• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month
Page
1. 1
1
2. 2
2
3. 3
3
4. 4
4
5. 5
5
6. 6
6
7. 7
7

# A comparison of maximal heart rates derived from prediction equations compared to actual laboratory measures for cycling and arm ergometry.

Free essay example:

A comparison of maximal heart rates derived from prediction equations compared to actual laboratory measures for cycling and arm ergometry

Introduction:

For this assignment I will aim to talk about the methods of predicting maximal heart rate that we used, the methods we undertook to get our results in the laboratory and then I will analyse these results and compare to how they should look if the predictive equations are correct.

Heart rate is used as a guide to set exercise intensity because of the relatively linear relationship between heart rate and VO2 max (ACSM, 2006, p.143 and ACSM, 2000; Londeree and Ames, 1976; Gilman, 1996; Zavorsky, 2000, all cited by Santos et al.,2005, p.270). It is best to measure maximal HR (HRmax) during a progressive maximal exercise test whenever possible because HRmax declines with age (ACSM, 2006, p.143). Finding HRmax is important for clinical and health reasons, for example a straight percentage of HRmax is used as a basis for prescribing exercise intensity in both rehabilitation and disease preventionprograms (ACSM, 2000; Fletcher, 1997, both cited by Tanaka et al., 2001, p.153).

Maximal heart rate also is widely used as a criterion for achieving peak exertion in the determination of maximal aerobic capacity (ACSM, 2000; Tanaka et al., 1997; Howley et al., 1995, all cited by Tanaka et al., 2001, p.153).

Therefore we know that calculating maximal heart rate is important in the clinical and rehabilitation settings but its also important when testing subjects but getting accurate is complicated and unlikely unless performed using direct methods of assessment.

For our study we performed maximal exercise tests using arm and cycle ergometers, we done this as the highest HR values are obtained in maximal tests conducted in the laboratory (Santos et al., 2005, p.170) however maximal exercise testing is not feasible in many settings (Tanaka et al., 2001, p.153) and some aspects of testing for HRmax can be quite difficult to control for in the field (Santos et al., 2005, p.170), when this is the case HRmax is often estimated using the age-predicted equation of 220 – age (Tanaka et al., 2001, p.153; Robergs and Landwehr, 2002, p.2).

Although the age-predicted HRmax is the most commonly used as a basis for prescribing exercise programs, as a criterion for achieving maximal exertion and as a clinical guide during diagnostic exercise testing and despite its importance to this field the validity of the HRmax equation has never been established in a sample that included a sufficient number of older adults(Tanaka et al., 2001, p.153).

Method:

For our assignment we performed maximal exercise tests on an arm and cycle ergometer, during these test the investigators measured HR, RPE and power. Pre testing we measured physiological variables such as height, weight and resting heart rate, we also measured blood lactate content and RPE both pre and post-testing. Upon completion of all tests we recorded max RPE, power and HR values. Healthy male (n = 29) and female (n = 13) participants were recruited for this study with an average age for males of 19.82 ± 0.93 years and 20.08 ± 1.89 years for females, other variables tested for are presented in tables 1 and 2.

Table 1: Ages and anthropometrical characteristics of male subjects

 Age (years) Height (m) Weight (kg) Resting Systolic BP (mmHg) Resting Diastolic BP (mmHg) Resting HR (bpm) Mean 19.83 1.78 76.17 129 69 66 SD 0.93 0.08 11.06 9.80 6.63 11.25 Minimum 19 1.59 52 110 59 46 Maximum 21 1.90 96.7 149 86 86

Table 2: Ages and anthropometrical characteristics of female subjects

 Age (years) Height (m) Weight (kg) Resting Systolic BP (mmHg) Resting Diastolic BP (mmHg) Resting HR (bpm) Mean 20.08 1.67 59.52 124 71 77 SD 1.89 0.06 4.19 12.52 5.56 16.19 Minimum 19 1.58 49 104 63 47 Maximum 26 1.77 64.05 152 81 106

The methods of predicting HRmax that we used were 220-age; the

Proposed equation by Tanaka et al., (2006) which is (207 – 0.7 x age) for active individuals and the final equation we used was proposed by Inbar et al., (1994) which is (205.8-(0.685 x HR)). All statistical analysis was performed using SPSS V.14 (SPSS Inc, Illinois, U.S.A) and the significant level adopted was of P < 0.05.

Before running a statistical test I checked for normality of data using a Shapiro-Wilk test. I placed our information about measured HRmax and the predictive equations of HR into a sheet for cycle ergometry and it came out that only measured maxHR was normally distributed but because the other three values are obtained from prediction equations they are possibly open to violation, this may cause type I or II errors; the results are as follows, measured maxHR = W41 = 0.963 P > 0.05; 220-age = W41 = 0.645, P < 0.05; Tanaka et al., = W41 = 0.744, P < 0.05 and finally Inbar et al., = W41 = 0.744, P < 0.05.

