Uma nova equação baseada em idade para predição da frequência cardíaca máxima em corredores aerobiamente treinados

rbce Revista Brasileira de Ciências do Esporte Rev. Bras. Ciênc. Esporte 0101-3289 2179-3255 Colégio Brasileiro de Ciências do Esporte Resumo Esse estudo objetivou gerar uma equação de frequência cardíaca máxima (FCmax) baseada na idade para corredores aerobiamente treinados. Trinta e quatro corredores homens realizaram três testes incrementais em esteira motorizada, com início a 8 km·h-1 e incrementos de 1 km·h-1 a cada um, dois ou, três minutos. A FCmax foi definida como o valor mais alto de frequência cardíaca registrado em cada teste. As análises de post hoc indicaram que a FCmax de cada teste foi significativamente menor que o valor mais elevado de FCmax para cada participante. A FCmax predita pela equação "206 - 0,7 x idade" subestimou a mais alta FCmax em 8,6 batimentos·min-1. Logo, a equação gerada baseada em idade "218 - 0,8 x idade" deveria ser usada para predizer a FCmax em corredores aerobiamente treinados. Introduction Maximal heart rate (HRmax) is one of the most commonly used values in clinical medicine and physiology (Tanaka et al., 2001) where it is used as a criterion for assessing maximal effort during graded exercise testing (Duncan et al., 1997; Howley et al., 1995) and can be used to prescribe appropriate exercise intensity (ACSM, 2006; Robergs and Landwehr, 2002). For these purposes, exercise and fitness professionals often use age-based equations to estimate HRmax (Engels et al., 1998; Sporis et al., 2011; Tanaka et al., 2001). However, several equations are available to estimate this value (Engels et al., 1998; Gellish et al., 2007; Graettinger et al., 1995; Inbar et al., 1994; Jones et al., 1985; Lester et al., 1968; Miller et al., 1993; Schiller et al., 2001; Sheffield et al., 1978; Tanaka et al., 1997; Whaley et al., 1992), including meta-analyses of published equations (Londeree and Moeschberger, 1982; Tanaka et al., 2001) and most of them differ widely in their estimates of HRmax (Londeree and Moeschberger, 1982). Additionally, these equations were derived from different populations and testing protocols, which can also affect the determination of this value. HRmax is a protocol-dependent physiological variable that is expected to be higher in long-duration tests as compared to short-duration tests (Bishop et al., 1998; Roffey et al., 2007). Nevertheless, some age-based HRmax equations were generated using short protocols (Graettinger et al., 1995; Inbar et al., 1994; Schiller et al., 2001; Tanaka et al., 1997), following the suggestion of Buchfuhrer et al. (1983) to bring the subject to the limit of tolerance in 10 ± 2 min. In fact, Buchfuhrer et al. (1983) suggested that short duration protocols were best to obtain the highest maximal oxygen uptake (VO2max) value, but not for attaining the highest HRmax value. This group also reported higher HRmax values in long (~26 min) tests than in short (~11 min) tests. These reports, therefore, suggest that age-based HRmax equations were generated using testing protocols that might not have been the most appropriate for attaining the highest value for HRmax. Additionally, one other shortcoming of the currently available age-based equations is that none of the previous studies derived a predictive equation by using the highest HRmax value obtained from two or more testing protocols, using the same individuals. Such a method could provide higher values for each subject and, consequently, higher values for the derived HRmax equation. The incorrect prediction of HRmax may cause systematic errors in exercise prescriptions (Cleary et al., 2011). When HRmax is overestimated, the prescribed exercise intensity will be greater than needed to improve cardiovascular fitness. On the other hand, the underestimation of HRmax leads to lower stimulus that may not improve aerobic parameters. Thus, reducing the error associated with estimating HRmax would