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. 2025 Jun 4;13(1):103.
doi: 10.1186/s40337-025-01303-0.

A personalized approach to understanding food cravings and intake: a study protocol

Saša Zorjan  1 , Sašo Karakatič  2 , Marina Horvat  3 , Satja Mulej Bratec  3 , Živa Krajnc  3
Affiliations

A personalized approach to understanding food cravings and intake: a study protocol

Saša Zorjan et al. J Eat Disord. .

Abstract

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.

Keywords: Craving regulation; EEG; Experience sampling; Explainable artificial intelligence; Food cue reactivity; Food intake; Personalized medicine; Unsupervised machine learning.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study will be carried out in accordance with the 1964 Declaration of Helsinki. Ethics approval was obtained from the local ethics committee (038-27-193/2024/7/FF/UM). Participation in the study will be voluntary, and all participants will provide written informed consent prior to commencing. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
A graphical representation of the approach used in the project
Fig. 2
Fig. 2
A graphical representation of study procedures. Note. Full lines represent the sequence of activities; dotted lines represent data flow
Fig. 3
Fig. 3
An example of a single trial in the EEG paradigm
See this image and copyright information in PMC

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