Current research status and perspectives on the core body temperature measurement and prediction models for high-temperature environments
- VernacularTitle:高温环境机体核心温度测量和预测模型研究现状及展望
- Author:
Wanchang WANG
1
;
Hua YANG
2
;
Chao NIU
3
Author Information
- Publication Type:Review
- Keywords: high temperature environment; heat stroke; core temperature; core temperature measurement; core temperature prediction model
- From: Journal of Environmental and Occupational Medicine 2025;42(11):1405-1413
- CountryChina
- Language:Chinese
- Abstract: To prevent heat injury among individuals working in high-temperature environments, monitoring their core temperature is of significant importance. This paper reviewed the current research status of measurement methods and prediction models for core temperature in such conditions. First, it highlighted the ongoing global increase in temperatures and the consequent rise in the risk of heat stroke among workers in high-temperature settings, underscoring the critical role of core temperature monitoring in occupational health protection. Next, it systematically introduced the application of rectal temperature measurement, capsule gastrointestinal temperature measurement, and heat flux method for core temperature assessment. Furthermore, it summarized the application status of widely used core temperature prediction models, such as Gagge model, Fiala model, the Predictive Heat Stress (PHS) model, the ECTempTM (the estimated core temperature) model, and the Heat Strain Decision Aid (HSDA) model, while also outlining recent advances in machine learning-based prediction models. Finally, the advantages and disadvantages of various measurement methods and prediction models were comprehensively analyzed, and future directions for the development of core temperature prediction models were proposed. The findings indicate that rectal temperature measurement remains the most direct method for assessing core temperature; prediction models for core temperature in high-temperature environments have achieved considerable progress and have been applied in certain practical scenarios; and future efforts should leverage deep learning algorithms to analyze relevant data and develop more accurate core temperature prediction models.
