INTRODUCTION: An uncontrollably rising core body temperature (T C ) is an indicator of an impending exertional heat illness. However, measuring T C invasively in field settings is challenging. By contrast, wearable sensors combined with machine-learning algorithms can continuously monitor T C nonintrusively. Here, we prospectively validated 2B-Cool , a hardware/software system that automatically learns how individuals respond to heat stress and provides individualized estimates of T C , 20-min ahead predictions, and early warning of a rising T C .
METHODS: We performed a crossover heat stress study in an environmental chamber, involving 11 men and 11 women (mean ± SD age = 20 ± 2 yr) who performed three bouts of varying physical activities on a treadmill over a 7.5-h trial, each under four different clothing and environmental conditions. Subjects wore the 2B-Cool system, consisting of a smartwatch, which collected vital signs, and a paired smartphone, which housed machine-learning algorithms and used the vital sign data to make individualized real-time forecasts. Subjects also wore a chest strap heart rate sensor and a rectal probe for comparison purposes.
RESULTS: We observed very good agreement between the 2B-Cool forecasts and the measured T C , with a mean bias of 0.16°C for T C estimates and nearly 75% of measurements falling within the 95% prediction intervals of ±0.62°C for the 20-min predictions. The early-warning system results for a 38.50°C threshold yielded a 98% sensitivity, an 81% specificity, a prediction horizon of 35 min, and a false alarm rate of 0.12 events per hour. We observed no sex differences in the measured or predicted peak T C .
CONCLUSION: 2B-Cool provides early warning of a rising T C with a sufficient lead time to enable clinical interventions and to help reduce the risk of exertional heat illness.