improve the accuracy of exercise prescriptions (Cleary et al., 2011), and improved estimates require the development and use of new equations designed for specific populations and modes of exercise (Robergs and Landwehr, 2002). Thus, the purpose of this study was threefold: (a) to compare three age-based HRmax equations derived for the same participants from each incremental test of different duration, (b) to derive an age-based HRmax equation for recreational endurance-trained runners from the highest HRmax value, for each participant, attained from the three incremental tests, and (c) to compare the generated equations with the well-known "206 - 0.7 × age" equation proposed by Tanaka et al. (2001) for recreational endurance-trained individuals. We hypothesized that the "206 - 0.7 × age" equation would underestimate the highest HRmax values obtained by the current protocol for recreational endurance-trained runners. Materials and methods Participants Thirty-four male, recreational, endurance-trained runners of regional and local level with a minimum of two years of training experience volunteered to take part in this study. The 10-km running times of the participants were between 35 and 60 min, with a pace between 10 and 17 km h-1 (~45–75% of the world record). Prior to testing, all participants provided written informed consent and the local ethics committee approved the experimental protocol (#719/2010). Experimental overview In a counterbalanced order, participants who were habituated to running tests performed three continuous incremental exercise tests of different stage durations on a motorized treadmill (Super ATL; Inbrasport, Porto Alegre, Brazil), with the gradient set at 1%. The tests were performed over two weeks, with each test separated from the other by at least 48 h. The three different stage duration protocols were as follows: (a) short stage duration of 1 min (SS), (b) intermediate stage duration of 2 min (IS), and (c) long stage duration of 3 min (LS). Exercise protocols After a warm-up that consisted of walking at 6 km h-1 for 3 min, each protocol started with an initial speed of 8 km h-1, followed by an increase of 1 km h-1 between each successive stage until volitional exhaustion. The speed was increased every 1 min for the SS test, every 2 min for the IS test, and every 3 min for the LS test. Consistently across each trial, participants were strongly encouraged, verbally to invest maximum effort. All the three tests were performed at the same time of the day, under normal laboratory conditions (temperature = 20–22 °C and relative humidity = 50–60%). Participants were instructed to report for testing well-rested, nourished, and hydrated, wearing lightweight comfortable clothing. Participants were also instructed to avoid eating 2 h before the maximal exercise tests, to abstain from caffeine and alcohol, and to refrain from strenuous exercise for 24 h before testing. Physiological variables Before testing, participants were familiarized with the 6–20 scale by Borg (1982), which was used to measure the rating of perceived exertion (RPE) during the last 15 s of each stage and at exhaustion. The highest RPE value was adopted as the peak RPE (RPEpeak). Heart rate (HR) was recorded every five seconds throughout the tests (Polar RS800sd, Kempele, Finland) and HRmax was defined as the highest HR value recorded during the test (Bentley and McNaughton, 2003; Schiller et al., 2001; Tanaka et al., 2001). Neither respiratory gases nor blood lactate were monitored during the tests concerning that such interventions would affect the performance of the participants (Schabort et al., 1998). Earlobe capillary blood samples (25 µL) was collected into a glass tube at the end of the tests and at the third, fifth, and seventh minute of passive recovery, sitting in a comfortable chair. From these samples, blood lactate concentration was subsequently determined by electroenzymatic methods using an automated analyzer (YSI 2300 