Heart Rate Measurements
Heart rate was monitored using a Polar S720i transmitter (Body Media, Pittsburgh, PA, USA) attached with an elasticized belt fitted around the chest and taped in place (Figures 3 and 4). Heart rate was recorded every 10 seconds during the rest phase, and stair-climbing test HR data were expressed as a %HRR. This index of cardiovascular strain is recommended by the American College of Sports Medicine (1,3), and it takes into account individually measured resting and maximal HRs. All tests were overseen by experienced investigators who had been trained in the proper technique, format, and procedures for each of the tests. The tests were explained and demonstrated to all the participants to ensure uniform procedures.
The results of the stair-climbing test were quantified using an appropriate function, deriving frequency, and phase from the sum of a straight line, with slope and intercept and a sinusoidal function, defined by amplitude, frequency, and phase. The value of %HRR measured during the test (%HRRt) was considered as a time-dependent variable; the value of %HRRt had a specific basal value (%HRR0) for each subject during the course of the test, and this value had a linear tendency to increase (Δ%HRR/t) because there was a progressive exertion. In addition, the value underwent some fluctuations (A%HRR) as a function of the moment in which each subject was going up or down the flight of stairs. Subjects also carried out a fixed number of fluctuations (ω) at the set time of 5 minutes. Hence, the following function, with the above-mentioned parameters, was used:
Nonlinear regression was performed determining unweighted least squares estimates of parameters using the Levenberg-Marquardt method. The goodness of fit of the obtained values of the stair-climbing test was quantified by the determination adjusted coefficient (R2).
Principal Component Analysis
To simultaneously obtain factors that influence the overall individual fitness of our subjects, analysis of the main components (PCA) was performed. This method aims to reduce the complexity of the data by decreasing the number of variables that need to be considered. Principal Component Analysis was performed on the estimators obtained from the nonlinear regression (%HRR0, Δ%HRR/t, A%HRR, ω), age, weight, height, body fat, V[Combining Dot Above]O2max. Principal Component Analysis is an approach to factor analysis that considers the total variance in the data, unlike common factor analysis. Principal Component Analysis generates a matrix that contains the factor loadings of all the variables on all the extracted factors. The factor loadings in PCA are the simple correlation between the factors and the variables. A varimax solution yields results that make it as easy as possible to identify each variable with a single factor. The number of factors was evaluated using the Kaiser criterion (eigenvalues >1) and scree plot. To determine if the variables were correlated highly enough to provide a reasonable basis for PCA, the Bartlett test was also performed. Factor loading under 0.35 is considered low and was not considered. Statistical analyses were performed using SPSS for Windows (version 13.0, Chicago, IL, USA).
In the stair-climbing test, 422 ± 69 steps were climbed, at a rate of 84.4 ± 13.9 steps per minute. The %HRR value of the sample under study was 82.5 ± 9.9. This value is close to the maximum %HRR values, which correspond to a “vigorous-maximum” physical activity level (25). The %HRR as a function of time is represented using function 1; it was proposed as the union of two equations:
- 1) A linear component, that is,
- , represents the linear growth rate as a function of time, starting from %HRR0. Clearly, the higher the value of the intercept (%HRR0), the smaller the range in which the cardiac frequency can fluctuate during the stair-climbing test. In the sample, the intercept has a value of 71.83 ± 11.27. On the contrary, the slope of the line (Δ%HRR/t) quantifies the variation rate over time, that is, how much the slope increases every 5 seconds. The value recorded in the sample was 7.58 ± 4.36% (Figure 4);
- 2) A trigonometric component, that is,
- , represents the fluctuation of the %HRR as a function of time. The A%HRR value shows an average value of 6.48 ± 2.78, whereas ω shows an average value of 0.058 ± 0.019, corresponding to 2.83 complete cycles ± 0.92 cycles, that is, the complete number (climbing up and down a training tower). The proposed function shows a discrete fit with all the experimental data. The multiple adjusted determination coefficient (R2) shows an average value of 0.84 ± 0.14. All scatter plots between variables used for PCA showed a reasonable linear dependence when variables were correlated.
Principal Component Analysis generates a matrix that contains the loading factors of all the variables on all the factors extracted. The loading factors in PCA are the simple correlation between the factors and the variables. A varimax solution yields results that make it as easy as possible to identify each variable with a single factor. The Bartlett test yielded significant results (p < 0.001). This means that the variables are correlated highly enough to provide a reasonable basis for PCA analysis. Both the scree plot and eigenvalues (with value >1) support the conclusion that the initial correlation matrix can be reduced to 4 principal components (PCs), which together explain 78.2% of the data variance. The first component (PC1) accounted for 22.8% of the total variance in the data sets; A%HRR, weight, and height are highly correlated with PC1. The second component (PC2), which accounted for 19.6% of the total variance, included body fat, V[Combining Dot Above]O2max, and weight. The third component (PC3), representing 19.3% of the total variance, is correlated to %HRR0, age, and V[Combining Dot Above]O2max. The fourth component (PC4), accounting for 16.4% of the total variance, comprises Δ%HRR/t, ω, and V[Combining Dot Above]O2max, which are highly correlated with this component. Principal Component Analysis results are reported in Table 2. These results allowed us to highlight the individual characteristics of our sample.
The aim of this study was to identify the main components that determine firefighters' level of physical fitness using a test involving stair climbing, one of the most demanding specific psychophysical tasks executed by firefighters. The data from this test may then be used as a basis to design functional individualized training programs to improve the subjects' performance, reduce the risk of injury, and hence save time, energy, and financial resources. Existing literature has thoroughly dealt with the energetic and psychological costs of the tasks firefighters are frequently required to carry out (6,12). This investigation focuses on the influence of the following variables on the subjects' fitness levels: weight, height, body fat, age, V[Combining Dot Above]O2max, and 4 parameters obtained from nonlinear regression of %HRR time series, followed by PCA. The PCs that influenced the specific performance of each subject were isolated, and the characteristics of the these 4 components are illustrated below.
