar
Kaiser HF. A second generation little jiffy. Psychometrika. 1970;35(4):401–15 [cited 2021 Feb 22]. Available from: https://link.springer.com/article/10.1007/BF02291817.
Article
Google Scholar
Bartlett M. A note on the multiplying factors for various chi square approximations. J R Stat Soc Ser B Methodol. 1654;16:296–8 [cited 2021 Feb 22]. Available from: https://www.scienceopen.com/document?vid=124c659a-1a2b-44cf-8a06-a502d6425a27.
Google Scholar
Kaiser HF. The application of electronic computers to factor analysis. Educ Psychol Meas. 1960;20(1):141–51 [cited 2021 Mar 17]. Available from: http://journals.sagepub.com/doi/10.1177/001316446002000116.
Article
Google Scholar
Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16(3):297–334 [cited 2021 Feb 22]. Available from: https://link.springer.com/article/10.1007/BF02310555.
Article
Google Scholar
Fangupo LJ, Haszard JJ, Leong C, Heath ALM, Fleming EA, Taylor RW. Relative validity and reproducibility of a food frequency questionnaire to assess energy intake from minimally processed and ultra-processed foods in young children. Nutrients. 2019;11(6):1–13.
Article
Google Scholar
Koslowsky M, Scheinberg Z, Bleich A, Mark M, Apter A, Danon Y, et al. The factor structure and criterion validity of the short form of the eating attitudes test. J Pers Assess. 1992;58(1):27–35 [cited 2021 May 14]. Available from: https://www.tandfonline.com/doi/abs/10.1207/s15327752jpa5801_3.
Article
CAS
PubMed
Google Scholar
Gibney MJ, Forde CG, Mullally D, Gibney ER. Ultra-processed foods in human health: a critical appraisal. Am J Clin Nutr. 2017;106(3):717–24 [cited 2020 Jun 25]. Available from: http://ajcn.nutrition.org/lookup/doi/10.3945/ajcn.117.160440.
CAS
PubMed
Google Scholar
Nieuwenhuijsen MJ. Design of exposure questionnaires for epidemiological studies. Occup Environ Med. 2005;62(4):272–80 [cited 2021 May 11]. Available from: http://oem.bmj.com/.
Article
CAS
PubMed
PubMed Central
Google Scholar
San-Cristobal R, Navas-Carretero S, Celis-Morales C, Brennan L, Walsh M, Lovegrove JA, et al. Analysis of dietary pattern impact on weight status for personalised nutrition through on-line advice: the food4Me Spanish cohort. Nutrients. 2015;7(11):9523–37.
Article
PubMed
PubMed Central
Google Scholar
San-Cristobal R, Navas-Carretero S, Celis-Morales C, Livingstone KM, Stewart-Knox B, Rankin A, et al. Capturing health and eating status through a nutritional perception screening questionnaire (NPSQ9) in a randomised internet-based personalised nutrition intervention: the Food4Me study. Int J Behav Nutr Phys Act. 2017;14(1):1–12.
Article
Google Scholar
Trichopoulou A, Costacou T, Bamia C, Trichopoulos D. Adherence to a Mediterranean diet and survival in a Greek population. N Engl J Med. 2003;348(26):2599–608 [cited 2021 May 10]. Available from: https://pubmed.ncbi.nlm.nih.gov/12826634/.
Article
PubMed
Google Scholar
Tertsunen H-M, Hantunen S, Tuomainen T-P, Virtanen JK. Adherence to a healthy Nordic diet and risk of type 2 diabetes among men: the Kuopio Ischaemic heart disease risk factor study. Eur J Nutr. 2021; [cited 2021 May 10];Online pub. Available from: https://link.springer.com/10.1007/s00394-021-02569-1.
Toh DWK, Xia X, Sutanto CN, Low JHM, Poh KK, Wang J-W, et al. Enhancing the cardiovascular protective effects of a healthy dietary pattern with wolfberry (Lycium barbarum): a randomized controlled trial. Am J Clin Nutr. 2021; [cited 2021 May 10];Online ahe. Available from: https://academic.oup.com/ajcn/advance-article/doi/10.1093/ajcn/nqab062/6272607.
Paterson EN, Neville CE, Wallace SM, Woodside JV, Kee F, Young IS, et al. Dietary patterns associated with renal impairment in the Northern Ireland cohort for the longitudinal study of ageing (NICOLA). Eur J Nutr. 2021; [cited 2021 May 10];Published. Available from: https://link.springer.com/10.1007/s00394-021-02579-z.
Guenther PM, Kirkpatrick SI, Reedy J, Krebs-Smith SM, Buckman DW, Dodd KW, et al. The healthy eating Index-2010 is a valid and reliable measure of diet quality according to the 2010 dietary guidelines for Americans 1-3. J Nutr Methodol Math Model J Nutr. 2014;144:399–407 [cited 2021 May 10]. Available from: http://jn.nutrition.org.
CAS
Google Scholar
Krebs-Smith SM, Pannucci TRE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, et al. Update of the healthy eating index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591–602 [cited 2021 May 14]. Available from: https://pubmed.ncbi.nlm.nih.gov/30146071/.
Article
PubMed
PubMed Central
Google Scholar
McCullough ML, Willett WC. Evaluating adherence to recommended diets in adults: the Alternate Healthy Eating Index. Public Health Nutr. 2006;9(1A):152–7 [cited 2021 May 14]. Available from: https://pubmed.ncbi.nlm.nih.gov/16512963/.
Article
PubMed
Google Scholar
Food Standards Scotland. Briefing paper on discretionary foods. 2018.
Google Scholar
National Health and Medical Research Council. Australian dietary guidelines. Canberra; 2013. [cited 2021 May 10]. Available from: http://www.nhmrc.gov.au
Grieger JA, Wycherley TP, Johnson BJ, Golley RK. Discrete strategies to reduce intake of discretionary food choices: a scoping review. Int J Behav Nutr Phys Act. 2016;13(1):1–22 [cited 2021 May 10]. Available from: https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-016-0380-z.
Article
Google Scholar
Livingstone KM, Celis-Morales C, Navas-Carretero S, San-Cristobal R, Forster H, Woolhead C, et al. Personalised nutrition advice reduces intake of discretionary foods and beverages: findings from the Food4Me randomised controlled trial. Int J Behav Nutr Phys Act. 2021;18(1):70 [cited 2021 Jun 8]. Available from: https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-021-01136-5.
Article
PubMed
PubMed Central
Google Scholar
Phulkerd S, Lawrence M, Vandevijvere S, Sacks G, Worsley A, Tangcharoensathien V. A review of methods and tools to assess the implementation of government policies to create healthy food environments for preventing obesity and diet-related non-communicable diseases. Implement Sci. 2016;11(15) [cited 2021 May 11]. Available from: https://pubmed.ncbi.nlm.nih.gov/26846789/.
Sarbagili-Shabat C, Zelber-Sagi S, Fliss Isakov N, Ron Y, Hirsch A, Maharshak N. Development and validation of processed foods questionnaire (PFQ) in adult inflammatory bowel diseases patients. Eur J Clin Nutr. 2020;74(12):1653–60 Available from: https://doi.org/10.1038/s41430-020-0632-5.
Article
PubMed
Google Scholar
Motta VW de L, Lima SCVC, Marchioni DML, Lyra C de O. Food frequency questionnaire for adults in the Brazilian northeast region: emphasis on the level of food processing. Rev Saude Publica 2021;55(51). [cited 2021 Aug 24]. Available from: https://www.revistas.usp.br/rsp/article/view/189620.