Prediction of Core Body Temperature from Multiple Variables.

Area of Science:

• Physiological measurement
• Environmental physiology
• Protective clothing research

Background:

• Accurate core body temperature (T c) monitoring is crucial for industrial and military applications.
• Previous models predicted T c using insulated skin temperature (T is) and micro-climate temperature (T mc), but accuracy varied.
• Need for improved T c prediction under diverse thermal and clothing conditions.

Purpose of the Study:

• To enhance the prediction of rectal temperature (T re) as a measure of T c.
• To incorporate additional physiological and environmental variables into T c prediction models.
• To validate prediction models under various clothing (permeable/impermeable) and climatic conditions (neutral, moderate, hot, with/without solar radiation).

Main Methods:

• Twelve males and nine females completed six trials involving treadmill walking and rest.
• Measurements included heart rate (HR), skin temperature (T s) at 11 sites, T is, breathing rate (f), T mc, and T re (gold standard).
• Stepwise multiple regression and bootstrap methodology were used to develop the prediction model from 30 variables.

Main Results:

• The optimal prediction model included T is, T mc, HR, and work status (rest/exercise).
• This model predicted T re with a standard error of the estimate of 0.27°C and an adjusted r² of 0.86.
• The model achieved 97% sensitivity and 85% specificity in predicting individuals reaching 39°C.

Conclusions:

• Insulated skin temperature (T is) is the most significant individual predictor of T re.
• A practical model incorporating T is, T mc, HR, and work status accurately predicts T c.
• This study demonstrates the viability of predicting T c in field environments using measurable parameters.