Principal Component 1 describes the relative workload, which may explain the inverse association of the amplitude of the sinusoidal fluctuation of the %HRR with weight and height. The relative workload is lower for subjects with a larger body mass because the same type of task with the same absolute load has a greater impact on smaller subjects. Wearing protective clothing and carrying SCBA added to the subjects' physical load, which increases cardiovascular strain in 2 ways: by impeding movement and by increasing the total mass of the individual (6). Moreover, during the trial, the subjects carried another additional load of 30 kg; hence, the relative increase in carried weight and the physical exertion are higher for smaller subjects (12). Subjects with a greater weight had fewer sudden jumps in %HRR because they felt the additional weight less during the stair climbing. Nevertheless, their average HR was similar to that of the other subjects because a taller person has a greater body mass and requires more energy to move (12).
During the test, cardiovascular strain, indicated by HR response, was maximal or near maximal, as has been reported in several other studies involving simulated firefighting or related physical activities (30). Such tasks should therefore be performed by persons with a larger body mass because the physiological strain that they experience is less than that of smaller persons carrying the same load (12).
Principal Component 2 describes the effect of body composition. Our results show that V[Combining Dot Above]O2max is inversely associated with body fat and weight. These results are in agreement with other studies (33) in which V[Combining Dot Above]O2max was inversely correlated to the sum of four skinfolds. Nokes (22) reported that body mass index and waist circumference were inversely associated with cardiorespiratory fitness. Excess body fat is also a risk factor for cardiovascular morbidities (4), and there is a strong consensus that firefighting is a physically demanding occupation requiring good cardiovascular fitness (15). Our results showed that the expression of V[Combining Dot Above]O2 peak relative to total mass was significantly reduced for the high-fat groups compared with their matched counterparts, which is consistent with the findings of Louhevaara et al. (18). Other studies (6,19) have shown that individuals with more fat tend to have more difficulty performing certain tasks, especially those requiring weight-bearing activity and cardiorespiratory endurance. Finally, Williford et al. (35) report a strong correlation between the percentage of body fat and stair-climbing ability: Subjects with the highest percentages of body fat performed the worst.
Principal Component 3 describes the role of physiological age. It showed how age is associated negatively with V[Combining Dot Above]O2max. This result is consistent with data in literature for the general population, which show a decline in maximal aerobic power at a rate of around 5 ml·kg−1·min−1 per decade after the age of 30 years in both endurance-trained and untrained individuals (10). Specific studies on firefighters show the same results (30,38). The decline in V[Combining Dot Above]O2max that worsens with age could be mitigated with exercise training (24,25).
Principal Component 4 describes the state of fitness. This last component showed that high V[Combining Dot Above]O2max values are associated with a smaller increase in HRR during the physical exertion and a better performance with a greater number of steps climbed. Indeed, several studies report the importance of V[Combining Dot Above]O2max as a measure of cardiorespiratory endurance (32). V[Combining Dot Above]O2max is a valid index measuring the limits of the cardiorespiratory system's ability to transport oxygen from the air to the tissues at a given level of physical conditioning and oxygen availability (10). In a person with a high maximal aerobic capacity, a given sub maximal workload is likely to cause less cardiovascular strain and a lower HR than it does in a person with lower maximal aerobic capacity (12). The studies analyzed by Barr et al. (4) in a recent review showed that firefighters had a mean aerobic power ranging from 39.6 to 61 ml·kg−1·min−1. If we compare this value with the mean V[Combining Dot Above]O2max of our sample, which was 39.56 ± 6.11 ml·kg−1·min−1, we can observe that our values were equal to the minimum values in the studies reviewed by Barr et al. (5). This is probably because of the high average age of our sample group (44.9 ± 4.7 years). Numerous studies (9,16,34) have sought to determine an adequate level of V[Combining Dot Above]O2max for firefighters because it is an important factor influencing their ability to perform their aerobically demanding jobs safely and effectively. Indeed, the International Association of Firefighters (2000) has recommended that firefighters be able to reach a maximal oxygen uptake of at least 42 ml·kg−1·min−1.
Though it is a simulation, the stair-climbing test comes very close to creating the conditions firefighters face in real situations. In this investigation, we applied nonlinear modeling to HRR time series and PCA to a specific task, which simulates the task that firefighters are called on to perform the most frequently. Hence, the test could be implemented within the context of the normal drills that are routinely performed in firehouses, alternating the crews on the basis of their shifts. Specific individualized training programs could then be designed for firefighters on the basis of the results of the stair-climbing test and the subsequent nonlinear modeling and PCA analysis. Ideally, every firehouse should have an area suitable to house the simple equipment necessary for these individually tailored programs to be implemented as rapidly as possible. The benefits of an ongoing program aiming to improve and maintain the physical efficiency of firefighters could be very useful in terms of reducing sick days, injuries, and cardiovascular risk. Such data could also be used in the selection of new recruits and to reduce risks associated with the increasing average age of firefighters.
The authors are grateful to all the volunteers in the Pesaro-Urbino Fire Department whose training program was interrupted by the tragic earthquake in the Abruzzo Region on April 6, 2009. These firefighters assisted in rescue efforts immediately after the quake hit and continued their work until just a few months ago.
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Keywords:© 2013 National Strength and Conditioning Association
stair-climbing test; heart rate modeling; public safety; heart rate