Risk factors associated with live fire training: Buildup of heat stress and fatigue, recovery and role of micro-breaks

Abstract

Background: Forty five percent of on-duty firefighter deaths every year are cardiovascular (CV) related. Heat stress and fatigue buildup are two common occupational risk factors for firefighters. These risk factors may increase the firefighters' chances of having cardiac events or even death. Objective: Buildup of heat stress and fatigue in firefighters and their recovery from these stresses during live-fire training exercises was investigated. Methods: Twenty full time firefighters, from two different fire-stations, performed live-fire training exercise constituting three real life firefighting scenarios and rest periods incorporated in between the scenarios. Core body temperature (CBT) and heart rate (HR) were measured in real time, using an FDA approved radio pill and a polar heart rate belt. Baseline and post-scenario measurements of perceptions of physical exertion, thermal stress and respiratory distress were also collected. Results: Heart rate and CBT increased significantly with the progression of the training. The HR and CBT levels at the end of each rest period were significantly higher than the baseline values. The actual rest periods provided after each scenario were shorter than the time needed for adequate recovery. Most of the firefighters crossed the industrial limit of hyperthermia and maximum recommended level of HR elevation from baseline. Firefighters from one of the stations took micro-breaks during scenarios and were found to spend less percent time over the limit of hyperthermia. These firefighters also needed less time to recover to baseline levels of HR and CBT. Conclusions: There was significant heat stress and fatigue buildup as a result of the live-fire training exercise. Longer rest periods should be provided between scenarios to ensure recovery. Also, taking micro-breaks during a live fire training scenario might help in preventing heat stress and fatigue buildup.

Uncertainty Analysis of the Core Body Temperature Under Thermal and Physical Stress Using a Three-Dimensional Whole Body Model

“…Experimental data from the previous studies describing a firefighting training drill were used to develop the human whole body computational model. A typical training drill consisted of alternating firefighting scenarios and rest periods [28,29]. The firefighting scenarios consisted of (1) search for the origin of flame, (2) hose advancement, and (3) search and rescue of the victim.…”

“…The firefighting scenarios consisted of (1) search for the origin of flame, (2) hose advancement, and (3) search and rescue of the victim. De-identified datasets including the heart rate, experimental core body temperature ( T c_E ), and physiological details of a single firefighter (age = 33 yr, weight = 86.7 kg) were obtained from Mani et al [29] and used in this study. The experimental study, conducted by Mani et al [29], was approved by University of Cincinnati Institutional Review Board and the firefighter provided informed consent prior to participating in the study.…”

Comparison Between Experimental and Heart Rate-Derived Core Body Temperatures Using a Three-Dimensional Whole Body Model

“…The human subject data of firefighter 2 and firefighter 3 were obtained during firefighting training drills in the study reported by Mani et al [17], which was performed with the approval of the University of Cincinnati Institutional Review Board. The Fig.…”

“…These drills consisted of alternating periods of firefighting and rest scenarios. The de-identified datasets included the heart rate, the experimental core body temperature, T c_E , and the physiological parameters of firefighters 1, 2, and 3 [16,17]. Additional details such as the age and weight of the firefighters are listed in Table 1.…”

Ergonomics and Beyond

“…Literature provides two models for predicting hyperthermia (core temperature >100.4°F): (a) one based on physiology of heat stress (Yokota, Berglund, Santee, Butler, & Hoyt, 2005; Yokota, Berglund, & Bathalon, 2012; Yokota, Berglund, Santee, et al, 2012) and (b) data-driven decision trees (Mani, Rao, James, & Bhattacharya, 2015). Use of such predictive modeling has a critical role in designing and developing innovative interventions to minimize heat stress in the working population of all ages in hot environments (Mani et al, 2013, 2015), such as firefighters and agricultural and construction workers.…”