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mHealth nutrition apps in dietary assessment
 
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Institute of Health Science, Nestlé Research and Development, Switzerland
 
 
Publication date: 2022-05-27
 
 
Public Health Toxicol 2022;2(Supplement 1):A19
 
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ABSTRACT
Conventional dietary assessment methods rely heavily on self-reporting and are prone to errors. Thus, there is a growing need for more specific and accurate dietary assessment methods. Due to the technological proliferation, image-based smartphone apps with intelligent features, which may improve dietary assessment, have been developed. However, there is room for improvement in the field of mHealth due to the lack of validation and robust scientific work behind the use of such systems. Moreover, when using image-based nutrition apps, a large number of pictures (approx. 12%) is discarded due to human errors made in the capturing procedure. Trials should be conducted under free-living conditions and mHealth solutions should be compared with conventional ones. Collaboration of multidisciplinary teams is of vital importance and especially the needs of healthcare professionals and end-user should be taken into account when designing and developing nutrition apps.
 
REFERENCES (6)
1.
Reber E, Gomes F, Vasiloglou MF, Schuetz P, Stanga Z. Nutritional Risk Screening and Assessment. J Clin Med. 2019;8(7):1065. doi:10.3390/jcm8071065
 
2.
Lu Y, Stathopoulou T, Vasiloglou MF, et al. goFOODTM: An Artificial Intelligence System for Dietary Assessment. Sensors (Basel). 2020;20(15):4283. doi:10.3390/s20154283
 
3.
Vasiloglou MF, Christodoulidis S, Reber E, et al. What Healthcare Professionals Think of "Nutrition & Diet" Apps: An International Survey. Nutrients. 2020;12(8):2214. doi:10.3390/nu12082214
 
4.
Vasiloglou MF, Christodoulidis S, Reber E, et al. Perspectives and Preferences of Adult Smartphone Users Regarding Nutrition and Diet Apps: Web-Based Survey Study. JMIR Mhealth Uhealth. 2021;9(7):e27885. doi:10.2196/27885
 
5.
Vasiloglou MF, van der Horst K, Stathopoulou T, et al. The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App. JMIR Mhealth Uhealth. 2021;9(1):e24467. doi:10.2196/24467
 
6.
Vasiloglou MF, Marcano I, Lizama S, Papathanail I, Spanakis EK, Mougiakakou S. Multimedia Data-Based Mobile Applications for Dietary Assessment. J Diabetes Sci Technol. 2022. doi:10.1177/19322968221085026
 
ISSN:2732-8929
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