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Creation and validation of a NOVA scored picture set to evaluate ultra-processed foods.

Hutelin, Zach, Ahrens, Monica, Baugh, Mary Elizabeth, Oster, Mary E., Hanlon, Alexandra L., DiFeliceantonio, Alexandra G.·2024·Appetite
DOI 10.1016/j.appet.2024.107358
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  1. Afshin, 2019, Health effects of dietary risks in 195 countries, 1990–2017: A systematic analysis for the global burden of disease study 2017, The Lancet, 393, 1958, 10.1016/S0140-6736(19)30041-8 · DOI · Paper page
  2. Astrup, 2022, Does the concept of “ultra-processed foods” help inform dietary guidelines, beyond conventional classification systems? no, The American Journal of Clinical Nutrition
  3. Becker, 1964, Measuring utility by a single-response sequential method, Behavioral Science, 9, 226, 10.1002/bs.3830090304 · DOI
  4. Bhattacharyya, 2012, Exposure to the common food additive carrageenan leads to glucose intolerance, insulin resistance and inhibition of insulin signalling in HepG2 cells and C57BL/6J mice, Diabetologia, 55, 194, 10.1007/s00125-011-2333-z · DOI
  5. Blechert, 2019, Food-Pics_Extended—an image database for experimental research on eating and appetite: Additional images, normative ratings and an updated review, Frontiers in Psychology, 10, 10.3389/fpsyg.2019.00307 · DOI
  6. Blechert, 2014, Food-pics: An image database for experimental research on eating and appetite, Frontiers in Psychology, 5, 10.3389/fpsyg.2014.00617 · DOI
  7. Boyland, 2022, Association of food and nonalcoholic beverage marketing with children and adolescents' eating behaviors and health: A systematic review and meta-analysis, JAMA Pediatrics, 176, 10.1001/jamapediatrics.2022.1037 · DOI
  8. Boyland, 2015, Food advertising to children and its effects on diet: Review of recent prevalence and impact data, Pediatric Diabetes, 16, 331, 10.1111/pedi.12278 · DOI
  9. Braesco, 2022, Ultra-processed foods: How functional is the NOVA system?, European Journal of Clinical Nutrition, 10.1038/s41430-022-01099-1 · DOI · Paper page
  10. Carmody, 2011, Energetic consequences of thermal and nonthermal food processing, Proceedings of the National Academy of Sciences, 108, 19199, 10.1073/pnas.1112128108 · DOI
  11. Cooksey-Stowers, 2017, Food swamps predict obesity rates better than food deserts in the United States, International Journal of Environmental Research and Public Health, 14, 10.3390/ijerph14111366 · DOI
  12. Dalenberg, 2020, Short-term consumption of sucralose with, but not without, carbohydrate impairs neural and metabolic sensitivity to sugar in humans, Cell Metabolism, 31, 493, 10.1016/j.cmet.2020.01.014 · DOI
  13. de Araujo, 2013, Metabolic regulation of brain response to food cues, Current Biology, 23, 878, 10.1016/j.cub.2013.04.001 · DOI
  14. DiFeliceantonio, 2018, Supra-Additive effects of combining fat and carbohydrate on food reward, Cell Metabolism, 28, 33, 10.1016/j.cmet.2018.05.018 · DOI
  15. Faulkner, 2014, Perceived ‘healthiness’ of foods can influence consumers' estimations of energy density and appropriate portion size, International Journal of Obesity, 38, 106, 10.1038/ijo.2013.69 · DOI
  16. Fromm, 2021, Development of MacroPics: A novel food picture set to dissociate the effects of carbohydrate and fat on eating behaviors, Appetite, 159, 10.1016/j.appet.2020.105051 · DOI
  17. Hellinger, 1909, Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen, Journal für die Reine und Angewandte Mathematik, 1909, 210, 10.1515/crll.1909.136.210 · DOI
  18. Huybrechts, 2022, Characterization of the degree of food processing in the European Prospective Investigation into Cancer and Nutrition: Application of the Nova classification and validation using selected biomarkers of food processing, Frontiers in Nutrition, 9, 10.3389/fnut.2022.1035580 · DOI · Paper page
  19. Hwang, 2018, Bisphenol A exposure and type 2 diabetes mellitus risk: A meta-analysis, BMC Endocrine Disorders, 18, 81, 10.1186/s12902-018-0310-y · DOI
  20. Juul, 2021, Ultra-processed foods and cardiovascular diseases: Potential mechanisms of action, Advances in Nutrition, 12, 1673, 10.1093/advances/nmab049 · DOI · Paper page
  21. Krippendorff, 1970, Estimating the reliability, systematic error and random error of interval data, Educational and Psychological Measurement, 30, 61, 10.1177/001316447003000105 · DOI
  22. Krippendorff, 2004, Reliability in content analysis: Some common misconceptions and recommendations, Human Communication Research, 30, 411
