Estimated and measured core temperature responses to high-intensity warm weather military training: implications for exertional heat illness risk assessment

A real-time biphasic Kalman filter-based model for estimating human core temperature from heart rate measurements for application in the occupational field

Introduction

Early identification of hypothermia or hyperthermia is of vital importance, and real-time monitoring of core temperature (CT) of the workers exposed to thermal environments is an extremely valuable tool. From the existing literature studies, the model developed by Buller et al. in their study of 2013 that generates real-time estimates of CT from heart rate (HR) measurements using the Kalman filter (KF) shows good potential for occupational application. However, some aspects could be improved to reliably handle the existing very wide range of workers and work activities. This study presents a real-time CT estimation model, called the Biphasic Kalman filter-based (BKFB) model, based on HR measurement, with characteristics suited to application in the occupational field.

Methods

Thirteen healthy subjects (six female and seven male) were included in the study to perform three consecutive tasks simulating work activities. During each test, an ingestible CT sensor was used to measure CT and a HR sensor to measure HR. The KF methodology was used to develop the BKFB model.

Results

An algorithm with a biphasic structure was developed using two different models for the increasing and decreasing phases of CT, with the ability to switch between the two based on an HR threshold. CT estimates were compared with CT measurements, and with respect to overall root mean square error (RMSE), the BKFB model achieved a sizeable reduction (0.28 ± 0.12°C) compared to the Buller et al. model (0.34 ± 0.16°C).

Discussion

The BKFB model introduced some modifications over the Buller et al. model for a more effective application in the occupational field. It was developed using data collected from a sample of workers (heavily weighted toward middle-aged, not very fit, and with a considerable fraction of female workers), and it also included two different modeling of CT (for the up- and down-phases), which allowed for better behavioral modeling in the two different stages. The BKFB model provides CT estimates reasonably in comparison to the measured intra-abdominal temperature values in both the activity and recovery phases but is more practical and easier to use for a real-time monitoring system of the workers' thermal states.

Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review

Heat-related illnesses, which range from heat exhaustion to heatstroke, affect thousands of individuals worldwide every year and are characterized by extreme hyperthermia with the core body temperature (CBT) usually > 40 °C, decline in physical and athletic performance, CNS dysfunction, and, eventually, multiorgan failure. The measurement of CBT has been shown to predict heat-related illness and its severity, but the current measurement methods are not practical for use in high acuity and high motion settings due to their invasive and obstructive nature or excessive costs. Noninvasive predictions of CBT using wearable technology and predictive algorithms offer the potential for continuous CBT monitoring and early intervention to prevent HRI in athletic, military, and intense work environments. Thus far, there has been a lack of peer-reviewed literature assessing the efficacy of wearable devices and predictive analytics to predict CBT to mitigate heat-related illness. This systematic review identified 20 studies representing a total of 25 distinct algorithms to predict the core body temperature using wearable technology. While a high accuracy in prediction was noted, with 17 out of 18 algorithms meeting the clinical validity standards. few algorithms incorporated individual and environmental data into their core body temperature prediction algorithms, despite the known impact of individual health and situational and environmental factors on CBT. Robust machine learning methods offer the ability to develop more accurate, reliable, and personalized CBT prediction algorithms using wearable devices by including additional data on user characteristics, workout intensity, and the surrounding environment. The integration and interoperability of CBT prediction algorithms with existing heat-related illness prevention and treatment tools, including heat indices such as the WBGT, athlete management systems, and electronic medical records, will further prevent HRI and increase the availability and speed of data access during critical heat events, improving the clinical decision-making process for athletic trainers and physicians, sports scientists, employers, and military officers.

A Heart Rate Based Algorithm to Estimate Core Temperature Responses in Elite Athletes Exercising in the Heat

Purpose

Non-invasive non-obtrusive continuous and real-time monitoring of core temperature (Tc) may enhance pacing strategies, the efficacy of heat mitigation measures, and early identification of athletes at risk for heat-related disorders. The Estimated Core Temperature (ECTemp™) algorithm uses sequential heart rate (HR) values to predict Tc. We examined the validity of ECTemp™ among elite athletes exercising in the heat.

Methods

101 elite athletes performed an exercise test in simulated hot and humid environmental conditions (ambient temperature: 31.6 ± 1.0°C, relative humidity: 74 ± 5%). Tc was continuously measured using a validated ingestible telemetric temperature capsule system. In addition, HR was continuously measured and used to compute the estimated core temperature (Tc-est) using the ECTemp™ algorithm.

