Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care.
apps
artificial intelligence
dietary assessment
dietetic practice
dietitian
food
mHealth
mobile phone
nutrition care process
smartphone
Journal
Nutrients
ISSN: 2072-6643
Titre abrégé: Nutrients
Pays: Switzerland
ID NLM: 101521595
Informations de publication
Date de publication:
05 Aug 2024
05 Aug 2024
Historique:
received:
08
07
2024
revised:
25
07
2024
accepted:
01
08
2024
medline:
10
8
2024
pubmed:
10
8
2024
entrez:
10
8
2024
Statut:
epublish
Résumé
For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps' features, quality, behaviour change potential, and comparative validity of dietary assessment via manual logging and AI. The top 200 free and paid nutrition-related apps from Australia's Apple App and Google Play stores were screened (
Identifiants
pubmed: 39125452
pii: nu16152573
doi: 10.3390/nu16152573
pii:
doi:
Types de publication
Journal Article
Comparative Study
Langues
eng
Sous-ensembles de citation
IM