SAINT XAVIER UNIVERSITY

School of Nursing and Health Sciences

EXSC 275, Exercise Physiology for Sport (4)

Fall 2022, Lecture: TTh 12:30p – 1:50p

Labs: M 9:00a – 11:00a

W 9:00a – 11:00a

Name: ___________________________________

Date: _______________

Lab #4, Anaerobic Power Tests

Introduction and Purpose

The anaerobic component of performance has been one of the most difficult to objectively

quantify. Part of this difficulty stems from a basic lack of agreement among sport scientists as to

how this component is defined. In fact, the use of different terminology to represent the same

basic concepts has greatly confused the issue. The terms anaerobic power, anaerobic capacity,

and anaerobic threshold have all been used interchangeably to denote the same physiological

concept by some scientists and have been used independently to describe other physiological

events by others. By way of introduction, the following definitions will be used in this lab:

Anaerobic power is defined as the peak power output attained in a test of short duration, usually

lasting less than/equal to 30 sec. Anaerobic capacity is defined as the maximal work performed

over a period of 30 seconds to 2 minutes. Anaerobic threshold is defined as being identical in

concept to the lactate threshold, that rate of work, or that percentage of VO2max, corresponding to

the initial increase in blood lactate above resting levels.

The ability to jump, sprint, put the shot, throw the javelin, or perform fast starts as would be

required of running backs in football or sprinters are a few examples of athletes converting energy

to power. The ability to develop considerable power is of prime importance when predicting

athletic success. Power is performance of work expressed per unit of time. The term anaerobic

power has been associated with anaerobic metabolism and the tests that measure it. The

development of power is related to muscular strength and especially to the amount and rate of

utilization of the stored phosphagens. Therefore, the tests that follow reflect the ability to employ

the immediate energy system.

Aerobic power during exercise is relatively simply and quite accurately assessed by measuring the

maximal oxygen uptake. A similar tool for the direct measure of anaerobic power is lacking, so

various indirect method shave been developed to study the kinetics, power, and capacity of the

non-oxidative processes. Performance tests that apparently cause maximal activation of the ATPCP system have been developed to provide practical “field tests “to evaluate the capacity of

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immediate energy transfer. These tests are generally referred to as power tests where power is

determined using the following formula:

POWER = F X D /Time

where:

F = force produced (generated)

D = distance through which force is moved or applied

T = time or duration of the work period

Depending on the units of measurement, power can be expressed in terms of foot-pounds (ft-lbs)

per sec or per minute, kilogram-meters (kg/m) per second or per minute, kcals per second or per

minute (kcal/sec or kcal/min), or in terms of watts (W) or horsepower (hp).

The purpose of this lab is to perform assorted field power tests to evaluate the immediate energy

system.

Objectives

•

The objective of the Margaria Kalamen Power Testis is to monitor the development of

the athlete’s strength and speed (power).

•

This objective of the vertical jump test is to measure lower limb explosive power by

measuring the height a client can jump.

Equipment

•

Flight of 12 stairs

•

Stopwatch

•

Jump mat

•

Vertec

Procedures

Margaria Power Test

1. The participant warms up for 10 minutes.

2. Your lab partner marks a starting line with cones 6 meters in front of the first step.

3. Your lab partner places a cone on and to one side of the 3rd, 6th, and 9th steps.

4. Your lab partner measures the vertical distance from the 3rd to the 9th step (in m).

5. Your lab partner weighs the participant (in kg).

6. The participant starts at the 6-meter line.

7. Your lab partner gives the command “GO.”

8. The participant sprints to the steps and up the flight of steps taking three steps at a time

landing on the 3rd, 6th, and 9th steps.

9. Your lab partner starts the stopwatch when the athlete’s foot lands on the 3rd step.

10. Your lab partner stops the stopwatch when the athlete’s foot lands on the 9th step and

records the time.

