Potter Adam W, Blanchard Laurie A, Friedl Karl E, Cadarette Bruce S, Hoyt Reed W
Biophysics and Biomedical Modeling Division, US Army Research Institute of Environmental Medicine, 10 General Greene Avenue, Bldg 42, Natick, MA 01760-5007, USA.
Thermal and Mountain Medicine Division, US Army Research Institute of Environmental Medicine, 10 General Greene Avenue, Bldg 42, Natick, MA 01760-5007, USA.
J Therm Biol. 2017 Feb;64:78-85. doi: 10.1016/j.jtherbio.2017.01.003. Epub 2017 Jan 16.
Physiological models provide useful summaries of complex interrelated regulatory functions. These can often be reduced to simple input requirements and simple predictions for pragmatic applications. This paper demonstrates this modeling efficiency by tracing the development of one such simple model, the Heat Strain Decision Aid (HSDA), originally developed to address Army needs. The HSDA, which derives from the Givoni-Goldman equilibrium body core temperature prediction model, uses 16 inputs from four elements: individual characteristics, physical activity, clothing biophysics, and environmental conditions. These inputs are used to mathematically predict core temperature (T) rise over time and can estimate water turnover from sweat loss. Based on a history of military applications such as derivation of training and mission planning tools, we conclude that the HSDA model is a robust integration of physiological rules that can guide a variety of useful predictions. The HSDA model is limited to generalized predictions of thermal strain and does not provide individualized predictions that could be obtained from physiological sensor data-driven predictive models. This fully transparent physiological model should be improved and extended with new findings and new challenging scenarios.
生理模型为复杂的相互关联的调节功能提供了有用的总结。对于实际应用而言，这些模型通常可以简化为简单的输入要求和简单的预测。本文通过追溯一个这样的简单模型——热应激决策辅助工具（HSDA）的发展过程，展示了这种建模效率。HSDA最初是为满足陆军需求而开发的，它源自吉沃尼 - 戈德曼平衡体核温度预测模型，使用来自四个要素的16个输入：个体特征、身体活动、服装生物物理学和环境条件。这些输入用于通过数学方法预测核温度（T）随时间的升高，并可根据汗液流失估算水分周转率。基于其在军事应用方面的历史，如训练和任务规划工具的推导，我们得出结论，HSDA模型是生理规则的强大整合，能够指导各种有用的预测。HSDA模型仅限于热应激的广义预测，不能提供可从生理传感器数据驱动的预测模型中获得的个性化预测。这个完全透明的生理模型应随着新发现和新的具有挑战性的场景而得到改进和扩展。
