AI-assisted dietary assessment and personalized “nudges” in urban Ghana: Preparing for scale-up
Diet-related risk factors cause 11 million deaths annually, making them the largest single factor included in the Global Burden of Disease analyses (Afshin et al. 2019). Recent trends associated with urbanization and the “nutrition transition”—which is characterized by shifts to unhealthy diets and...
| Autores principales: | , , , , , , , , , |
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| Formato: | Brief |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2024
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| Acceso en línea: | https://hdl.handle.net/10568/138824 |
| _version_ | 1855528727316791296 |
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| author | Gelli, Aulo Folson, Gloria Nwabuikwu, Odiche Bannerman, Boateng Ador, Gabriel Atadze, Vicentia Asante, Millicent McCloskey, Peter Nguyen, Phuong Hong Hughes, David |
| author_browse | Ador, Gabriel Asante, Millicent Atadze, Vicentia Bannerman, Boateng Folson, Gloria Gelli, Aulo Hughes, David McCloskey, Peter Nguyen, Phuong Hong Nwabuikwu, Odiche |
| author_facet | Gelli, Aulo Folson, Gloria Nwabuikwu, Odiche Bannerman, Boateng Ador, Gabriel Atadze, Vicentia Asante, Millicent McCloskey, Peter Nguyen, Phuong Hong Hughes, David |
| author_sort | Gelli, Aulo |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Diet-related risk factors cause 11 million deaths annually, making them the largest single factor included in the Global Burden of Disease analyses (Afshin et al. 2019). Recent trends associated with urbanization and the “nutrition transition”—which is characterized by shifts to unhealthy diets and increased consumption of energy dense and nutrient poor processed foods and decreased physical activity—have led to increases in rates of overweight and obesity (Popkin et al. 2020). Regular data collection to document population-level dietary intake is essential for effective, evidence-based nutrition actions (Hargreaves et al. 2022). However, collecting and analyzing dietary data is complex and expensive (Bell et al. 2017). Dietary assessments typically involve the use of the multi-pass 24-hour recall (24HR) method, which has been validated in adults reporting their intake and/or that of their young children (Gibson and Ferguson 2008), and to some degree in adolescents. The costs associated with conducting a 24HR are approximately $500 per recall (Adams et al. 2022). Mobile phone-based tools have potential to lower the cost of diet assessment; however, evidence is lacking on the validity and feasibility of conducting phone-based assessments in low- and middle-income countries (LMICs) (Bell et al. 2017). |
| format | Brief |
| id | CGSpace138824 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1388242025-11-06T06:34:54Z AI-assisted dietary assessment and personalized “nudges” in urban Ghana: Preparing for scale-up Gelli, Aulo Folson, Gloria Nwabuikwu, Odiche Bannerman, Boateng Ador, Gabriel Atadze, Vicentia Asante, Millicent McCloskey, Peter Nguyen, Phuong Hong Hughes, David artificial intelligence urban areas healthy diets Diet-related risk factors cause 11 million deaths annually, making them the largest single factor included in the Global Burden of Disease analyses (Afshin et al. 2019). Recent trends associated with urbanization and the “nutrition transition”—which is characterized by shifts to unhealthy diets and increased consumption of energy dense and nutrient poor processed foods and decreased physical activity—have led to increases in rates of overweight and obesity (Popkin et al. 2020). Regular data collection to document population-level dietary intake is essential for effective, evidence-based nutrition actions (Hargreaves et al. 2022). However, collecting and analyzing dietary data is complex and expensive (Bell et al. 2017). Dietary assessments typically involve the use of the multi-pass 24-hour recall (24HR) method, which has been validated in adults reporting their intake and/or that of their young children (Gibson and Ferguson 2008), and to some degree in adolescents. The costs associated with conducting a 24HR are approximately $500 per recall (Adams et al. 2022). Mobile phone-based tools have potential to lower the cost of diet assessment; however, evidence is lacking on the validity and feasibility of conducting phone-based assessments in low- and middle-income countries (LMICs) (Bell et al. 2017). 2024-02-01 2024-02-01T18:47:09Z 2024-02-01T18:47:09Z Brief https://hdl.handle.net/10568/138824 en https://hdl.handle.net/10568/137352 https://doi.org/10.2499/p15738coll2.137058 Open Access application/pdf International Food Policy Research Institute Gelli, Aulo; Folson, Gloria; et al. 2024. AI-assisted dietary assessment and personalized “nudges” in urban Ghana: Preparing for scale-up. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/138824 |
| spellingShingle | artificial intelligence urban areas healthy diets Gelli, Aulo Folson, Gloria Nwabuikwu, Odiche Bannerman, Boateng Ador, Gabriel Atadze, Vicentia Asante, Millicent McCloskey, Peter Nguyen, Phuong Hong Hughes, David AI-assisted dietary assessment and personalized “nudges” in urban Ghana: Preparing for scale-up |
| title | AI-assisted dietary assessment and personalized “nudges” in urban Ghana: Preparing for scale-up |
| title_full | AI-assisted dietary assessment and personalized “nudges” in urban Ghana: Preparing for scale-up |
| title_fullStr | AI-assisted dietary assessment and personalized “nudges” in urban Ghana: Preparing for scale-up |
| title_full_unstemmed | AI-assisted dietary assessment and personalized “nudges” in urban Ghana: Preparing for scale-up |
| title_short | AI-assisted dietary assessment and personalized “nudges” in urban Ghana: Preparing for scale-up |
| title_sort | ai assisted dietary assessment and personalized nudges in urban ghana preparing for scale up |
| topic | artificial intelligence urban areas healthy diets |
| url | https://hdl.handle.net/10568/138824 |
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