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Nutritional Characteristics of Foods With Addictive Potential: A Machine-Learning Approach.

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Title: Nutritional Characteristics of Foods With Addictive Potential: A Machine-Learning Approach.
Authors: 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.
Source: American Journal of Public Health. Jul2026, Vol. 116 Issue 7, p950-959. 10p.
Subjects: Random forest algorithms, Compulsive behavior, Research funding, Carbohydrates, Food consumption, Visual analog scale, Nutritional requirements, Descriptive statistics, Carbohydrate content of food, Food habits, Machine learning, Food supply, Glycemic index, Data analysis software, Nutrition, Algorithms, Social classes, Educational attainment
Geographic Terms: United States
Abstract: 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) [ABSTRACT FROM AUTHOR]
Copyright of American Journal of Public Health is the property of American Public Health Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Nutritional Characteristics of Foods With Addictive Potential: A Machine-Learning Approach.
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  Data: <searchLink fieldCode="AR" term="%22Gearhardt%2C+Ashley+N%2E%22">Gearhardt, Ashley N.</searchLink><br /><searchLink fieldCode="AR" term="%22Hutelin%2C+Zach%22">Hutelin, Zach</searchLink><br /><searchLink fieldCode="AR" term="%22Nartey%2C+Emmanuel%22">Nartey, Emmanuel</searchLink><br /><searchLink fieldCode="AR" term="%22Ahrens%2C+Monica+L%2E%22">Ahrens, Monica L.</searchLink><br /><searchLink fieldCode="AR" term="%22Baugh%2C+Mary+Elizabeth%22">Baugh, Mary Elizabeth</searchLink><br /><searchLink fieldCode="AR" term="%22Fazzino%2C+Tera+L%2E%22">Fazzino, Tera L.</searchLink><br /><searchLink fieldCode="AR" term="%22LaFata%2C+Erica+M%2E%22">LaFata, Erica M.</searchLink><br /><searchLink fieldCode="AR" term="%22Sonneville%2C+Kendrin+R%2E%22">Sonneville, Kendrin R.</searchLink><br /><searchLink fieldCode="AR" term="%22DiFeliceantonio%2C+Alexandra+G%2E%22">DiFeliceantonio, Alexandra G.</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink>
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  Data: 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) [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of American Journal of Public Health is the property of American Public Health Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.2105/AJPH.2026.308500
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      – Code: eng
        Text: English
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        PageCount: 10
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    Subjects:
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Compulsive behavior
        Type: general
      – SubjectFull: Research funding
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      – SubjectFull: Carbohydrates
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      – SubjectFull: Nutritional requirements
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Carbohydrate content of food
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      – SubjectFull: Machine learning
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      – SubjectFull: Food supply
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      – SubjectFull: Glycemic index
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              Text: Jul2026
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