  23. Labbe, 2017, Is portion size selection associated with expected satiation, perceived healthfulness or expected tastiness? A case study on pizza using a photograph-based computer task, Appetite, 108, 311, 10.1016/j.appet.2016.10.012 · DOI
  24. Leung, 2022, Food insecurity and ultra-processed food consumption: The modifying role of participation in the Supplemental Nutrition Assistance Program (SNAP), The American Journal of Clinical Nutrition, 116, 197, 10.1093/ajcn/nqac049 · DOI · Paper page
  25. Lim, 2009, Derivation and evaluation of a labeled hedonic scale, Chemical Senses, 34, 739, 10.1093/chemse/bjp054 · DOI
  26. Marino, 2021, A systematic review of worldwide consumption of ultra-processed foods: Findings and criticisms, Nutrients, 13, 2778, 10.3390/nu13082778 · DOI
  27. Monteiro, 2022, Does the concept of “ultra-processed foods” help inform dietary guidelines, beyond conventional classification systems? yes, The American Journal of Clinical Nutrition, 10.1093/ajcn/nqac122 · DOI
  28. Monteiro, 2016, NOVA. The star shines bright, World Nutrition, 7
  29. Monteiro, 2019, Ultra-processed foods: What they are and how to identify them, Public Health Nutrition, 22, 936, 10.1017/S1368980018003762 · DOI · Paper page
  30. Monteiro, 2018, The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing, Public Health Nutrition, 21, 5, 10.1017/S1368980017000234 · DOI · Paper page
  31. Newton-Fenner, 2023, Economic value in the brain: A meta-analysis of willingness-to-pay using the becker-DeGroot-marschak auction, PLoS One, 18, 10.1371/journal.pone.0286969 · DOI
  32. Perszyk, 2021, Fat and carbohydrate interact to potentiate food reward in healthy weight but not in overweight or obesity, Nutrients, 13, 10.3390/nu13041203 · DOI
  33. Plassmann, 2007, Orbitofrontal cortex encodes willingness to pay in everyday economic transactions, Journal of Neuroscience, 27, 9984, 10.1523/JNEUROSCI.2131-07.2007 · DOI · Paper page
  34. Provencher, 2009, Perceived healthiness of food. If it's healthy, you can eat more, Appetite, 52, 340, 10.1016/j.appet.2008.11.005 · DOI · Paper page
  35. Ren, 2010, Nutrient selection in the absence of taste receptor signaling, Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 30, 8012, 10.1523/JNEUROSCI.5749-09.2010 · DOI
  36. Rogers, 2024, Evidence that carbohydrate-to-fat ratio and taste, but not energy density or NOVA level of processing, are determinants of food liking and food reward, Appetite, 193, 10.1016/j.appet.2023.107124 · DOI
  37. Romaguera, 2021, Consumption of ultra-processed foods and drinks and colorectal, breast, and prostate cancer, Clinical Nutrition, 40, 1537, 10.1016/j.clnu.2021.02.033 · DOI
  38. Sadeghirad, 2016, Influence of unhealthy food and beverage marketing on children's dietary intake and preference: A systematic review and meta-analysis of randomized trials, Obesity Reviews, 17, 945, 10.1111/obr.12445 · DOI
  39. Serafim, 2022, Ultra-processed food availability and sociodemographic associated factors in a Brazilian municipality, Frontiers in Nutrition, 9, 10.3389/fnut.2022.858089 · DOI
  40. Small, 2019, Processed foods and food reward, Science (New York, N.Y.), 363, 346, 10.1126/science.aav0556 · DOI · Paper page
  41. Steele, 2016, Ultra-processed foods and added sugars in the US diet: Evidence from a nationally representative cross-sectional study, BMJ Open, 6
  42. Steele, 2023, Identifying and estimating ultraprocessed food intake in the US NHANES according to the nova classification system of food processing, The Journal of Nutrition, 153, 225, 10.1016/j.tjnut.2022.09.001 · DOI
  43. Tang, 2014, Behavioral and neural valuation of foods is driven by implicit knowledge of caloric content, Psychological Science, 25, 2168, 10.1177/0956797614552081 · DOI
  44. Veldhuizen, 2017, Integration of sweet taste and metabolism determines carbohydrate reward, Current Biology, 27, 2476, 10.1016/j.cub.2017.07.018 · DOI
  45. Wolever, 2019, Impact of oat processing on glycaemic and insulinaemic responses in healthy humans: A randomised clinical trial, British Journal of Nutrition, 121, 1264, 10.1017/S0007114519000370 · DOI

Cited by

Sorted by citations

Metadata

Authors
Hutelin, Z., Ahrens, M., Baugh, M. E., Oster, M. E., Hanlon, A. L., & DiFeliceantonio, A. G.
ORCID
DiFeliceantonio, Alexandra G.
Venue
Appetite
Publisher
Elsevier BV
Published
2024-07
Volume
198
Pages
107358
ISSN
0195-6663
DOI
10.1016/j.appet.2024.107358
Cited by
5
References
5
Language
en
Licence
Funders
  • NIDDK
  • NSF

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