In Medicine and science in sports and exercise
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. In contrast, wearable sensors combined with machine-learning (ML) algorithms can continuously monitor TC non-intrusively. 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-minute 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 [average age = 20 (standard deviation = ±2) years] who performed three bouts of varying physical activities on a treadmill over a 7.5-hour 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 ML 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 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-minute predictions. The early-warning system results for a 38.50 °C threshold yielded a 98% sensitivity, 81% specificity, prediction horizon of 35 minutes, 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 help reduce the risk of exertional heat illness.
Laxminarayan Srinivas, Hornby Samantha, Belval Luke N, Giersch Gabrielle E W, Morrissey Margaret C, Casa Douglas J, Reifman Jaques
2022-Nov-29
