Multi-Modal Wearable and Biological Data Integration Reveals Physiological Signatures of Weight Variation in Overweight and Obese Adults: An AI4Food Machine Learning Study

This study is part of the AI4Food project, which aims to identify the physiological and behavioral factors influencing weight variation in overweight and obese individuals through integrative analysis of multi-source data. A 30-day crossover intervention was conducted on 93 participants at IMDEA Nutrition Institute, combining a traditional phase (questionnaires, food diaries) and a digital phase (Fitbit Sense Smartwatch, FreeStyle Libre 2 glucose sensor). The collected data encompassed sleep, glucose, skin temperature, nutrition, and gut microbiome information. After rigorous preprocessing and

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