Exercise Physiology, PRELAB

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
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
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

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.

Flight of 12 stairs


Jump mat

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.
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.
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
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
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.
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.
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
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.
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.

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

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.
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.
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.
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.
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,
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.
Data Table 1, Anthropometric information
Height (m)
Weight (kg)
Margaria Power Test
Jump Test
Jump mat (cm)
Vertec (cm)
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.

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