PEMES 3153 Physiology of Exercise
Introduction to Laboratory Data
If you are not familiar with using Excel to graph and write computational formulas, you need to work through some tutorials that can be accessed at lynda.uni.edu. You log in with your CAT ID and then can access the tutorials. You are expected to be able to do basic graphing and writing computational formula in Excel.
The Exercise Physiology Laboratory provides an opportunity to examine the acute effects of exercise on various physiological variables in a controlled environment. During the semester, we will be taking measurements using the available laboratory instruments on members of the lab section. The measurements will then be used to illustrate the response between an independent variable (exercise intensity or time for instance) and a dependent variable related to that week’s lecture content. In order to report these measures, the data must be recorded in scientific units using the common international system (SI). The inside front cover of your textbook lists the SI units that must be used in the reporting of the laboratory findings. Refer to this list to ensure you are using the correct unit of measure for the measure in question. Additionally, this list provides derived units commonly calculated in exercise physiology (work, power).
Data are also used to establish relationships between dependent variables in order to evaluate the interactions between physiologic mechanisms in the body. Relationships are classified as linear or curvilinear based upon the pattern created when the two variables are plotted on a graph. The strength of the relationships is determined using the correlation coefficient (Pearson r). For our purposes in this class, refer to the following table when describing a relationship:
Strength: / r-value:Strong / >0.70
Moderate / 0.30-0.70
Weak / <0.30
The correlation coefficient is a measure of how well one variable can be predicted by knowing the other variable. When two variables have a strong relationship one variable can be predicted from the other variable with a higher degree of accuracy.
In addition to the strength of a relationship, the correlation coefficient can be either positive or negative. Whether the value is positive or negative determines direction of the relationship. If the correlation coefficient is positive then as the value of one variable increases the value of the second variable also increases or as one decreases the second also decreases. If the correlation coefficient is negative, as the value of one variable increases the second variable decreases or vice versa.
The sequence in the laboratory assignments throughout the semester will be to introduce a topic (related to the previous lectures), take measures or make observations, organize the data into a spreadsheet for analysis and/or graphing, and answer questions related the topic. Today you will examine the relationship between body mass and stature.
Measurement
Each member of the lab section must perform the following measures:
1. Remove shoes and measure body mass using the scale (record in pounds to the nearest 0.5 pounds.).
2. Determine standing height (stature) using the stadiometer (record in inches to the nearest 0.5 inches.)
3. Use a flexible tape measure and determine your abdominal circumference by measuring your circumference at the level of the umbilicus. Make sure the tape is parallel with the floor when you make the measurement. Measure in inches to the nearest 0.5 inches.
Analyses
Use an Excel spreadsheet to create a group record of the data. Create the following columns on the spreadsheet and compute the BMI: Column A – Gender (Male = M, Female = F); Column B = height in inches to the nearest 0.5 inches; Column C = weight in pounds to the nearest 0.5 pounds; Column D = abdominal circumference to nearest 0.5 inches; Column E = Body Mass Index or BMI (BMI = weight in kg/(height in m)2). Calculate the mean and standard deviation for each of the values. Cut and paste the means and standard deviation of the data in a table created in MS Word. Label the columns and rows appropriately.
Graph 1. Plot the weight (vertical axis) and height (horizontal axis) values on a scatter graph and label the axis and indicate the units of measure on the axis label. Determine the line of best fit and compute the correlation coefficient to determine the strength of the relationship. Cut and paste the graph below.
Graph 2. Plot the weight (vertical axis) and abdominal circumference (horizontal axis) values on a scatter graph and label the axis and indicate the units of measure on the axis label. Determine the line of best fit and compute the correlation coefficient to determine the strength of the relationship. Cut and paste the graph below.
On your spreadsheet and in column F compute the predicted value for weight by knowing the height of each subject. In column G compute the predicted value for weight by knowing the abdominal circumference. These predictions are just the equation of the best-fit line (or trendline) that you determined from the scatter plots above. The equation will be in a form of:
Y = aX +b
Where: Y = the variable you are predicting
X = the variable you are using to predict Y.
a = the slope of the line of best-fit
b = where the line of best-fit intercepts the Y axis.
In column G compute the difference in the predicted weights using height and the actual weights (to nearest 0.5 pounds) and in column H compute the difference in the predicted weights using abdominal circumference and the actual weight. The mean of the differences in predicted and actual weights can be viewed as the average error one gets when predicting weight by knowing either height or abdominal circumference.
Application
1. Describe the relationship between height and weight as determined in our sample. Include the correlation coefficient to support your interpretation of the data.
2. Describe the relationship between abdominal circumference and weight as determined in our sample. Include the correlation coefficient to support your interpretation of the data.
3. What do you think the effect of each of the following would be on predicting weight by knowing height?
- Increase the number of individuals measured to all persons in all 5 labs of Phys. of Ex instead of just your lab.
- Randomly select 100 students from UNI to measure instead of individuals in Phys. of Ex.
4. What do you think the effect of each of the following would be on predicting weight by knowing abdominal circumference?
- Increase the number of individuals measured to all persons in all 5 labs of Phys. of Ex instead of just your lab.
- Randomly select 100 students from UNI to measure instead of individuals in Phys. of Ex.
5. Based on the data, is height or abdominal circumference a better predictor of weight? Explain.