20.500.12556/DKUM-94772 A personalized approach to understanding food cravings and intake a study protocol Background: Studies on food craving and consumption often overlook the interconnectedness of risk factors, assuming uniform mechanisms that drive individuals to (over)consume food. This project seeks to address this gap by leveraging a precision health framework to explore whether multimodal clustering can predict weight and eating outcomes after six months, providing a more nuanced understanding of individual variability. Methods: The project will include a longitudinal study, encompassing several sub-studies where self-report, electrophysiological, and time series dynamic data will be collected at three time points. At baseline, participants will complete comprehensive assessments, including an electroencephalography (EEG) experiment and a one-week experience sampling study (ESM). Machine learning techniques will be employed to uncover distinct participant clusters, characterized by unique patterns of food consumption and weight changes over six months. Markers that best differentiate these profiles will be identified with explainable AI techniques, which aim to make machine learning model outputs understandable by highlighting the key features or patterns driving predictions, enabling personalized insights into key factors contributing to eating behaviors and weight management. Discussion: By exploring the variability of mechanisms influencing food consumption, eating regulation, and weight gain, we aim to uncover subgroups of individuals who are most affected by specific influences, such as stress, emotion regulation difficulties, or sleep deprivation. This project will advance theoretical understanding by integrating multimodal data and emphasizing idiographic methods to capture individual variability. Findings will provide a foundation for future research on precision approaches to eating behaviors and may offer insights into personalized strategies for prevention and management of both normative and disordered eating patterns. food cue reactivity EEG experienxe sampling methodology personalized medicine achine learning explainable artificial inteligence reaktivnost na prehranske dražljaje elektroencefalografija metoda vzorčenja izkušenj personalizirana medicina strojno učenje umetna inteligenca true false true Angleški jezik Slovenski jezik Znanstveno delo 2025-08-27 03:50:03 2025-12-19 13:21:42 2025-12-20 03:19:14 0000-00-00 00:00:00 2025 0 0 str. 1-13 št. članka 103 Letn. 13 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 2025-05-28 2025-06-04 159.98:615.851:612.39 238752771 10.1186/s40337-025-01303-0 2050-2974 RAZ_Zorjan_Sasa_2025.pdf RAZ_Zorjan_Sasa_2025.pdf 1 6940A0463665C7CE40D5A83D2A397C04 2f43e3973e46d183ce74d6cc7cd6c3b806d93d421e8b4207607fb4d7722c9ebf 6c693d12-dcd5-11f0-a44c-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=201571 https://doi.org/10.1186/s40337-025-01303-0 0 26f83e6c-82e8-11f0-8e8c-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=196527 Filozofska fakulteta 0 0 0