Validity of heart rate derived core temperature estimation during simulated firefighting tasks

Abstract

Rectal core temperature monitoring can help fire services mitigate heat injury but can be invasive and impractical. EQ02 + LifeMonitor provides a non-invasive estimation of core temperature. Therefore, the primary purpose of this study was to determine the validity of the EQ02 + LifeMonitor compared to the gold standard rectal thermometer core temperature assessment, as well as the potential influence of turnout gear on the estimated and physiological strain experienced during these activities. Thirteen participants completed simulated firefighting tasks with and without turnout gear, involving four rounds of a 5-min walk on a treadmill at 2.8 mph/2.5% grade and 20 deadlifts over 5 min in an environmental chamber set to 40.6 °C; 50% humidity. During each trial participants wore both an EQ02 + LifeMonitor and DataTherm II rectal thermometer. The results from the devices were statistically equivalent (p < 0.001), yet there was a statistically significant difference in the value (~ 0.1 °C; p < 0.001). There was a significant effect of devices [p < 0.001] and time [p < 0.001], but no interaction effect [p = 0.70] on core temperature drift. Estimated core temperature was marginally different from that measured via the DataTherm II. The EQ02 on average overestimated core temperature. Heart rate, rating of perceived exertion, and area under the curve of core temperature were significantly elevated due to turnout gear [ps < 0.025], but not core temperature skin temperature, or ventilatory rate [ps > 0.372]. These results suggest the EQ02 + LifeMonitor may be a viable, non-invasive alternative for assessing core temperature compared to rectal temperature monitoring, especially during rigorous, intermittent activities. Turnout gear does however increase heart rate, cumulative core temperature, and perceived exertion. Additionally, the validity of the estimated core temperature is not impacted by the use turnout gear. This is likely due to significant changes in heart rate, which allowed the heart-rate derived estimate of core temperature to remain consistent with changes in DataTherm II rectal temperatures.

Figure 1

Bland–Altman plot comparing estimated core temperature of the Equivital (ECT) to the measured rectal core temperature (RCT). Dotted lines = Upper and Lower limits of agreement. Red Line = Bias Mean difference reported as degrees Celsius. Made in Microsoft Excel.

Figure 2

Lin’s concordance correlation to determine the agreement between the Equivital and DataTherm. Made in Microsoft Excel. Red line = actual line LCC; Black line = 1.0 correlation for comparison.

Figure 3

Representation of changes over time in core temperature measurements. Condition (recreational clothing × turnout gear) was used as a covariate. Data represented as mean and standard deviation. Unless specified, all timepoints were significantly different (all p-values < 0.037). ns = not significantly different (p > 0.05). Made in Graphpad Prism.

Figure 4

Resulting post hoc ANOVA analyses from MANOVA analysis. Data represented as mean and standard deviation. Effects are reported from the MANOVA test. Made in Graphpad Prism.

Figure 5

Two-way ANOVA comparison of Rating of Perceived Exertion (RPE) utilizing the Borg 6–20 Scale. Data represented as mean and standard deviation.

Figure 6

Student t-test comparison of cumulative heat stress between conditions. Graph represent mean and standard deviation and individual data points represent individual participants. *Denotes significant difference p < 0.05. Made in Graphpad Prism.