Ten-year Changes in Diet Quality Among Adolescents and Young Adults (Food Consumption Survey 2004 and 2014, Belgium)

Overview

Journal Eur J Nutr

Specialty Nutritional Sciences

Date 2021 Feb 11

PMID 33570658

Citations 1

Authors

Lucille Desbouys

Manon Rouche

Karin De Ridder

Camille Pedroni

Katia Castetbon

Affiliations

Soon will be listed here.

Abstract

Purpose: To estimate the 10-year change in the overall nutritional quality of adolescent and young adult's diet, as measured by the modified Nutrient Profiling System of the British Food Standards Agency individual Dietary Index (FSAm-NPS-DI) which funds the Nutri-Score development, and in different components of this score, overall and according to the individual characteristics.

Methods: Two 24-h dietary recalls were carried out in 15- to 39-year-old respondents included in the Belgian Food Consumption Surveys in 2004 (n = 1186) and 2014 (n = 952). The weighted mean individual FSAm-NPS-DI was computed from all foods and beverages consumed, converted into a scale from 0 to 100 (from the poorest to the most favorable diet), and compared between survey years. Subject characteristics associated with the score, along with the mean daily intake of food groups, energy, and nutrients were explored in multiple linear regressions stratified by survey year and age group.

Results: The weighted mean daily FSAm-NPS-DI significantly increased between 2004 and 2014 [2004: 55.3 (SEM: 0.2) vs. 2014: 57.4 (0.5), P < 0.001 in 15- to 18-year olds; 55.0 (0.6) vs. 58.1 (0.4), P < 0.001 in 19- to 25-year olds; 57.1 (0.4) vs. 58.5 (0.3), P < 0.01 in 26- to 39-year olds]. SFA intake decreased in all age groups, and sugar-sweetened beverage, sugar, sodium, and fiber intakes decreased among 15‒18-year olds. The nutritional quality changed unevenly according to sociocultural characteristics, levels of education and regions being the main sources of disparities.

Conclusion: The quality of diet improved overall between 2004 and 2014 among young people in Belgium, an uneven change that need to be confirmed in future surveys, following the implementation of the Nutri-Score.

Citing Articles

Nutrition Transition in Europe: East-West Dimensions in the Last 30 Years-A Narrative Review.

Dokova K, Pancheva R, Usheva N, Haralanova G, Nikolova S, Kostadinova T Front Nutr. 2022; 9:919112.

PMID: 35873435 PMC: 9301044. DOI: 10.3389/fnut.2022.919112.

References

Ambrosini G, Johns D, Northstone K, Emmett P, Jebb S . Free Sugars and Total Fat Are Important Characteristics of a Dietary Pattern Associated with Adiposity across Childhood and Adolescence. J Nutr. 2016; 146(4):778-784. PMC: 4807647. DOI: 10.3945/jn.115.224659. View

Huybrechts I, Lioret S, Mouratidou T, Gunter M, Manios Y, Kersting M . Using reduced rank regression methods to identify dietary patterns associated with obesity: a cross-country study among European and Australian adolescents. Br J Nutr. 2017; 117(2):295-305. DOI: 10.1017/S0007114516004669. View

Bel S, De Ridder K, Lebacq T, Ost C, Teppers E, Cuypers K . Habitual food consumption of the Belgian population in 2014-2015 and adherence to food-based dietary guidelines. Arch Public Health. 2019; 77:14. PMC: 6449930. DOI: 10.1186/s13690-019-0343-3. View

Julia C, Kesse-Guyot E, Touvier M, Mejean C, Fezeu L, Hercberg S . Application of the British Food Standards Agency nutrient profiling system in a French food composition database. Br J Nutr. 2014; 112(10):1699-705. DOI: 10.1017/S0007114514002761. View

Julia C, Mejean C, Touvier M, Peneau S, Lassale C, Ducrot P . Validation of the FSA nutrient profiling system dietary index in French adults-findings from SUVIMAX study. Eur J Nutr. 2015; 55(5):1901-10. DOI: 10.1007/s00394-015-1006-y. View