STAT, Ohio, USA). Peak blood lactate concentration (LApeak) was defined for each participant as the highest post-exercise blood lactate concentration value. The maximal effort was deemed to be achieved if the incremental test met two of the following criteria: 1) LApeak higher than 8 mmol L-1, 2) HRmax within ±10 beats min-1 of endurance-trained age-predicted HRmax using the age-based "206 - 0.7 × age" equation (Tanaka et al., 2001) and 3) RPEpeak greater than 18 in the 6–20 Borg scale (Howley et al., 1995). Statistical analyses Data are presented as mean ± SD and were analyzed using the SPSS 17.0 software. The Shapiro–Wilk test was used to check the normality of the data distribution. HRmax values were compared using repeated measures ANOVA with Bonferroni post hoc test. The sphericity assumption was checked by Mauchly's test of sphericity. A simple linear regression was employed to determine the age-based HRmax equation from the SS (HRmaxSS), IS (HRmaxIS) and LS (HRmaxLS) tests. Additionally, an age-based HRmax equation was derived from the highest HRmax value attained for each participant from the SS, IS, and LS tests (HRmaxSIL). The relationship between HRmax and age was examined using the Pearson's correlation coefficient (r), the coefficient of determination (R 2) and the standard error of estimate (SEE). Statistical significance was set at p < 0.05. Results The characteristics of the participants (mean ± SD) are presented in Table 1. Table 1 Characteristics of the participants and training. Variables Mean ± SD Age (years) 40.1 ± 12.8 Body mass (kg) 67.7 ± 7.2 Height (cm) 173.3 ± 7.0 Body mass index (kg m-2) 22.5 ± 1.8 Training frequency (days wk-1) 4.9 ± 1.5 Training distance (km wk-1) 58.5 ± 31.7 n = 30. Four participants did not meet at least two criteria of maximal effort in at least one test. The tests from these participants were not included in the results. The summary of the physiological parameters of the remaining thirty participants (mean ± SD) is given in Table 2. Analysis of variance revealed a significant effect of the stage duration on the LApeak (p = 0.001), percentage of endurance-trained age-predicted HRmax (p = 0.003), time to exhaustion (p < 0.001) and HRmax (p = 0.003). The RPEpeak did not significantly differ among the three protocols (p = 0.36). Post hoc analyses indicated that the HRmaxSIL was significantly higher than the HRmax derived from the SS, IS or LS tests (p < 0.001). Table 2 Values of maximum rating of perceived exertion (RPEpeak), peak blood lactate concentration (LApeak), percentage of age-predicted maximum heart rate (%APMHR), and maximal heart rate (HRmax) during treadmill incremental protocols with stage length of 1 min (SS), 2 min (IS) and 3 min (LS). Protocol RPEpeak (6-20 Borg scale) LApeak (mmol L-1) %APMHR (%) Time to exhaustion (min) HRmax (beats min-1) SS 19.8 ± 0.6 9.4 ± 2.2 102.3 ± 4.0 11.1 ± 1.6 181.8 ± 12.1 IS 19.8 ± 0.4 9.2 ± 1.9 104.0 ± 4.3a 19.4 ± 3.0a 184.8 ± 12.7a LS 19.9 ± 0.4 7.9 ± 2.2a 103.0 ± 4.1a 26.7 ± 4.7a,b 183.1 ± 12.9 SIL 20.0 ± 0.0 10.3 ± 2.0a,b,c 104.8 ± 4.1a,b,c - 186.3 ± 12.7a,b,c Values are means ± SD, n = 30. SIL, highest values attained for each participant from the SS, IS and LS protocols. a p < 0.05 compared with the SS protocol. b p < 0.05 compared with the IS protocol. c p < 0.05 compared with the LS protocol. Fig. 1 presents the measured HRmax (i.e., the highest HRmax values attained for each participant from the SS, IS, and LS tests) and the age-based HRmax equations from the SS (HRmaxSS), IS (HRmaxIS) and LS (HRmaxLS) tests. Further, it presents the proposed HRmaxSIL equation from the measured HRmax and the "206 - 0.7 × age" meta-analysis equation proposed by Tanaka et al. (2001). In developing the HRmaxSIL equation from the three protocols, the IS protocol contributed with 56.7% of the HRmax values. The LS and SS protocols contributed with 33.3% and 10.0%, respectively. The correlation coefficient, coefficient of determination and standard