Assessment of Performance between Different Wildland Firefighter Crews for a Modified Pack Hike Test

1.1. Problem Statement

The current PHT is a baseline work capacity test that does not account for the daily physical demands placed on hand crews, especially type 1, who often work in terrain with elevation changes. Therefore, the aim of this study was to assess whether a modified PHT (a more arduous and situational-specific (presence of inclined terrain) work capacity test) should be utilized to determine occupational fitness amongst hand crews.

1.2. Research Hypothesis

It was hypothesized that there would be larger differences in performance test outcomes amongst type 1 and type 2 hand crews when looking at the modified PHT. In regards to these performance differences, the following outcomes were assumed: type 1 crew would achieve faster completion time (as indicated by faster pace) scores, increased heart rate max (HRmax) and average (HRave) values, and larger heart rate recovery (HRrec) values than type 2 crew during the modified PHT, thus indicating increased fitness levels of the T1C.

2.1. Experimental Approach to the Problem

This study utilized a cross-sectional design to compare performance outcome measures amongst two classes of hand crews (type 1 and type 2). All participants engaged in one maximal effort test: one modified PHT (mPHT) that was performed on incline terrain and required the carrying of 20 kg packs. The comparisons between crew types allowed the determination of performance differences or indifferences between the hand crew members.

2.2. Participants

This population that completed the experiment consists of type 1 and type 2 HCM. Nineteen participants (one female in T1C, all the rest were males) from the California hand crews employed by the U.S. Forest Service agreed to participate in the experimental protocol and descriptive statistics such as crew type, age (years), height (meters), weight (kg) and job experience (years) were obtained prior to testing (Table 1). The number of subjects was obtained based on firefighter availability and the willingness of the captains in the region to have their crews be asked to participate, which did limit the number of participants available. In order to be included in the current study, all hand crew members must have been employed during at least one fire season at their current rank (type 1 or type 2). Participants between the two groups were not matched for any anthropometric characteristics as there was a desire to take an adequate cross-sectional sample of the groups as they existed. Furthermore, participants must have had no history of musculo

skeletal injury to the lower extremities or torso that resulted in lost work time during the past fire season. For the current study, musculoskeletal injury can be defined as any injury to the musculoskeletal system, such as muscles, tendons, ligaments and bones; lost work time can be defined as any alteration to tactical activities such as physical training or fire suppression activities [11]. Lastly, at the time of testing, participants must have been free of any condition that could have affected hiking performance with load carriage. Common conditions that might have affected hiking performance included, but were not limited to, acute illnesses such as cold, flu, and COVID-19. Participants were recruited to perform this test at the beginning of their training season as testing could not occur during the fire season due to the potential impact on the working availability of the firefighters (to minimize any impact on job performance due to the emergency nature of the work).

2.3. Procedures

All procedures were approved by the California State University Long Beach Institutional Review Board. Prior to beginning any data collection, all participants signed an informed consent document. Secondly, participants were asked to sign the medical health and history form as well as a physical activity readiness questionnaire. Finally, by using the ACSM preparticipation screening algorithm, all participants were confirmed safe to perform the maximal effort exercise test [12].

2.4. Instrumentation

In order to confirm the accuracy of all distances and incline levels, Garmin GPS technology was used for three hiking trials performed by the researcher on the trail participants were expected to perform on [15]. Prior to the execution of the mPHT, all participants were required to attach Polar H10 (Polar, USA) heart rate monitors to their Xiphoid process and download the Polar Beat app on their phones. In the app, participants recorded a hiking session during the test in order to collect HRave (average heart rate), HRmax (maximum heart rate) and TTC (time to completion).

2.5. Data Extraction

After the execution of the mPHT, each participant had two activities recorded in their Polar Beat app: the mPHT and the recovery period. The app automatically gives a session analysis with the maximum HR, average HR and Time to Completion (TTC) of the trial. All participants were logged into the PI’s Polar Beat account to allow for direct collection of the data. In order to indirectly assess the HR recovery of each participant, the author utilized the HRave and HRmax variables to mathematically solve for the minimum HR; HR recovery was computed by subtracting the HRmin from the HRmax during the 1-minute recovery session.