Deschasaux M, Julia C, Kesse-Guyot E, Lecuyer L, Adriouch S, Mejean C . Are self-reported unhealthy food choices associated with an increased risk of breast cancer? Prospective cohort study using the British Food Standards Agency nutrient profiling system. BMJ Open. 2017; 7(6):e013718. PMC: 5577898. DOI: 10.1136/bmjopen-2016-013718. View

Deschasaux M, Huybrechts I, Murphy N, Julia C, Hercberg S, Srour B . Nutritional quality of food as represented by the FSAm-NPS nutrient profiling system underlying the Nutri-Score label and cancer risk in Europe: Results from the EPIC prospective cohort study. PLoS Med. 2018; 15(9):e1002651. PMC: 6143197. DOI: 10.1371/journal.pmed.1002651. View

Julia C, Touvier M, Mejean C, Ducrot P, Peneau S, Hercberg S . Development and validation of an individual dietary index based on the British Food Standard Agency nutrient profiling system in a French context. J Nutr. 2014; 144(12):2009-17. DOI: 10.3945/jn.114.199679. View

Fismen A, Smith O, Torsheim T, Rasmussen M, Pedersen Pagh T, Augustine L . Trends in Food Habits and Their Relation to Socioeconomic Status among Nordic Adolescents 2001/2002-2009/2010. PLoS One. 2016; 11(2):e0148541. PMC: 4747535. DOI: 10.1371/journal.pone.0148541. View

10.

Mendez M, Miles D, Poti J, Sotres-Alvarez D, Popkin B . Persistent disparities over time in the distribution of sugar-sweetened beverage intake among children in the United States. Am J Clin Nutr. 2018; 109(1):79-89. PMC: 6698637. DOI: 10.1093/ajcn/nqy123. View

11.

Vereecken C, Pedersen T, Ojala K, Krolner R, Dzielska A, Ahluwalia N . Fruit and vegetable consumption trends among adolescents from 2002 to 2010 in 33 countries. Eur J Public Health. 2015; 25 Suppl 2:16-9. DOI: 10.1093/eurpub/ckv012. View

12.

Bel S, Van den Abeele S, Lebacq T, Ost C, Brocatus L, Stievenart C . Protocol of the Belgian food consumption survey 2014: objectives, design and methods. Arch Public Health. 2016; 74:20. PMC: 4868031. DOI: 10.1186/s13690-016-0131-2. View

13.

Slimani N, Casagrande C, Nicolas G, Freisling H, Huybrechts I, Ocke M . The standardized computerized 24-h dietary recall method EPIC-Soft adapted for pan-European dietary monitoring. Eur J Clin Nutr. 2011; 65 Suppl 1:S5-15. DOI: 10.1038/ejcn.2011.83. View

14.

Moshfegh A, Rhodes D, Baer D, Murayi T, Clemens J, Rumpler W . The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am J Clin Nutr. 2008; 88(2):324-32. DOI: 10.1093/ajcn/88.2.324. View

15.

Black A . Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int J Obes Relat Metab Disord. 2000; 24(9):1119-30. DOI: 10.1038/sj.ijo.0801376. View

16.

Schofield W . Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985; 39 Suppl 1:5-41. View

17.

Maurer J, Taren D, Teixeira P, Thomson C, Lohman T, Going S . The psychosocial and behavioral characteristics related to energy misreporting. Nutr Rev. 2006; 64(2 Pt 1):53-66. DOI: 10.1111/j.1753-4887.2006.tb00188.x. View

18.

Black A, Cole T . Biased over- or under-reporting is characteristic of individuals whether over time or by different assessment methods. J Am Diet Assoc. 2001; 101(1):70-80. DOI: 10.1016/S0002-8223(01)00018-9. View

19.

Johnson R . Dietary intake--how do we measure what people are really eating?. Obes Res. 2002; 10 Suppl 1:63S-68S. DOI: 10.1038/oby.2002.192. View

20.

Bragg M, Liu P, Roberto C, Sarda V, Harris J, Brownell K . The use of sports references in marketing of food and beverage products in supermarkets. Public Health Nutr. 2012; 16(4):738-42. PMC: 10271797. DOI: 10.1017/S1368980012003163. View