The same process was performed for data from arm ergometry values and it followed a similar pattern; measured maxHR = W42 = 0.960, P > 0.05; 220-age = W42 = 0.641, P < 0.05; Tanaka et al., = W42 = 0.739, P < 0.05 and finally Inbar et al., = W42 = 0.739, P < 0.05.

Results:

Statistical analysis was performed and its output showed that for arm ergometry there was a significant difference between HR values and the predictive equations; F1, 41.04 = 77.588, P < 0.05, therefore the null hypothesis can be rejected as there was a significant difference between measured HR and prediction equations.

Post hoc testing was run for arm ergometry and it showed some important findings, it showed measured maxHR was significantly different than the three predictive equations, with mean differences of 24.1 (220-age), 17.1 (Tanaka et al., 2006) and 16.1 (Inbar et al., 1994) bpm respectively. It showed that 220-age was not significantly different than any other value. Tanaka et al., (2006) prediction equation was not significantly different from measured HR or Inbar et al., 1994, however it was significantly different to 220-age with a mean difference of 6.9 bpm and finally the testing showed that Inbar et al., 1994 predictive equation was not significantly different to measured HR but it was significantly different to 220-age and Tanaka et al., 2006, with mean differences of 7.9 and 1 bpm respectively.

Cycling ergometry also followed a similar trend as arm ergometry as the output showed that F1, 40.07= 55.017, P < 0.05, therefore this shows there was also a significant difference in HR values and the predictive equations when exercise was performed on a cycle ergometer.

Post hoc testing was also run for cycle ergometry and this also proved to be important, it showed that measured maxHR was also significantly different than the three predictive equations with mean differences of 15.8 (220-age), 8.9 (Tanaka et al., 2006) and 7.9 (Inbar et al., 1994) bpm respectively. The testing also proved that the prediction equation of 220-age was not significantly different to any other prediction or measured value of maxHR. The predictive equation of Tanaka et al., 2006, was not significantly different from measured maxHR or Inbar et al., 1994, however it was significantly different to 220-age with a mean difference of 6.9 bpm and finally Inbar et al., 1994 showed it was not significantly different from measured maxHR but it was significantly different to 220-age and Tanaka et al., 2006, with mean differences of 7.9 and 1 bpm respectively.

It was also found that mean maxHR values for arm ergometry with a mean of 176.02 bpm (see figure 1) were slightly lower than maxHR values obtained for cycle ergometry with a mean of 184.27 bpm (see figure 2).

Figure 1: Mean max HR values for arm ergometry using

predictive equations and actual measurements

Figure 2: Mean max HR values for cycle ergometry using

predictive equations and actual measurements

Discussion and conclusion:

From the analysis and figures 1 and 2 we can identify that there are significant differences in measured maxHR and prediction equations of maxHR and also differences in maxHR between cycle and arm ergometry. Due to the large differences between our data for measured maxHR and the predictive equations this finding is down to either poor collection of data or inaccurate predictive equations, I believe these two factors contribute equally.

I get this reasoning from the fact that the equation by Inbar et al., 1994, is the most accurate general equation available but the error associated with this is still too large (Robergs and Landwehr, 2002). This is the case for our data as well because the means of measured maxHR and the equation by Inbar et al., 1994, are the closest of the three, with means for cycle ergometry of 184.27 bpm and 192.15 bpm respectively and the means of arm ergometry being 176.02 and 192.17 bpm, these values are still unacceptable though because there is too much variation. There is set criterion that must be met for a test to be passed as being maximal such as a HR within 10 bpm of age predicted values, I believe that the majority of these would not have been passed therefore the subjects may not have worked to their maximal effort which could have affected the reliability of the results.

For the analysis I combined male and females into either arm or cycle ergometry, my reasoning for this is that maxHR is predicted to a large extent by age alone independent of gender and physical activity status (Tanaka et al., 2001, p.155).

This study had some limitations that may affect its reliability and validity. Firstly the data collection was made by groups of at least 5 people and not an individual or a small number of people, this could have affected the testing procedures because people may have administered a test slightly different to another group. Secondly tests were administered over several weeks so it may have been a month between the two tests, this could again effect the results as the subject may have trained or detrained over this period. Thirdly some data was missing from the testing sheets as people had filled them incorrectly so a full data set may not have been obtained, this again could affect mean and SD values of anthropometrical measurements. Fourthly after all data collection was completed each group had to type their own data up from photocopied sheets, this could lead to type I errors by people putting in the wrong values so there may be variability between one groups analysis and another groups. Fifthly the guidelines of a maximal VO2 max test were not followed so the test may not have been truly maximal which is what the test was meant to be to get the most accurate maxHR values as possible.

