How Reliable Are the Equations for Predicting Maximal Heart Rate Values in Military Personnel?

Transcription

1 MILITARY MEDICINE, 176, 3:347, 2011 How Reliable Are the Equations for Predicting Maximal Heart Rate Values in Military Personnel? Goran Sporis, PhD ; Vlatko Vucetic, PhD ; Igor Jukic, PhD ; Darija Omrcen, Msc ; Daniel Bok, MagEd ; Zrinko Custonja, MagEd ABSTRACT Objectives: The purpose of this study was to evaluate the validity and reliability of equations for predicting maximal values of heart rate (HR) in military personnel. Methods: Five hundred and nine members of the Croatian Armed Forces (age 29.1 ± 5.5 years; height ± 6.6 cm; body mass 83.4 ± 11.3 kg; maximal oxygen uptake [ ] 49.7 ± 6.9 ml O 2 /kg/min) were tested. The graded exercise test with gas exchange measurements was used to determine and maximum HR (HRmax ). The analysis of variance was used to determine the differences between the equations to calculate. Results: The analysis of variance yielded statistically significant differences between seven equations ( p < 0.05). The values from Stevens Creek s ( = 205 [age/2]) and Fox and Haskell s ( = 220 age) equations had the highest correlation with the obtained by the graded exercise test. Conclusion: The authors recommend using the values from the Stevens Creek and the Fox and Haskell equations for the purpose of training, testing, and daily exercise routine in military personnel. INTRODUCTION Heart rate (HR) can be easily and noninvasively measured which makes it widely used to assess cardiovascular responses to exercise and its recovery from stress and to study sympathoadrenal reactivity during cognitive tests. 1 During a graded exercise test (GXT), HR rises progressively and proportionally to the amount of work performed 2 until a maximum value is reached that cannot be surpassed despite subsequent increases in workload. This point is called maximum HR ( ) and is usually regarded as the upper limit of the central cardiovascular system. 3 Aerobic training is important for military personnel because efficiency of their daily routine is dependent on their level of training. This is one of the reasons why aerobic training for military purposes must be organized according to a detailed plan and according to the individual level of the soldier s physical fitness level. The best way of determining the soldier s aerobic capacity is to use the GXT on a motordriven treadmill with simultaneous measuring of the and all other ventilation and metabolic parameters. This procedure is almost impossible to realize with military personnel so that for this purpose the percentage of the is used to program the training intensity of soldiers. Aerobic capacity, also called cardiorespiratory fitness, is mostly tested and measured in military members by using timed run tests, such as the 2-mile or 3,200-m run test. However, planning and programming a quality training on the basis of the results of these tests are difficult because they provide information only about the physical fitness level of soldiers, but they do not supply the necessary parameters for the quality planning and programming intensities and the volume of training. In practice, several tests are used to determine the actual, but they require that the HR monitors be used during the test on the Human Performance Laboratory, Faculty of Kinesiology, University of Zagreb, Horvacanski zavoj 15, Zagreb, Croatia. one hand, and on the other, they demand that the soldiers be asked to perform the task at maximal exertion and are therefore not useful for everyday military practice. Consequently, the prediction equations based on a person s age are frequently used in prescribing exercise intensity and for other clinical applications as well. The question that needs to be answered is how to make a valid and reliable prediction of the and which equation to use. The customary equation for predicting the (220 age in years) is acknowledged to be quite variable. The original equation, on the basis of the data compiled in 1971 by Fox and Haskell, 4 was intended to be only a rough equation based on the apparent decline of the with age observed in those studies. One of the most accurate general prediction equations was presented by Tanaka et al. 5 They published the cross-confirmatory findings from an extensive meta-analysis of available research on prediction equations in 2001 along with their own well-controlled, laboratory-based complementary prospective study. Their research used data from 351 published studies involving 18,712 healthy people along with observations from 514 healthy individuals recruited for their laboratory research. Both approaches were cross-sectional and yielded virtually identical regression equations for ( age), with the age variable alone explaining approximately 80% of the variance in the predicted. Additionally, the use of drugs with negative chronotropic action, especially β-blockers, significantly decreases in young people. 6,7 For the purpose of this study, the authors have tested 7 equations for their prediction efficiency to determine which one is the most appropriate for military personnel. METHODS Experimental Approach to the Problem As for the military personnel, there is a problem of how to determine and how to improve aerobic capacity. For military MILITARY MEDICINE, Vol. 176, March