error of estimate of the generated "218 – 0.8 × age" equation were r = -0.81; R 2 = 0.66, and SEE = 7.5 beats min-1. The relative SEE (i.e., SEE expressed as a percentage of the mean of the outcome measure) was 4.1%. The HRmax predicted by the "206 - 0.7 × age" equation was 177.7 ± 9.2 beats min-1 and, on average, according to repeated measures ANOVA, underestimated the measured HRmax by 8.6 beats min-1 in the present population. Figure 1 Comparison between age-based maximal heart rate equations derived from incremental protocols with stage length of 1 min (HRmaxSS), 2 min (HRmaxIS) and 3 min (HRmaxLS). It also presents the proposed HRmaxSIL equation from the highest maximal heart rate value attained for each participant from the three tests of different stage duration and the meta-analysis equation "206 - 0.7 × age" for endurance-trained individuals. Discussion The main purpose of the present study was to derive an age-based HRmax equation for recreational endurance-trained runners. The main finding was that the generated "218 - 0.8 × age" equation should be used to predict HRmax in endurance-trained runners rather than the well-known "206 - 0.7 × age" meta-analysis equation by Tanaka et al. (2001). In fact, the "206 - 0.7 × age" equation, on average, underestimated the highest HRmax values by 8.6 beats min-1. The incorrect prediction of HRmax may cause systematic errors in exercise prescriptions (Tanaka et al., 2001). When the HRmax is overestimated, the prescribed exercise intensity will be greater than needed to improve cardiovascular ?tness. On the other hand, the underestimation of HRmax leads to lower stimulus that possibly will not improve aerobic parameters. Reducing the error of estimating HRmax would improve the accuracy of exercise prescription (Cleary et al., 2011). Consequently, several studies have been conducted to generate new age-based HRmax equations to estimate the HRmax more accurately (Engels et al., 1998; Gellish et al., 2007; Graettinger et al., 1995; Inbar et al., 1994; Jones et al., 1985; Lester et al., 1968; Londeree and Moeschberger, 1982; Miller et al., 1993; Schiller et al., 2001; Sheffield et al., 1978; Tanaka et al., 1997, 2001; Whaley et al., 1992). Nevertheless, most of them are too general or unspecific for the population of endurance-trained runners. Tanaka et al. (2001) presented one of the most usual equations to predict HRmax (Cleary et al., 2011; Franckowiak et al., 2011). They proposed the general age-based "208 - 0.7 × age" equation, independent of gender and habitual physical activity status, which was generated by a meta-analytic study that included 351 studies involving 18.712 individuals. This general equation is valid for heterogeneous groups within a wide age range including children and adolescents (Machado and Denadai, 2011). They also proposed the equation "206 - 0.7 × age" for the specific population of endurance-trained individuals. Nevertheless, in contrast to some studies (Camarda et al., 2008; Cleary et al., 2011; Silva et al., 2007), we found that the specific "206 - 0.7 × age" equation underestimated the actual HRmax in endurance-trained runners. Camarda et al. (2008), for example, analyzed the data from 2047 sedentary individuals (age, 12–69 years), and reported that the HRmax obtained by the "208 - 0.7 × age" equation (182.7 ± 8.2 beats min-1) overestimated the measured HRmax (180.8 ± 13.8 beats min-1). Similarly, Cleary et al. (2011) evaluated 96 active subjects ranging from 18 to 33 years old. They found that the general "208 - 0.7 × age" equation overestimated the measured HRmax (192.6 ± 1.9 and 190.1 ± 7.9 beats min-1, respectively). Silva et al. (2007) assessed 93 elderly women (age, 67 ± 5 years) during the modified Bruce protocol and found that the measured HRmax (145.5 ± 12.5 beats min-1) was highly overestimated by the general "208 - 0.7 × age" equation (161.0 ± 3.9 beats min-1). On the other hand, we found that the specific "206 - 0.7 × age" equation for