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

Abstract

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.

Keywords: athlete management systems; core body temperature; exertional heat illness; heat stroke; machine learning; occupational physiology; physiological modeling; sports medicine; wearable technology.

Figure 1

Populations at a high risk for HRI and, thus, most important for the monitoring of CBT. This review focuses on the monitoring of CBT in athletes and other patients in dynamic environments such as first responders, oil rig and construction workers, and military, as solutions already exist for inpatient and neonate monitoring.

Figure 2

A schematic representation of the CBT at which heat-related illness occurs [5,6,20,21,22,35]. Includes how wearable CBT measurement systems can provide early intervention to reduce the need for the treatment and, ultimately, morbidity and mortality of HRI. EAMCs = exercise-associated muscle cramps.

Figure 3

Flowchart depicting the search results and methodology of the study selection. Additionally depicted is the enumeration of multiple prediction models from some studies.

Figure 4

Forest plot depicting the RMSE ± standard deviation of studies reporting this metric. Tsadok et al. DW [47] is excluded both from the plot and from the displayed average due to its status as an outlier. The asterisks (*) denote models that did not report a RMSE SD, and so, the error bars represent ± ½ of the reported RMSE. [29,31,32,34,37,46,56,58,59,62,64,66].

Figure 5

A workflow schematic depicting the hypothetical interoperability of an algorithm for CBT prediction using wearable devices [25,26,27,54]. Both sensor inputs to the CBT prediction algorithm and its output to other systems are included in the schematic. Heart Rate is used to calculate both the CBT and internal workload. EMR = Electronic Medical Record, RPE = Rating of Perceived Exertion, and SmO₂ = Muscle Oxygen Saturation.

Figure 6

Conclusions from this review and proposed future directions for the technology. HRI = heat-related illness, CBT = core body temperature, RMSE = root mean square error, WBGT = wet bulb globe temperature, AMS = athlete management system, and EMR = electronic medical record.