Exploration of Remote Nutrition Intervention for Metabolic Diseases Supported by Multimodal Dietary Data

Abstract

The prevalence rate of metabolic syndrome, type 2 diabetes and dyslipidemia in adults in China continues to rise, and the affected population is becoming younger. Nutritional intervention is a key measure for the control of metabolic chronic diseases. Traditional offline guidance is constrained by time and space, and dietary data relies on subjective recollection, which has shortcomings such as large errors and insufficient personalization. This article aims to explore new intervention pathways that are suitable for long-term management of chronic diseases. This study integrates multi-dimensional data on diet, physiology, metabolism, and behavior, relying on image recognition, wearable devices, and cloud technology, to build a remote nutrition intervention system that integrates data collection, intelligent analysis, individualized intervention plans, and online follow-up and guidance. Combining authoritative monitoring data and scientific research achievements, the architecture and application path are sorted out. The results confirm that multimodal data can overcome the inherent limits of traditional nutritional interventions and effectively improve the level of dietary management and the effectiveness of chronic disease control. This model is suitable for the development needs of primary healthcare and can provide theoretical and practical support for digital remote nutrition management of metabolic diseases.