endurance-trained runners underestimated the actual HRmax for the sample of this study. Supporting our results, Sporis et al. (2011), assessed 509 members of the Croatian Armed Forces (age, 29.1 ± 5.5 years) and concluded that the general "208 - 0.7 × age" equation underestimates the actual HRmax value by 4.2 beats min-1 in military personnel. Similarly, Nes et al. (2013) examined the relationship between HRmax and age in 3320 healthy individuals within a wide age range (19–89 years) and reported that the "208 - 0.7 × age" equation underestimated the measured HRmax by 6.2 beats min-1 even using a individualized protocol that brought the subjects to exhaustion within 8–12 min. An important factor to develop an accurate age-based HRmax equation for a specific population is the homogeneity of the sample. This study involved 34 recreational endurance-trained runners. The small number of participants was the main limitation of this study. However, the difficulty in obtaining a homogeneous group of participants willing to perform three running tests in laboratory until exhaustion was a factor that restricted the sample size. In contrast, the sample of this study ranged widely in age, contributing to the generation of the equation. Another factor that influences the accuracy of a generated age-based HRmax equation is the duration of the incremental test. Roffey et al. (2007) showed that the HRmax was significantly higher during long (25 ± 4 min) incremental tests than it was in shorter tests (10 ± 2 min). Moreover, Bishop et al. (1998) found significantly higher values for HRmax when a stage duration of 3 min was used, rather than 1 min, during incremental tests. Thus, the design of the incremental tests might have affected most of the available age-based HRmax equations. In this study, the age-based equation generated from IS protocol was the most similar to the HRmaxSIL equation (Fig. 1) and contributed with about 60% of the data for its generation. Additionally, not only the HRmaxSIL but also all the three equations generated from just one test (i.e., HRmaxSS, HRmaxIS and HRmaxLS) presented higher values for HRmax than the "206 - 0.7 × age" equation. Further, the "206 - 0.7 × age" equation, on average, underestimated the measured HRmax values by 8.6 beats min-1 in the recreational endurance-trained population. Thus, the proposed "218 - 0.8 × age" equation, generated from three incremental tests of different durations, seems to be more appropriate for predicting HRmax in this population of runners. The majority of age-based univariate equation for predicting HRmax have large errors between 7 and 11 beats min-1 (Robergs and Landwehr, 2002). Further, according to these authors, equations need to be developed that are population specific. The error of the proposed "218 - 0.8 × age" equation for the specific population of recreational endurance-trained runners was relatively low, and in accordance with previous studies. Nes et al. (2013), for example, proposed recently the "211 - 0.64 × age" equation to predict the HRmax in healthy individuals and reported a higher, but also in accordance with previous studies, error (i.e., SEE = 10.8 beats min-1; r = -0.60). Nevertheless, due to the inherent error associated with the estimation of HRmax, using the proposed "218 - 0.8 × age" equation, approximately 67% of the population should fall within ±8 beats min-1 of the actual HRmax. Further, for 5% of the population, the actual HRmax could differ by more than 15 beats min-1. Therefore, direct measurements of HRmax should be used whenever possible. Although this study adds important value to the literature as well as present great practical application, it also has a limitation. Due to the specific characteristics of the sample (recreational endurance-trained runners), our sample size is small for this type of study. However, as mentioned, the equation was built to provide a great practical application for this specific population. Conclusion