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Vertical Jump Test

The participant stands side on to a wall and reaches up with the hand closest to the wall. Keeping

the feet flat on the ground, the point of the fingertips is marked or recorded. This is called the

standing reach. The person puts chalk on their fingertips to mark the wall at the height of their

jump. The person then stands away from the wall and jumps vertically as high as possible using

both arms and legs to assist in projecting the body upwards. Attempt to touch the wall at the

highest point of the jump. The difference in distance between the standing reach height and the

jump height is the score. The best of three attempts is recorded.

Evidence

Margaria Power Test

The disadvantages of the test include the ability to successfully conduct this test requires the

availability of steps of the appropriate height and with a clear run up area. The accuracy of this

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test will be reduced if a stopwatch is used instead of timing mats for measurement of the time. If

using a stopwatch, you should have two people record simultaneously and use the average of the

two measurements. It is also important to give the participants adequate practice so that they can

confidently run up the stairs with maximum effort.

Vertical Jump Test

The athletes’ jump height can be affected by a multitude of factors which should be considered

when reviewing the results:

•

•

•

•

•

•

•

Shoulder range of motion

Fatigue

Motivation

Displacement from the take-off point

Single- or double-arm swing

Placement of the non-dominant hand if a single-arm swing is used

Flexing of the hips, knees, or ankles (bending)

Statistical Analysis

Student’s t-test

A t-test is used to measure the difference between exactly two means. Its focus is on the same

numeric data variable rather than counts or correlations between multiple variables. If you are

taking the average of a sample of measurements, t-tests are the most used method to evaluate

that data. It is particularly useful for small samples of less than 30 observations. For example, you

might compare whether systolic blood pressure differs between a control and treated group,

between men and women, or any other two groups.

Assumptions of t tests

Because there are several versions of t tests, it’s important to check the assumptions to figure out

which is best suited for your project. Here are our analysis checklists for unpaired t-tests and

paired t-tests, which are the two most common. These (and the ultimate guide to t-tests) go into

detail on the basic assumptions underlying any t-test:

•

•

•

•

•

Exactly two groups

Sample is normally distributed

Independent observations

Unequal or equal variance?

Paired or unpaired data?

Interpreting results

The three different options for t-tests have slightly different interpretations, but they all hinge on

hypothesis testing and P values. You need to select a significance threshold for your P value

(often 0.05) before doing the test.

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While P values can be easy to misinterpret, they are the most used method to evaluate whether

there is evidence of a difference between the sample of data collected and the null hypothesis.

Once you have run the correct t test, look at the resulting P value. If the test result is less than

your threshold, you have enough evidence to conclude that the data are significantly different.

If the test result is larger or equal to your threshold, you cannot conclude that there is a difference.

However, you cannot conclude that there was definitively no difference either. It’s possible that a

dataset with more observations would have resulted in a different conclusion.

Depending on the test you run, you may see other statistics that were used to calculate the P

value, including the mean difference, t-statistic, degrees of freedom, and standard error. The

confidence interval and a review of your dataset is given as well on the results page.

Tails

The tail refers to the end of the distribution of the test statistic for the particular analysis that you

are conducting. A one-tailed test looks for an “increase” or “decrease” in the parameter whereas a

two-tailed test looks for a “change” (could be increase or decrease) in the parameter. For this

analysis, choose two (H1: µ1 ≠ µ2).

Effect size

Effect size is a quantitative measure of the magnitude of the experimental effect. The larger the

effect size the stronger the relationship between two variables. You can look at the effect size

when comparing any two groups to see how substantially different they are. For this analysis,

choose medium effect size (0.5).

Standardized or unstandardized effect sizes

For the unstandardized effect size, you just subtract the group means. To standardize it, divide

that difference by the standard deviation. It’s an appropriate effect size to report with t-test and

ANOVA results. The numerator is simply the unstandardized effect size, which you divide by the

standard deviation. For this analysis, choose standardized effect size.

Significance level or alpha level

The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis

when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a

difference exists when there is no actual difference. For this analysis, the significance level will be

0.05.

