Maria F. Vasiloglou, Switzerland

University of Bern ARTORG Center for Biomedical Engineering Research
Maria Vasiloglou is a Ph.D. candidate at AI in Health & Nutrition of ARTORG Center for Biomedical Engineering Research which belongs to the University of Bern, Switzerland. She holds a Master of Science in Medical Science, MSc (Med Sci) in Human Nutrition with specialization in Clinical Nutrition from the University of Glasgow and a BSc in Nutrition and Dietetics from Alexander Technological Educational Institute of Thessaloniki, Greece. She has published 16 papers in peer-reviewed journals. Maria was a research assistant in European and National research projects in the area of endocrinology and nutrition in Greece. She also taught nutrition for professionals while she was working with children and adolescents living with diabetes, eating disorders or obesity at Pediatric Endocrine Clinics in Greece. Before that, she did an internship at the Joint Research Center of the European Commission in Italy and in particular at the Institute for Health and Consumer Protection. There, she worked on science-based policy support in the field of nutrition, evaluating the effect of merging innovation and technology in the food and pharmaceutical area. Her main scientific interests focus on mHealth, eHealth and innovative technologies for dietary monitoring and assessment.

Presenter of 1 Presentation

 Conference Calendar
PARALLEL SESSION

Comparison of novel and traditional dietary assessment methods in diabetes patients

Date
Sat, 05.06.2021
Session Name
0500 - Live Q&A: Parallel Session 18: Nutrition And Food Technologies (ID 55)
Lecture Time
14:42 - 15:02
Presenter
Authors

Abstract

Abstract Body

Accurate estimation of carbohydrates as well as other nutrients, are beneficial in the management of diabetes mellitus, leading to improved glycemic control and balanced dietary patterns. This presentation includes an overview of studies that compare conventional versus innovative dietary assessment methods and discusses the challenges of both categories. Various conventional dietary assessment methods exist (e.g. 24-h food recall, food frequency questionnaires) which aim at measuring dietary intake. However, existing methods encounter different drawbacks such as lack of precision, inaccuracy in portion size estimation and non-real-time feedback. Innovative methods of dietary assessment that have been introduced will be presented, with a focus on image-based apps which use food photos/videos as an input and ,via artificial intelligence, translate them into nutrients. Finally, based on international surveys, the preferences, criteria and barriers related to recommendation or usage of nutrition apps from the perspective of healthcare professionals and end-users will be presented.

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