In summary, the results from the present study suggest that the proposed "218 - 0.8 × age" equation should be used to predict HRmax in recreational endurance-trained runners rather than the well-known "206 - 0.7 × age" equation proposed by Tanaka et al. (2001). Additionally, the "206 - 0.7 × age" equation, on average, underestimated the measured HRmax values by 8.6 beats min-1 in the recreational endurance-trained population. Researchers, coaches, and practitioners are recommended to use this formula to determine HRmax more accurately in endurance-trained runners. Because the results of this study are limited to this specific population, further research is warranted to verify whether these results apply to other populations that have different performance levels. Funding This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES/Brazil and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq/Brazil). Ethical statement Comite de Ética e Pesquisa em Seres Humanos, Universidade Estadual de Maringá (#1.262.502/2015). References ACSM, 2006 ACSM, 2006 American College of Sports Medicine - ACSM. ACSM's guidelines for exercise testing and prescription. Philadelphia, PA: Lippincott Williams & Wilkins; 2006. American College of Sports Medicine – ACSM ACSM's guidelines for exercise testing and prescription 2006 Lippincott Williams & Wilkins Philadelphia, PA Bentley and McNaughton, 2003 Bentley and McNaughton, 2003 Bentley DJ, McNaughton LR. Comparison of Wpeak VO2peak and the ventilation threshold from two different incremental exercise tests: relationship to endurance performance. J Sci Med Sport. 2003;6:422-35. Bentley D.J. McNaughton L.R. Comparison of Wpeak VO2peak and the ventilation threshold from two different incremental exercise tests: relationship to endurance performance J Sci Med Sport 6 2003 422 435 Bishop et al., 1998 Bishop et al., 1998 Bishop D, Jenkins DG, Mackinnon LT. The effect of stage duration on the calculation of peak VO2 during cycle ergometry. J Sci Med Sport. 1998;1:171-8. Bishop D. Jenkins D.G. Mackinnon L.T. The effect of stage duration on the calculation of peak VO2 during cycle ergometry J Sci Med Sport 1 1998 171 178 Borg, 1982 Borg, 1982 Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc. 1982;14:377-81. Borg G.A. Psychophysical bases of perceived exertion Med Sci Sports Exerc 14 1982 377 381 Buchfuhrer et al., 1983 Buchfuhrer et al., 1983 Buchfuhrer MJ, Hansen JE, Robinson TE, Sue DY, Wasserman K, Whipp BJ. Optimizing the exercise protocol for cardiopulmonary assessment. J Appl Physiol. 1983;55:1558-64. Buchfuhrer M.J. Hansen J.E. Robinson T.E. Sue D.Y. Wasserman K. Whipp B.J. Optimizing the exercise protocol for cardiopulmonary assessment J Appl Physiol 55 1983 1558 1564 Camarda et al., 2008 Camarda et al., 2008 Camarda SR, Tebexreni AS, Páfaro CN, Sasai FB, Tambeiro VL, Juliano Y, et al. Comparison of maximal heart rate using the prediction equations proposed by Karvonen and Tanaka. Arq Bras Cardiol. 2008;91:311-4. Camarda S.R. Tebexreni A.S. Páfaro C.N. Sasai F.B. Tambeiro V.L. Juliano Y. Comparison of maximal heart rate using the prediction equations proposed by Karvonen and Tanaka Arq Bras Cardiol 91 2008 311 314 Cleary et al., 2011 Cleary et al., 2011 Cleary MA, Hetzler RK, Wages JJ, Lentz MA, Stickley CD, Kimura IF. Comparisons of age-predicted maximum heart rate equations in college-aged subjects. J Strength Cond Res. 2011;25:2591-7. Cleary M.A. Hetzler R.K. Wages J.J. Lentz M.A. Stickley C.D. Kimura I.F. Comparisons of age-predicted maximum heart rate equations in college-aged subjects J Strength Cond Res 25 2011 2591 2597 Duncan et al., 1997 Duncan et al., 1997 Duncan GE, Howley ET, Johnson BN. Applicability of VO2max criteria: discontinuous versus continuous protocols. Med Sci Sports Exerc. 1997;29:273-8. Duncan G.E. Howley E.T. Johnson B.N. Applicability of VO2max criteria: discontinuous versus continuous protocols Med Sci Sports Exerc 29 1997 273 278 Engels et al., 1998 Engels et al., 1998 Engels HJ, Zhu W, Moffatt RJ. An empirical evaluation of the prediction of maximal heart-rate. Res Q Exerc Sport. 