Correlation Coefficient

There is a correlation between two variables, or statistical association, when the value of one

variable may at least partially predict the value of the other variable.

The correlation is a standardized covariance, the correlation range is between -1 and 1.

The correlation ignores the cause-and-effect question, is X depends on Y or Y depends on X or

both variables depend on the third variable Z.

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Similarly, to the covariance, for independent variables, the correlation is zero. A positive

correlation, changes go in the same direction, when one variable increases usually also the

second variable increases, and when one variable decreases usually also the second variable

decreases. A negative correlation, opposite direction, when one variable increases usually the

second variable decreases, and when one variable decreases usually the second variable

increases. A perfect correlation entails when you know the value of one variable you may

calculate the exact value of the second variable. A perfect positive correlation r = 1 and for a

perfect negative correlation r = -1.

Assumptions

•

Continuous variables – the two variables are continuous (ratio or interval).

•

Outliers – the sample correlation value is sensitive to outliers. We check for outliers in

the pair level, on the linear regression residuals,

•

Linearity – a linear relationship between the two variables, the correlation is the effect

size of the linearity (the commonly used effect size f2 is derived from R2 (r and R are the

same).

•

Normality – bivariate normal distribution. Instead of checking for bivariate normal, we

calculate the linear regression and check the normality of the residuals.

•

Homoscedasticity, homogeneity of variance – the variance of the residuals is constant

and does not depend on the independent variables Xi.

The following website will help with the statistical analysis you must run for the data. You should

report calculate and report the sample size (n), mean, standard deviation, variance, and range for

all metrics.

https://www.statskingdom.com/index.html

Applications

1.

Using the student’s t-test, compare the means of the vertical jump height between the jump

map and the vertec. Include the statistics to support your interpretation of the data with respect to

your stated hypotheses.

2.

Explain the relationship the relationship between jump height (use jump mat) and power as

determined by the data. Include the correlation coefficient and confidence intervals to support your

interpretation of the data.

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References

Hetzler RK, Vogelpohl RE, Stickley CD, Kuramoto AN, Delaura MR, Kimura IF. Development of a

modified Margaria-Kalamen anaerobic power test for American football athletes. Journal of

strength and conditioning research / National Strength & Conditioning Association.

2010;24(4):978-984.

Lamas L, Ugrinowitsch C, Rodacki A, et al. Effects of strength and power training on

neuromuscular adaptations and jumping movement pattern and performance. Journal of strength

and conditioning research / National Strength & Conditioning Association. 2012;26(12):3335-3344.

Sayers SP, Harackiewicz DV, Harman EA, Frykman PN, Rosenstein MT. Cross-validation of three

jump power equations. Medicine and science in sports and exercise. 1999;31(4):572-577.

Johnson DL, Bahamonde R. Power Output Estimate in University Athletes. The Journal of

Strength & Conditioning Research. 1996;10(3):161-166.

Altman N and Krzywinski M. Association, correlation and causation. Nature methods 12: 899-900,

2015.

Hazra A and Gogtay N. Biostatistics Series Module 6: Correlation and Linear Regression. Indian

journal of dermatology 61: 593-601, 2016.

Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical

research. Malawi medical journal : the journal of Medical Association of Malawi 24: 69-71, 2012.

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Data Table 1, Anthropometric information

Gender

Height (m)

Weight (kg)

Margaria Power Test

(W)

Jump Test

Jump mat (cm)

Vertec (cm)

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Group members name:

Lab title:

Null hypothesis (H0): states that a population parameter (such as the mean, the standard

deviation, and so on) is equal to a hypothesized value. The null hypothesis is often an initial claim

that is based on previous analyses or specialized knowledge.

Alternative Hypothesis (H1): states that a population parameter is smaller, greater, or different

than the hypothesized value in the null hypothesis. The alternative hypothesis is what you might

believe to be true or hope to prove true.

Ho:

Rationale:

H1:

Rationale:

References:

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