Results

Athletes exercised for 44 ± 10 min and n = 5,025 readouts of Tc (range: 35.8-40.4°C), HR (range: 45-207 bpm), and Tc-est (range: 36.7-39.9°C) were collected. Tc-est demonstrated a small yet significant bias of 0.15 ± 0.29°C (p < 0.001) compared to Tc, with a limit of agreement of ±0.45°C and a root mean square error of 0.35 ± 0.18°C. Utilizing the ECTemp™ algorithm as a diagnostic test resulted in a fair to excellent sensitivity (73-96%) and specificity (72-93%) for Tc-est thresholds between 37.75 and 38.75°C, but a low to very-low sensitivity (50-0%) for Tc-est thresholds >39.0°C, due to a high prevalence of false-negative observations.

Conclusion

ECTemp™ provides a valuable and representative indication of thermal strain in the low- to mid-range of Tc values observed during exercise in the heat. It may, therefore, be a useful non-invasive and non-obtrusive tool to inform athletes and coaches about the estimated core temperature during controlled hyperthermia heat acclimation protocols. However, the ECTemp™ algorithm, in its current form, should not solely be used to identify athletes at risk for heat-related disorders due to low sensitivity and high false-negative rate in the upper end of the Tc spectrum.

Accuracy of an estimated core temperature algorithm for agricultural workers

There is a substantial burden of occupational health effects from heat exposure. We sought to assess the accuracy of estimated core body temperature (CBT_est) derived from an algorithm that uses sequential heart rate and initializing CBT,¹ compared with gastrointestinal temperature measured using more invasive ingestible sensors (CBT_gi), among outdoor agricultural workers. We analyzed CBT_est and CBT_gi data from Washington State, USA, pear and apple harvesters collected across one work shift in 2015 (13,413 observations, 35 participants) using Bland Altman methods. The mean (standard deviation, range) CBT_gi was 37.7 (0.4, 36.5-39.4)°C. Overall CBT bias (limits of agreement) was -0.14 (±0.76)°C. Biases ranged from -0.006 to -0.75 °C. The algorithm, which does not require the use of ingestible sensors, may be a practical tool in research among groups of workers for evaluating the effectiveness of interventions to prevent adverse occupational heat health effects.

Field validation of The Heat Strain Decision Aid during military load carriage

OBJECTIVES

We aimed to determine the agreement between actual and predicted core body temperature, using the Heat Strain Decision Aid (HSDA), in non-Ground Close Combat (GCC) personnel wearing multi terrain pattern clothing during two stages of load carriage in temperate conditions.

DESIGN

Cross-sectional.

METHODS

Sixty participants (men = 49, women = 11, age 31 ± 8 years; height 171.1 ± 9.0 cm; body mass 78.1 ± 11.5 kg) completed two stages of load carriage, of increasing metabolic rate, as part of the development of new British Army physical employment standards (PES). An ingestible gastrointestinal sensor was used to measure core temperature. Testing was completed in wet bulb globe temperature conditions; 1.2-12.6 °C. Predictive accuracy and precision were analysed using individual and group mean inputs. Assessments were evaluated by bias, limits of agreement (LoA), mean absolute error (MAE), and root mean square error (RMSE). Accuracy was evaluated using a prediction bias of ±0.27 °C and by comparing predictions to the standard deviation of the actual core temperature.

RESULTS

Modelling individual predictions provided an acceptable level of accuracy based on bias criterion; where the total of all trials bias ± LoA was 0.08 ± 0.82 °C. Predicted values were in close agreement with the actual data: MAE 0.37 °C and RMSE 0.46 °C for the collective data. Modelling using group mean inputs were less accurate than using individual inputs, but within the mean observed.

CONCLUSION

The HSDA acceptably predicts core temperature during load carriage to the new British Army non-GCC PES, in temperate conditions.

On the real-time prevention and monitoring of exertional heat illness in military personnel

The proliferation of user-friendly low-cost wearable sensors has brought the concept of real-time physiological monitoring for exertional heat illness to the cusp of reality. This paper reviews and discusses the current state of the art in real-time physiological status monitoring for exertional heat illness mitigation within the military context. The review examines how both advanced sensor systems, models and algorithms are being combined in an international and collaborative way and how this is providing real solutions to military units to reduce the risk held by the commander. This paper provides additional detail into the process of integrating physiological status monitoring into military training, it explores the development of on-body sensors, the algorithms that can provide actionable information, the process of planning and dynamic risk assessment and describes some of the physiological monitoring systems that are currently being developed by the representative nations. It then discusses the knowledge gaps of how the technology will be integrated into military training, the importance of meaningful, accurate information that is both sensitive and specific and further developing the accuracy of the algorithms and models that are being employed. Finally, it talks about future direction and how individualizing physiological status monitoring can lead to performance enhancement in the form of individualized heat acclimatization programs. In conclusion, physiological status monitoring is at a stage of transition and integration where it can be used effectively to manage and reduce exertional heat illness to enable military personnel to train hard-train safe.