Accuracy of Algorithm to Non-Invasively Predict Core Body Temperature Using the Kenzen Wearable Device

With climate change increasing global temperatures, more workers are exposed to hotter ambient temperatures that exacerbate risk for heat injury and illness. Continuously monitoring core body temperature (T-C) can help workers avoid reaching unsafe T-C. However, continuous T-C measurements are currently cost-prohibitive or invasive for daily use. Here, we show that Kenzen's wearable device can accurately predict T-C compared to gold standard T-C measurements (rectal probe or gastrointestinal pill). Data from four different studies (n = 52 trials; 27 unique subjects; >4000 min data) were used to develop and validate Kenzen's machine learning T-C algorithm, which uses subject's real-time physiological data combined with baseline anthropometric data. We show Kenzen's T-C algorithm meets pre-established accuracy criteria compared to gold standard T-C: mean absolute error = 0.25 degrees C, root mean squared error = 0.30 degrees C, Pearson r correlation = 0.94, standard error of the measurement = 0.18 degrees C, and mean bias = 0.07 degrees C. Overall, the Kenzen T-C algorithm is accurate for a wide range of T-C, environmental temperatures (13-43 degrees C), light to vigorous heart rate zones, and both biological sexes. To our knowledge, this is the first study demonstrating a wearable device can accurately predict T-C in real-time, thus offering workers protection from heat injuries and illnesses.