1998;69:94-8. Engels H.J. Zhu W. Moffatt R.J. An empirical evaluation of the prediction of maximal heart-rate Res Q Exerc Sport 69 1998 94 98 Franckowiak et al., 2011 Franckowiak et al., 2011 Franckowiak SC, Dobrosielski DA, Reilley SM, Walston JD, Andersen RE. Maximal heart rate prediction in adults that are overweight or obese. J Strength Cond Res. 2011;25:1407-12. Franckowiak S.C. Dobrosielski D.A. Reilley S.M. Walston J.D. Andersen R.E. Maximal heart rate prediction in adults that are overweight or obese J Strength Cond Res 25 2011 1407 1412 Gellish et al., 2007 Gellish et al., 2007 Gellish RL, Goslin BR, Olson RE, McDonald A, Russi GD, Moudgil VK. Longitudinal modeling of the relationship between age and maximal heart rate. Med Sci Sports Exerc. 2007;39:822-9. Gellish R.L. Goslin B.R. Olson R.E. McDonald A. Russi G.D. Moudgil V.K. Longitudinal modeling of the relationship between age and maximal heart rate Med Sci Sports Exerc 39 2007 822 829 Graettinger et al., 1995 Graettinger et al., 1995 Graettinger WF, Smith DHG, Neutel JM, Myers J, Froelicher VF, Weber M. Relationship of left ventricular structure to maximal heart rate during exercise. Chest. 1995;107:341-5. Graettinger W.F. Smith D.H.G. Neutel J.M. Myers J. Froelicher V.F. Weber M. Relationship of left ventricular structure to maximal heart rate during exercise Chest 107 1995 341 345 Howley et al., 1995 Howley et al., 1995 Howley ET, Bassett DR, Welch HG. Criteria for maximal oxygen uptake: review and commentary. Med Sci Sports Exerc. 1995;27:1292-301. Howley E.T. Bassett D.R. Jr. Welch H.G. Criteria for maximal oxygen uptake: review and commentary Med Sci Sports Exerc 27 1995 1292 1301 Inbar et al., 1994 Inbar et al., 1994 Inbar O, Oten A, Scheinowitz M, Rotstein A, Dlin R, Casaburi R. Normal cardiopulmonary responses during incremental exercise in 20-70-yr-old men. Med Sci Sport Exerc. 1994;26:538-46. Inbar O. Oten A. Scheinowitz M. Rotstein A. Dlin R. Casaburi R. Normal cardiopulmonary responses during incremental exercise in 20-70-yr-old men Med Sci Sport Exerc 26 1994 538 546 Jones et al., 1985 Jones et al., 1985 Jones NL, Makrides L, Hitchcock C, Chypchar T, McCartney N. Normal standards for an incremental progressive cycle ergometer test. Am Rev Respir Dis. 1985;131:700-8. Jones N.L. Makrides L. Hitchcock C. Chypchar T. McCartney N. Normal standards for an incremental progressive cycle ergometer test Am Rev Respir Dis 131 1985 700 708 Lester et al., 1968 Lester et al., 1968 Lester M, Sheffield LT, Trammel P, Reeves TJ. The effect of age and athletic training on the maximal heart rate during muscular exercise. Am Heart J. 1968;76:370-6. Lester M. Sheffield L.T. Trammel P. Reeves T.J. The effect of age and athletic training on the maximal heart rate during muscular exercise Am Heart J 76 1968 370 376 Londeree and Moeschberger, 1982 Londeree and Moeschberger, 1982 Londeree BR, Moeschberger ML. Effect of age and other factors on maximal heart rate. Res Q Exerc Sport. 1982;53:297-304. Londeree B.R. Moeschberger M.L. Effect of age and other factors on maximal heart rate Res Q Exerc Sport 53 1982 297 304 Machado and Denadai, 2011 Machado and Denadai, 2011 Machado FA, Denadai BS. Validity of maximum heart rate prediction equations for children and adolescents. Arq Bras Cardiol. 2011;97:136-40. Machado F.A. Denadai B.S. Validity of maximum heart rate prediction equations for children and adolescents Arq Bras Cardiol 97 2011 136 140 Miller et al., 1993 Miller et al., 1993 Miller WC, Wallace JP, Eggert KE. Predicting max HR and the HR-VO2 relationship for exercise prescription in obesity. Med Sci Sports Exerc. 1993;25:1077-81. Miller W.C. Wallace J.P. Eggert K.E. Predicting max HR and the HR-VO2 relationship for exercise prescription in obesity Med Sci Sports Exerc 25 1993 1077 1081 Nes et al., 2013 Nes et al., 2013 Nes BM, Janszky I, Wisløff U, Støylen A, Karlsen T. Age-predicted maximal heart rate in healthy subjects: the HUNT Fitness Study. Scand J Med Sci Sports. 