Mathematical prediction of core body temperature from environment, activity, and clothing: The heat strain decision aid (HSDA).
J Therm Biol
; 64: 78-85, 2017 Feb.
Article
em En
| MEDLINE
| ID: mdl-28166950
ABSTRACT
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 (Tc) 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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Sudorese
/
Exercício Físico
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Resposta ao Choque Térmico
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Temperatura Alta
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Modelos Teóricos
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article