References:

Tanaka, H., Monahan, K.D. and Seals, D.R. (2001). Age predicted maximal heart rate revisited. Journal of the American College of Cardiology, 37: 153-156.

Robergs, R.A. and Landwehr, R. (2002). Prediction of maximal heart rate. Journal of Exercise Physiology, 5(2); 1-10.

Dos Santos, A.L., Silva, S.C., Farinatti, P.D.T.V. and Monteiro, W.D. (2005). Peak heart rate response in maximum laboratory and field tests. Sociedade Brasileira de Medicina du Esporte, 11(3); 170-173.

American College of Sports Medicine. (2006). ACSM’s guidelines for exercise testing and prescription. USA: Lippincott Williams & Wilkins

This student written piece of work is one of many that can be found in our University Degree Sports Science section.

Not the one? Search for your essay title...
• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month

Close
1. ## A TECHNICAL COMPARISON OF ELITE MALE SOCCER

Word count: 11498

2. ## Fitness Testing

Word count: 1878

3. ## Nutritional analysis case study

Word count: 6602

4. ## How may a maximal velocity shot in soccer lead to ankle impingement syndrome?

Word count: 3529

5. ## Appraisal of the Physiological Underpinnings and Testing Requirement for a Semi-Professional Football Player

Word count: 3841

6. ## Physiological Adaptations to Exercise

Word count: 3532

7. ## Effect of external wrist weights on maximal countermovement vertical jump height in Loughborough University ...

Word count: 2125

8. ## Technical Analysis of elite male soccer players by position and success

Word count: 15510

# Related University Degree Sports Science essays

1. ## Technical Analysis of elite male soccer players by position and success

coach observations to be formed, especially by defining each skill performed as successful or unsuccessful (Franks and Goodman, 1986). If an accurate analysis of the technical attributes of each player position was able to be established then the results could significantly influence team selections and coaching sessions (Bate, 1996).

2. ## How may a maximal velocity shot in soccer lead to ankle impingement syndrome?

The most relevant previous study to examine when observing how a maximal velocity shot in soccer may lead to ankle impingement syndrome is that conducted by Tol et al (2002). The study examined 150 kicking actions performed by 15 professional soccer players, and observed where the ball made contact with

1. ## Nutritional analysis case study

The recommended dietary allowance (RDA) for protein is 52-56 g/day of adult males or 46 g/day for adult females or 0.80g . kg-1. day-1 for adult males (DRI, 2002). According to Burke & Deakin (2006) active athletes playing power sports (such as football), those engaged in muscle-development training, and elite

2. ## Fitness Testing

tests, the specificity of the fitness tests, and it will look at what component of fitness is being used in the fitness test. Client Profile Name: Kevin McDonald Occupation: Footballer This person participates in exercise four times a week. He does on average eleven and a half hours a week exercise.

1. ## A TECHNICAL COMPARISON OF ELITE MALE SOCCER

The technical measure was based on a subjectively drawn continuum, which allows for the analysis of a player's technical performance throughout the game. The final system design was specific to a striker / attacking player. The Chi squared test of independence was used to determine whether differences between the results were statistically significant.

2. ## Effect of external wrist weights on maximal countermovement vertical jump height in Loughborough University ...

The aim of this study is to add to our understanding in this area, by investigating the effect of wrist weights on maximal countermovement vertical jump height. The results from Corwin's investigation lead us to hypothesize that increasing wrist weights will reduce maximal countermovement vertical jump height.

1. ## Physiological Adaptations to Exercise

10 20 70 Tennis 70 20 10 Volleyball 80 5 15 Fig 1. Energy System Recruitment. Source: Fox E. L. et al. (1993) As the above table illustrates, different energy systems are employed at differing times and rates dependant upon the duration and intensity of the exercise.

2. ## Appraisal of the Physiological Underpinnings and Testing Requirement for a Semi-Professional Football Player

170?192. doi: 10.1113/jphysiol.1966.sp007909. Haugen, T., Tonnessen, E., Leirstein, S., Hem, E. and Seiler, S. (2014) ?Not quite so fast: Effect of training at 90% sprint speed on maximal and repeated-sprint ability in soccer players?, Journal of Sports Sciences, 32(20), pp. 1979?1986.

• Over 160,000 pieces
of student written work
• Annotated by
experienced teachers
• Ideas and feedback to
improve your own work