Gait instability and estimated core temperature predict exertional heat stroke

Area of Science:

• Sports Medicine
• Environmental Physiology
• Biomedical Engineering

Background:

• Exertional heat stroke (EHS) poses significant risks to athletes, military personnel, and workers in hot climates.
• High core body temperature (Tcr) and central nervous system (CNS) dysfunction are key indicators of EHS.
• Early detection and prediction of EHS are crucial for preventing severe outcomes.

Purpose of the Study:

• To evaluate the efficacy of algorithms estimating core body temperature (Tcr) and gait instability for predicting exertional heat stroke (EHS) onset.
• To determine if trunk-worn sensor data can provide real-time alerts for impending EHS.
• To assess the predictive accuracy of combined physiological and biomechanical monitoring.

Main Methods:

• Collected heart rate and accelerometry data from 1806 US military personnel during high-risk training exercises.
• Utilized chest-worn sensors to capture physiological and gait data.
• Developed algorithms to estimate core temperature (ECTemp) from heart rate and gait instability from accelerometry.

Main Results:

• Six cases of heat stroke were identified, with estimated core temperatures ranging from 39.2°C to 40.8°C.
• The combined algorithm successfully predicted all six EHS cases at least 3.5 minutes before collapse.
• A 6.1% false positive rate was observed, with no false negatives.

Conclusions:

• The combination of estimated core body temperature and gait instability algorithms shows promise for real-time EHS prediction.
• This approach could enable timely interventions to prevent heat stroke in at-risk populations.
• Further validation of these algorithms in diverse operational environments is warranted.