Research On Sports Health Management Strategy In The Context Of Big Data

With the rapid development of big data technology, sports health management is also making continuous progress, but at the same time, it also faces some challenges, such as low data processing efficiency and inadequate personalized services. In response to these pain points, this study designed and built a comprehensive health management system based on big data, which mainly includes two core parts: one is the health monitoring management system, and the other is the follow-up service management model. In terms of technical implementation, we integrated Internet of Things devices (IoT), distributed databases, and an improved dynamic weighted clustering algorithm to achieve efficient collection and accurate analysis of health data, and further generate personalized intervention recommendations. From the experimental results, this system has shown obvious advantages in multiple core indicators. For example: data collection efficiency has increased by 33.3%; data analysis accuracy has increased by 7.9%; the user's health improvement rate has increased from the original 48% to 72%. It can be said that in practical applications, this system not only solves the efficiency and accuracy problems in traditional methods, but also significantly enhances the effectiveness of personalized intervention. The innovation of this study lies in the deep integration of multi-layer system architecture and intelligent algorithms, and proposes an efficient and intelligent sports health management solution. This not only has strong reference value in theory, but also provides strong support for personalized health intervention and health data management in reality. It also points out an important direction for future research on intelligent health management.