2 training purposes, the percentage of the is the parameter most frequently used to determine the training intensity. For assessing the proper training intensity, the exact of an individual needs to be identified. For this purpose, the equations are sometimes used, when testing procedures cannot be conducted. However, different equations usually yield different values of the. Therefore, it is of utmost importance to investigate and define the most accurate equation that can be used for assessing the on the population of military personnel. In this study, we have used 7 most frequently used equations for predicting the (Table I ). The values of the assessed using the GXT were used as referent values for determining the reliability of the equations. Subjects To realize the aim of this study, 509 (age 29.1 ± 5.5 years, height ± 6.6 cm, body mass 83.4 ± 11.3 kg, and 49.7 ± 6.9 ml O 2 /kg/min) members of the Croatian Armed Forces were tested ( Table II ). Members of each of the 3 combat military services were included in the research: Infantry, n = 209; Aviation, n = 100; and Navy, n = 200. The Ethics Committee of the Faculty of Kinesiology, University of Zagreb, approved the study. All subjects were submitted to health inspection before the GXT and only the ones with adequate health status and with the physician s permission were allowed to participate in the study. The participants were aware that they could withdraw from the study any time. The subjects were not excluded on the basis of their smoking or alcohol consumptions history or body mass index. When using the equations, the following must be considered. TABLE I. Algorithms for Predicting the Algorithm References 220 = 220 Age Fox and Haskell = (0.711 Age) Londeree and 217 = 217 (0.85 Age) Miller et al = (0.67 Age) Gellish et al = 202 (0.55 Age) Whyte et al = 208 (0.7 Age) Tanaka et al = 205 (Age/2) Stevens Creek 12 TABLE II. General Descriptive Parameters of the Sample N = 509 Mean ± SD Min Max Age (year) 29.1 ± HR at Rest (bpm) 73.6 ± Body Height (cm) ± Body Mass (kg) 83.4 ± RVO (ml O /kg/min) ± max 2 (L O 2 /kg/min) 4.1 ± GXT (bpm) ± None of the equations in use is applicable for every population. Each equation is population specific, and the best way to establish the is always to use an appropriate test. As for military personnel, the best test to determine the actual is the GXT performed on a motor-driven treadmill, which is almost impossible to be done with all army members. When it comes to predicting the, gender differences must also be considered. However, only the male military population has been analyzed in this study. Differences in age, the level of physical fitness, body mass index, smoking, alcohol consumption, cardiovascular diseases, medication, etc., all affect values. However, the participants taking any medications known to affect the HR response to exercise (such as β-blockers, calcium channel blockers, or other heart and antihypertensive medications, stimulants, or antidepressants) or those with a previous medical history of heart disease were excluded. Similarly, the subjects in whom the GXT exhibited an abnormal, sign/symptom-limited response (e.g., angina, ST-segment depression, significant dysrhythmia, or abnormal blood pressure response), leading to an early test endpoint, and positive interpretation, suggesting the presence of ischemic heart disease, were also excluded from the analysis. Procedure Laboratory assessments were done at the Faculty of Kinesiology, University of Zagreb, Croatia. Each soldier was measured by 2 experienced staff members, according to the instructions of the International Biological Program, before the measurement of the. Body mass was assessed to the nearest 0.1 kg using the beam balance scale, and the subjects wore a minimal amount of clothes. Body height was assessed to the nearest 0.1 cm using a portable stadiometer. The stadiometer and the scale were calibrated periodically during the study. In the process of morphological measurement, the soldiers were asked about their training experience, and their medical charts were also taken into account. Experimental Protocols To prevent unnecessary fatigue accumulation, the subjects were asked to restrain from strenuous exercise for 24 hours before the exercise test. After the warm-up and the stretching routine executed in compliance with the subject s habits, the was measured by means of the standard incremental maximal exercise test protocol that was performed on a motordriven treadmill (Run race, Technogym, Italy) with a 1.5% inclination. During the testing period, the air temperature ranged from 21 C to 23 C. The testing was performed during morning hours (between 9:00 a.m. and 1:00 p.m.) in thermoneutral conditions. After 1 minute of the VO 2 measurement at rest (standing position), the starting speed was 3 km/h, with speed increments of 0.5 km/h every 30 s. The subjects walked the first few steps (up to 6 km/h) and continued running from 7 km/h until volitional exhaustion. The expired gas was sampled continuously, and O 2 and CO 2 concentrations in the expired gas were determined by using stable and fast zirconium oxygen 348 MILITARY MEDICINE, Vol. 176, March 2011

