Nutritional Characteristics of Foods With Addictive Potential: A Machine-Learning Approach.
Objectives. To identify nutritional characteristics associated with the perceived addictive potential of commonly consumed foods in the US food supply, the majority of which are ultraprocessed foods (UPFs). Methods. In a demographically diverse sample of US adults (n = 1664; 55.2% female), participa...
| Publicado en: | American Journal of Public Health Vol. 116; no. 7; pp. 950 - 960 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
American Public Health Association
Jul2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=194643736&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 194643736 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00900036 APH jtl: American Journal of Public Health issn: 00900036 maglogo: N pubinfo: dt: Jul2026 vid: 116 iid: 7 pid: 44 pub: American Public Health Association artinfo: ui: 194643736 10.2105/AJPH.2026.308500 ppf: 950 ppct: 10 formats: tig: atl: Nutritional Characteristics of Foods With Addictive Potential: A Machine-Learning Approach. aug: au: Gearhardt, Ashley N. Hutelin, Zach Nartey, Emmanuel Ahrens, Monica L. Baugh, Mary Elizabeth Fazzino, Tera L. LaFata, Erica M. Sonneville, Kendrin R. DiFeliceantonio, Alexandra G. affil: Department of Psychology, University of Michigan, Ann Arbor, MI. Fralin Biomedical Research Institute, Virginia Tech Carilion, Roanoke, VA. Center for Biostatistics and Health Data Science, Department of Statistics, Virginia Tech, Blacksburg, VA. Department of Psychology, University of Kansas, Lawrence, KS. Oregon Research Institute, Eugene, OR. Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI. su: United States Compulsive behavior Food consumption Food habits Food supply Nutrition Social classes Educational attainment Random forest algorithms Research funding Carbohydrates Visual analog scale Nutritional requirements Descriptive statistics Carbohydrate content of food Machine learning Glycemic index Data analysis software Algorithms sug: subj: Compulsive behavior Food consumption Food habits Food supply Nutrition Social classes Educational attainment United States Random forest algorithms Research funding Carbohydrates Visual analog scale Nutritional requirements Descriptive statistics Carbohydrate content of food Machine learning Glycemic index Data analysis software Algorithms ab: Objectives. To identify nutritional characteristics associated with the perceived addictive potential of commonly consumed foods in the US food supply, the majority of which are ultraprocessed foods (UPFs). Methods. In a demographically diverse sample of US adults (n = 1664; 55.2% female), participants rated the perceived addictiveness of 297 commonly consumed foods (74.4% UPFs). Data were collected through Prolific in June 2024. Machine-learning models identified nutritional predictors of addictiveness using both the 15 variables required on US Nutrition Facts labels and an expanded set of 166 nutrient characteristics from the Nutrition Data System for Research. Results. Models performed comparably and revealed consistent nonlinear associations between nutrient content and perceived addictiveness. Foods higher in carbohydrates, glycemic load, energy density, and fat were rated as more addictive. These nutrient profiles were rare in minimally processed foods but common in UPFs, which frequently exceeded multiple addictive nutrient thresholds simultaneously. Conclusions. This study identifies a nutritional signature linked to perceived addictive potential. Findings provide a data-driven framework for identifying foods most likely to promote compulsive intake and inform policies aimed at creating a healthier, less addictive food environment. (Am J Public Health. 2026;116(7):950–959. https://doi.org/10.2105/AJPH.2026.308500) pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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