Cargando…
Relative Validation of an Artificial Intelligence–Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study
BACKGROUND: Thorough dietary assessment is essential to obtain accurate food and nutrient intake data yet challenging because of the limitations of current methods. Image-based methods may decrease energy underreporting and increase the validity of self-reported dietary intake. Keenoa is an image-as...
Autores principales: | Moyen, Audrey, Rappaport, Aviva Ilysse, Fleurent-Grégoire, Chloé, Tessier, Anne-Julie, Brazeau, Anne-Sophie, Chevalier, Stéphanie |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9723975/ https://www.ncbi.nlm.nih.gov/pubmed/36409539 http://dx.doi.org/10.2196/40449 |
Ejemplares similares
-
Lifestyle Behavior Changes and Associated Risk Factors During the COVID-19 Pandemic: Results from the Canadian COVIDiet Online Cohort Study
por: Tessier, Anne-Julie, et al.
Publicado: (2023) -
Adopting a Less Healthy Lifestyle Pattern During the COVID-19 Pandemic Is Modulated by Body Image Dissatisfaction and Increased Stress in Adults of the Canadian COVIDiet Study
por: Tessier, Anne-Julie, et al.
Publicado: (2022) -
Validity and Reliability of the Smart Food Diary Keenoa Against Recovery Biomarkers: A Study Protocol
por: Moyen, Audrey, et al.
Publicado: (2022) -
Artificial intelligence and machine learning in mobile apps for mental health: A scoping review
por: Milne-Ives, Madison, et al.
Publicado: (2022) -
Digital Natives’ Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study
por: Haggenmüller, Sarah, et al.
Publicado: (2021)