2013;23:697-704. Nes B.M. Janszky I. Wisløff U. Støylen A. Karlsen T. Age-predicted maximal heart rate in healthy subjects: the HUNT Fitness Study Scand J Med Sci Sports 23 2013 697 704 Robergs and Landwehr, 2002 Robergs and Landwehr, 2002 Robergs RA, Landwehr R. The surprising history of the "HRmax=220-age" equation. J Exerc Physiol Online. 2002;5:1-10. Robergs R.A. Landwehr R. The surprising history of the "HRmax=220-age" equation J Exerc Physiol Online 5 2002 1 10 Roffey et al., 2007 Roffey et al., 2007 Roffey DM, Byrne NM, Hills AP. Effect of stage duration on physiological variables commonly used to determine maximum aerobic performance during cycle ergometry. J Sports Sci. 2007;25:1325-35. Roffey D.M. Byrne N.M. Hills A.P. Effect of stage duration on physiological variables commonly used to determine maximum aerobic performance during cycle ergometry J Sports Sci 25 2007 1325 1335 Schabort et al., 1998 Schabort et al., 1998 Schabort EJ, Hopkins WG, Hawley JA. Reproducibility of self-paced treadmill performance of trained endurance runners. Int J Sports Med. 1998;19:48-51. Schabort E.J. Hopkins W.G. Hawley J.A. Reproducibility of self-paced treadmill performance of trained endurance runners Int J Sports Med 19 1998 48 51 Schiller et al., 2001 Schiller et al., 2001 Schiller BC, Casas YG, Desouza CA, Seals DR. Maximal aerobic capacity across age in healthy Hispanic and Caucasian women. J Appl Physiol. 2001;91:1048-54. Schiller B.C. Casas Y.G. Desouza C.A. Seals D.R. Maximal aerobic capacity across age in healthy Hispanic and Caucasian women J Appl Physiol 91 2001 1048 1054 Sheffield et al., 1978 Sheffield et al., 1978 Sheffield LT, Maloof JA, Sawyer JA, Roitman D. Maximal heart rate and treadmill performance of healthy women in relation to age. Circulation. 1978;57:79-84. Sheffield L.T. Maloof J.A. Sawyer J.A. Roitman D. Maximal heart rate and treadmill performance of healthy women in relation to age Circulation 57 1978 79 84 Silva et al., 2007 Silva et al., 2007 Silva VA, Bottaro M, Justino MA, Ribeiro MM, Lima RM, Oliveira RJ. Maximum heart rate in Brazilian elderly women: comparing measured and predicted values. Arq Bras Cardiol. 2007;88:314-20. Silva V.A. Bottaro M. Justino M.A. Ribeiro M.M. Lima R.M. Oliveira R.J. Maximum heart rate in Brazilian elderly women: comparing measured and predicted values Arq Bras Cardiol 88 2007 314 320 Sporis et al., 2011 Sporis et al., 2011 Sporis G, Vucetic V, Jukic I, Omrcen D, Bok D, Custonja Z. How reliable are the equations for predicting maximal heart rate values in military personnel?. Military Med. 2011;176:347-51. Sporis G. Vucetic V. Jukic I. Omrcen D. Bok D. Custonja Z. How reliable are the equations for predicting maximal heart rate values in military personnel? Military Med 176 2011 347 351 Tanaka et al., 2001 Tanaka et al., 2001 Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol. 2001;37:153-6. Tanaka H. Monahan K.D. Seals D.R. Age-predicted maximal heart rate revisited J Am Coll Cardiol 37 2001 153 156 Tanaka et al., 1997 Tanaka et al., 1997 Tanaka H, DeSouza CA, Jones PP, Stevenson ET, Davy KP, Seals DR. Greater rate of decline in maximal aerobic capacity with age in physically active vs. sedentary healthy women. J Appl Physiol. 1997;83:1947-53. Tanaka H. DeSouza C.A. Jones P.P. Stevenson E.T. Davy K.P. Seals D.R. Greater rate of decline in maximal aerobic capacity with age in physically active vs. sedentary healthy women J Appl Physiol 83 1997 1947 1953 Whaley et al., 1992 Whaley et al., 1992 Whaley MW, Kaminsky LA, Dwyer GB, Getchell LH, Norton JA. Predictors of over – and underachievement of age – predicted maximal heart rate. Med Sci Sports Exerc. 1992;24:1173-9. Whaley M.W. Kaminsky L.A. Dwyer G.B. Getchell L.H. Norton J.A. Predictors of over – and underachievement of age – predicted maximal heart rate Med Sci Sports Exerc 24 1992 1173 1179