2.6. Statistical Analysis

Data were analyzed using SPSS 28.0 software (SPSS Inc, Chicago, Illinois, USA). Normality of the sample was conducted with the Shapiro-Wilk test. HRave measurements were represented as a percentage of the individuals' age-predicted maximum HR. Age-predicted HRmax was collected via the following equation: HRmax = 208-.7(age) [16]. TTC measurements were converted into ‘pace’ values to allow for universal comparison amongst future studies executing hikes with different parameters. Pacing was computed as follows: Pace = TTC/1.3, where 1.3 was the mileage covered over the hike. Between-group differences of the dependent variables were compared using an independent samples T-test. The level of significance was set to p < .05. Effect sizes were assessed using Cohen’s d values calculated within SPSS.

4.1. Relative Intensity as an Indicator of Fitness

To the best of the author’s knowledge, this was the first study that attempted to alter the parameters of the PHT and identify fitness discrepancies in performance between type 1 and type 2 HCM. The authors aimed to differentiate the fitness tiers of the two crews by instructing all individuals to execute the test at a maximal pace, thus altering the traditional 45-minute cut-off window of the PHT. Additionally, the PHT is typically executed on a track with no elevation gain [4], and the author included the variable of incline to increase the intensity and energetic demands of the test in order to facilitate maximal exertion from the participants. This addition of incline allowed for increased specificity within the occupational tasks associated with wildland fire suppression, which typically take place on rugged mountainside terrain [4] while simultaneously carrying a heavy load of equipment, simulated by the 20 kg pack.

After execution of the modified PHT, it was observed that none of the performance or heart rate response variables between crews achieved statistical significance (p > .05). These findings suggest the mPHT protocols were not sufficient enough to differentiate hiking performance between the crews; this was possibly due to the fact that participants executed the hike at a self-selected pace and temperature was not controlled for. Tucker and Noakes noted that exercise bouts executed at high ambient temperatures provoke large variations in exertion levels when athletes are allowed to regulate their work rate at a self-selected pace [17]. Although the temperature was not controlled, the author did note that all tests were conducted above 33.3 degrees Celsius, which could have resulted in participants deviating from the ‘maximal effort pace’ cued by the researcher, in order to respond to their rising core temperatures, and consequent increased ratings of perceived exertion in conjunction with decreased arousal levels [18, 19]. The T1C group achieved a mean HRmax of 100.8%, suggesting this group achieved maximum effort, while T2C achieved a mean HRmax of 92.3%. While this is not a statistically significant difference, it could signify the ability of the T1C to perform at a higher physiologic threshold, potentially indicating a higher level of fitness. This goes in conjunction with the T1C group’s ability to execute the test at a faster pace therefore sustaining higher HR levels for a longer duration of time.

This variation in exertion level opposes the traditional models of fatigue and decrements in performance one might expect during a controlled maximal effort exercise test [20]. In agreement with Galloway and Maughan, a 2015 study observed potential differences between self-selected pace versus standardized RAMP protocols amongst aerobically trained females during an incremental running test to volitional fatigue. Augustine et al. noted that the self-selected pace protocols elicited lower measurements of relative intensities and time to exhaustion by the participants, thus reducing performance outcome measures [21]. Future studies looking to assess cardiovascular fitness between type 1 and type 2 HCM should employ RAMP protocols during an incremental exercise test in a controlled setting to minimize the influence of strategy and temperature fluctuations.

4.2. Anaerobic Influence on Maximal Effort Testing

The measurements of HRmax and HRave were utilized to assess the intensity of the test relative to each HCM [22]. All HR values were expressed as a percentage of the individual age-predicted HRmax; The Tanaka equation, HRmax = 208-(.7*age), was employed in order to avoid underestimating the maximal heart rates of older individuals [16]. The positive linear relationship between HR and VO2 has been well-established in previous work [23]. For anaerobic efforts, heart rate is not an accurate index of the metabolic or cardiovascular stress experienced [24]. In the current study, type 1 and type 2 crews obtained HRave values, indicated as %HRmax, of 92.9% and 84.7%, respectively. Age and body weight could have potentially impacted these results. Anthropometric characteristics were not matched between groups for this study. The T1C had a mean age of more than 6.8 years and a mean body weight of 7.6kg greater compared to the T2C. This could have potentially altered HRmax and HRave variables, even though an attempt was made to account for age using the Tanaka equation. The effect of additional body weight is unclear as the composition of the weight (i.e., tissue type) is unknown. The overall tendency towards a faster pace of the T1C crews suggests that age and body weight did not hinder performance. The additional experience of the T1C crews may have led to the development of additional muscle tissue over time that makes them better suited to an increased performance (in terms of pace) and the ability to operate at a higher HR level for a longer period of time.