3 and nondispersive infrared sensor (NDIR) carbon dioxide analyzers (breath-by-breath gas exchange system Quark b 2, COSMED, Italy), which were calibrated before and following each test using precision reference gases. The system was calibrated before each test using the gases of known concentrations. The HR values were collected continuously during the tests using the telemetric HR monitor (Polar Electro, Kempele, Finland) and stored in the personal computer memory. The expired airflow was measured by means of the digital turbine flow meter (COSMED, Italy), which was calibrated before and following each test using a 3-L syringe at flow rate and volumes in the expected physiological range. The temperature and humidity of the expired gas were measured using a rapidly responding sensor (Quark b 2, COSMED, Italy). End-of-test criteria, for the determination of V. O 2max included 2 of the following: (1) volitional exhaustion, (2) achieving the V. O plateau (the highest values were calculated as arithmetic 2 means of the 2 consecutive highest 30 s values), and (3) HR 90% of the age-predicted maximum. All subjects restrained from any type of exercise for 24 hours before testing. During recovery after the test protocol, the subjects walked at 5 km/h for 2 minutes. The last half of the full stage the subject could sustain (for 30 s) was defined as the subject s maximal speed. Statistical Analyses The data analysis was performed using the Statistical Package for Social Sciences (v18.0, SPSS, Chicago, IL). Descriptive statistics were calculated for all experimental data. In addition, the Kolmogorov Smirnov test of the normality of distribution was carried out on all data before further analysis. Statistical power and effect size were calculated using the G-power software. 13,14 Differences between 7 equations to predict the were determined using the analysis of variance (ANOVA) for repeated measures. The post-hoc Bonferroni test for relevant differences between the means was also used. Pearson product moment correlation coefficients were used to determine the relationship between the predicted and the actual in military personnel. Statistical significance was set at p <0.05. RESULTS The Kolmogorov Smirnov test showed that the data were normally distributed. Statistical power was The effect size for the ANOVA was medium (0.56). The ANOVA showed statistically significant differences between the analyzed equations ( p < 0.05). The highest correlation between the values determined during the GXT was between the Stevens Creek ( = 205 [age/2]) ( r = 0.45) and the Fox and Haskell ( = 220 age) ( r = 0.41) equation. The Miller et al 9 equation had the correlation coefficient of 0.39 with referent values of the determined on GXT. All the correlation coefficients ranged from 0.25 to The highest value of (192.2 bpm) was obtained with the Miller et al 9 equation, and the lowest value of (185.5 bpm) was obtained with the Londeree and Moeschberger 8 equation. Six from 7 equations underestimate the values of. Only the Miller et al 9 equation overestimates the referent value of the determined on GXT (Table III ). The HRmax values determined on GXT had a negative correlation with body mass ( r = 0.19) and a positive correlation with the maximal running speed on treadmill ( r = 0.12). The SD of the assessed by means of the GXT running test was 8.9 and only 2.7 when assessed with the Stevens Creek equation. The Tanaka et al 5 equation ( = 208 [age 0.7]) underestimated the real value assessed in military personnel by 4.2 bpm. The similar underestimated prediction of the in military personnel was obtained by the Gellish et al 10 equation ( = [0.67 age]). The results of the assessed with this equation were 6.3 bpm lower than the referent value (191.8 bpm) obtained by the GXT. The Whyte et al 11 equation ( = 202 [0.55 age]) underestimated the actual by 5.9 bpm. The Londeree and Moeschberger 15 equation underestimated the value by 6.3 bpm. This was the largest deviation from the referent value of the, which was ± 8.9 bpm. The Miller et al 9 equation overestimated the values by 0.3 bpm. DISCUSSION In our study, we have used the values assessed during the GXT performed on a motor-driven treadmill with a 1.5% inclination. Significant differences between equations for the prediction of the are an interesting finding of this study. The equation used by Stevens and Creek 12 had the highest correlation coefficient with the referent value. It should be noted that the SD of the assessed throughout different tests and the equations had ranged very differently. When using the equations for predicting the, specific characteristics of a population have to be considered. An important component of the military experience is physical training, which prepares military personnel for the physical rigors of military service, which develops discipline, and provides a unifying experience. Physical fitness may be assessed in a variety of ways but is typically categorized either as field based or as clinical. Physical fitness is of extreme importance for military personnel. The preparation of military personnel must meet an accelerated schedule, for they may find themselves in TABLE III. Values Mean ± SD Min Max Correlation With GXT GXT (bpm) ± (bpm) ± (bpm) ± (bpm) ± (bpm) ± (bpm) ± (bpm) ± (bpm) ± MILITARY MEDICINE, Vol. 176, March

4 physically challenging, stressful, combat environments within a short time frame. Therefore, the most effective training regimen and accurate assessment of outcomes have garnered increased importance, where equations for the assessment of are very useful. However, the most frequently used equation for the assessment is the Fox and Haskell equation.4 This equation was created to provide approximate values of the only, and its large variability has also been identified namely, it has SD of bpm. 16 The variability of the estimated is mostly due to the subject s age, and it can fall within the range between 35% and 80%. 