Prospective Validation of 2B-Cool: Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries

Abstract

ABSTRACT Introduction An uncontrollably rising core body temperature (TC) is an indicator of an impending exertional heat illness. However, measuring TC invasively in field settings is challenging. By contrast, wearable sensors combined with machine-learning algorithms can continuously monitor TC 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 TC, 20-min ahead predictions, and early warning of a rising TC. 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 TC, with a mean bias of 0.16°C for TC 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 TC. Conclusion 2B-Cool provides early warning of a rising TC with a sufficient lead time to enable clinical interventions and to help reduce the risk of exertional heat illness.