Zorjan Saša, Karakatič Sašo, Horvat Marina, Bratec Satja Mulej, Krajnc Živa
Department of Psychology, Faculty of Arts, University of Maribor, Koroška cesta 160, Maribor, 2000, Slovenia.
Institute of Informatics, Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, Maribor, 2000, Slovenia.
J Eat Disord. 2025 Jun 4;13(1):103. doi: 10.1186/s40337-025-01303-0.
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.
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.
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.
关于食物渴望和消费的研究往往忽视了风险因素的相互关联性，假定存在驱动个体（过度）消费食物的统一机制。本项目旨在通过利用精准健康框架来填补这一空白，以探索多模态聚类是否能够预测六个月后的体重和饮食结果，从而对个体差异有更细致入微的理解。
该项目将包括一项纵向研究，涵盖多个子研究，在三个时间点收集自我报告、电生理和时间序列动态数据。在基线时，参与者将完成全面评估，包括脑电图（EEG）实验和为期一周的经验取样研究（ESM）。将采用机器学习技术来揭示不同的参与者聚类，其特征是六个月内独特的食物消费和体重变化模式。将使用可解释人工智能技术识别最能区分这些特征的标志物，该技术旨在通过突出驱动预测的关键特征或模式，使机器学习模型输出易于理解，从而对影响饮食行为和体重管理的关键因素进行个性化洞察。
通过探索影响食物消费、饮食调节和体重增加的机制的变异性，我们旨在发现受特定影响（如压力、情绪调节困难或睡眠不足）影响最大的个体亚组。本项目将通过整合多模态数据并强调采用个性化方法来捕捉个体差异，推进理论理解。研究结果将为未来关于饮食行为精准方法的研究奠定基础，并可能为预防和管理正常及紊乱饮食模式的个性化策略提供见解。