Previous authors have illustrated that high-level endurance athletes typically reach their lactate deflection point (LaT), a clear indicator of a switch between aerobic and anaerobic metabolism, between 84% and 86% of the relative HRmax [25, 26]. The current mPHT protocols were not sufficient in differentiating fitness levels between crews based on HR response and levels of relative intensity. It is possible that HR in isolation may not be an adequate variable to assess metabolic efficiency during activities that do not primarily rely upon aerobic metabolism, and therefore, variables such as LaT or blood lactate accumulation post-exercise, may allow for a deeper understanding of the underlying differences between type 1 and type 2 crews [24].

4.3. Recovery Capabilities as an Indicator of Fitness

Lastly, type 1 HCM saw 5.3% higher (non-statistically significant) HRrec values than type 2 HCM. Although these results were not significant (P = .62), it is worth mentioning the slight positive trend in cardiovascular fitness towards type 1 compared to type 2 crews. Aside from the differences between the two crews, it must be noted that the HRrec values obtained were particularly high based on previous studies performed on elite athletes [27]. Additionally, there are compelling data to show a positive correlation between VO_2max and HRrec measurements in well-trained endurance athletes (r = .51) [28]. It is plausible that both crews assessed in the current study exhibit above-average fitness levels and may not fully represent the broad spectrum of hand crews across the entire country. This assumption was further supported when this particular type 2 crew served as a “feeder system” for HCM to improve their fitness and expertise to eventually earn a spot in the IHC.

4.4. Future Studies and Limitations

This study is not without its limitations. A lack of consistency within the testing environment and protocols can be seen as a primary limitation of the study. Temperatures could not be strictly controlled and could have resulted in greater physiological strain, subsequently altering performance and HR variables. Along with temperature, food intake was not monitored and could have affected the participants’ ability to supply the energetic demand of the maximal effort bout. Additionally, each individual likely experienced deviations in their hike due to the following ecological variables: rocks, potholes, and alternating surfaces. Furthermore, the horizontal displacement covered by each participant was not taken into account and could have affected their time to completion scores. It must be noted that the PI did not account for the current fitness level at the time of testing. All HCM were tested prior to their first mandatory period of physical training with their crews, however, the researchers were not granted access to this testing and therefore, could not incorporate it into the analysis presented here. Therefore, the fitness variables achieved for each participant may have been a result of the residual effects of training adaptations from the prior year, along with their personal training regimen in preparation for the new season.

Although no significant differences were observed between crews for any performance and HR variables assessed, these values may be more pronounced in a controlled environment such as an indoor graded exercise test, where temperatures can be controlled, and the “strategy” component of the test is diminished as a result of all individuals executing the test at the same work rate. In addition, both crews appeared to approach or reach maximal exertion during the test. However, measuring relative intensity by means of HR response was not successful in differentiating fitness levels between crew types. It is likely that this maximal effort exercise in high ambient temperatures did not rely primarily on aerobic means and therefore challenged the classic model of HR response indicating fitness level based upon exertion. Analyzing variables such as lactate deflection points as well as overall lactate accumulation may illustrate differences in fitness levels between crews to a higher magnitude and should be utilized as a marker in further studies. Lastly, the sample size for this study consisted of two hand crews from Central California; the type 2 crew observed has been said to serve as a “feeder” system for hand crew members to improve their fitness, skill and field knowledge to eventually earn a spot on the type 1 crew observed. Regional limitations were a problem as was the willingness of fire captains to solicit their crews to undergo this test. Additional crews up to 160 kilometers away from the location of the PI were approached, but a willingness to participate was limited prior to the start of the working season. As such, the attainable sample size was quite limited. An a-priori analysis for sample size was not conducted due to the limited ability to attain participants. The researchers approached all available wildland firefighters within a reasonable geographic radius (approximately 160 km) and were only able to attain nineteen total participants. In spite of that, the researchers still believed that while small in size, the available sample size would still serve as a solid starting point for a novel test. As significance was approached for HRmax, it stands to reason that a larger sample size would help further delineate whether or not that statistical significance exists as the power is ultimately limited. Future studies should recruit a larger sample size of crews from different geographical locations to better represent possible fitness discrepancies between crew types across the entire Forest Service.