Digital Tools and Artificial Intelligence in Nutritionist Practice and Research (REK_320)
About Study Course
Objective
The aim of the study course is to develop students’ understanding and practical competence in working with digital methods of measuring health and nutrition (eHealth, mobile health) in order to train students’ ability to choose and justify the most appropriate methods in clinical practice and research, critically evaluate data quality and limitations, as well as to develop, analyse, interpret and communicate nutrition and health related data. At the end of the course, students demonstrate what they have learned by developing a science conference poster and creating a protocol for a randomized controlled study.
Prerequisites
Basic knowledge of research methodology.• basic nutrition assessment skills• behavioural change theories
Learning outcomes
1.After the course, the student:
- explain the basic concepts of digital transformation, eHealth and mHealth, as well as the role, benefits and limitations of teleconsultation in the nutritionist’s practice;
- describe and compare methods for measuring paper and digital dietary intake;
- describe digital health measurement tools in the context of nutrition;
- explain the basic principles for measuring body mass/composition and energy consumption;
- explain the sources of nutritional data errors in nutritional studies;
- describe the guiding principles of GDPR in the processing of health data, confidentiality requirements and the guiding principles of safe use of AI in dietary practices;
- describes the possibilities and risks of using AI in research and understands the conditions for academic integrity and use of AI
1.- recommends and justifies the most appropriate method of measuring dietary intake for a specific purpose and situation;
- interpret technology data in the context of nutrition by identifying potential adjacent factors and data quality risks and formulate conclusions understandable to the patient;
- interpret the results of bioimpedence, DEXA and indirect calorimetry;
- identify nutritional and digital measurement data errors, possible mixing factors and sources of heterogeneity;
- use AI support to document nutritional advice;
- develop a poster for a scientific conference on a categorised study and present it;
- plan and develop a study protocol for a specific target group with an mHealth component in intervention, prepare and present a summary of the protocol.
1.- integrate digital data into the nutritionist’s work process
- make informed decisions on the choice of method and interpretation of data
- ensure data quality and formulate reasoned conclusions
- respect ethics, GDPR and confidentiality in digital nutrition practices/research
- safe and targeted use of AI in nutritional practices
- working on an interdisciplinary team in the context of digital solutions
- communicate scientific information in a professional format
- plans a digital study