17,18 The Fox and Haskell 4 equation was derived from 10 studies examining the relationship between age and the exercise. When we consider the results of our study, we can see that the Fox and Haskell 4 equation, HRmax = 220 age, is the second best equation for the prediction in military personnel. The correlation of the Fox and Haskell 4 equation to the referent value (HRmaxGXT) was 0.41, which is only 0.04 lower than correlation HRmaxGXT with the value estimated by means of the Stevens Creek 12 equation. Despite the fact that this equation was criticized by many authors 5,10 for overestimating the value of the in a young population and underestimating it in the elderly population, it is reasonably appropriate for military personnel. According to the results of our study, the Fox and Haskell 4 equation (HRmax = 220 age) can be used as a reliable and valid equation for predicting the in military personnel. One of the reasons why the Fox and Haskell4 equation is suitable for predicting the in military personnel is the sample similarities. In our study, all subjects were men under 49.7 years of age, the same as in the study by Fox and Haskell. 4 It is known that the values of the drop by less than 1 beat per year as people age. 10 The problem of estimating the by means of this equation occurred in many cross-sectional studies where very different samples as regards their age, gender, physical activity, and aerobic testing experience were used. An accurate equation for the estimation in military personnel is almost indispensable because physical training is a job requirement of every soldier. 19 One of the most accurate equations for general population was presented by Tanaka et al. 5 According to our results, the Tanaka et al 5 equation (HRmax = 208 [age 0.7]) underestimates the real value assessed in military personnel by 4.2 bpm. The similar underestimated prediction of the in military personnel was obtained by the Gellish et al 10 equation ( = [0.67 age]). The results of the assessed with this equation were 6.3 bpm lower than the referent value (191.8 bpm) obtained by the GXT. We can conclude that the 2 most accurate equations according to the existing literature and other studies are not suitable for the prediction of the in military personnel. The Whyte et al 11 equation ( = 202 [0.55 age]) underestimates the actual by 5.9 bpm. This was expected because the sample in the Whyte et al study 11 comprised endurance athletes (24 men, 24.0 ± 5.6 years of age). It is well-known that endurance training decreases HR values, 20 and so that this equation is appropriate for endurance athletes aged 24.0 ± 5.6 years; however, it is not suitable for military personnel. This is why this equation can be accurate for the population of endurance athletes, but not for the population of military personnel. Londeree and Moeschberger 15,17 used the data from available studies to derive a univariate generalized population equation for the assessment ( = [0.711 age]) that is very similar to the equation of Tanaka et al. 5 Similarly to the Tanaka et al 5 equation, Londeree and Moeschberger 15 underestimate the value. The application of their equation produces the largest deviation from the referent value of the, this value being ± 8.9 bpm. In comparison to the Tanaka et al 5 and the Londeree and Moeschberger 15 equation, the Miller et al 9 equation overestimates the values by 0.3 bpm. Still, this was the third best equation for predicting among military personnel. For military personnel, it is very important to have the necessary physical fitness level and in particular the cardiorespiratory fitness level. Soldiers have approximately 9 hours of strenuous low to moderate intensity activity per week mostly in uniform. We found the negative correlation between body weight and values. In the last decades, a tendency of increased obesity has been observed with military personnel. 21 The effects of excess body weight raise a variety of concerns relevant to the health and field performance of members of the military. 22 It has been proved that soldiers with larger body mass have poorer results in distance run times, since the law of allometry dictates the running capacity. 23 Sustaining an increased exercise program of physical training is also an essential component of comprehensive weight reduction strategy for overweight personnel. 22 To safely manage the weight or more precisely the ratio of fatfree body mass and body fat, it has been proved that the plan must also cover the optimization of daily calorie intake, which is achieved by altering unwanted eating habits. 22 Therefore, the well-organized and well-planned military training on the basis of soldier s level of physical fitness is crucial for their everyday tasks. According to the results of this study, the best equations for the estimation in military personnel were the Stevens Creek 12 and the Fox and Haskell 4 equations. However, physical training has also been shown to be associated with a high rate of injury, an outcome that erodes the benefits of physical training. To counter negative effects of overtraining, recent efforts to reduce injuries have focused on modifications in the physical training program itself, and the best way to do that is to plan the training program on the basis of the soldier s level of fitness, determined by using either the Stevens Creek 12 equation or the Fox and Haskell 4 equation. ACKNOWLEDGMENTS The authors thank all the members of the Human Performance Laboratory of Faculty of Kinesiology and members of the Croatian Armed Forces. This study was financed by Faculty of Kinesiology, University of Zagreb, and Institute for Research and Development of Defense System, Republic of Croatia, under the project Research of Human Resources and Potential. 350 MILITARY MEDICINE, Vol. 176, March 2011

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