# Calculating Relationships Between Common Measures Of Cardiovascular Health

- Category
**Health** - Subcategory
**Human Body** - Topic
**Cardiovascular System**

- Words
**874** -
Pages
**2**

## Abstract:

The cardiovascular system helps cells maintain a stable internal environment in different ways, but in the research done in this lab, we focused on the cardiovascular system interacting with the outside environment to pump blood throughout different parts of the body. Resting heart rate, systolic and diastolic blood pressure, body mass index, and fitness index were calculated for 23 people, before and after a step exercise, to estimate the linear relationships between these different factors of cardiovascular health. Four out of five linear relationships calculated were negative, and one of the correlation coefficients calculated was statistically significant. This data supports the hypotheses made for each linear relationship but does not support the hypothesis made that none of the data will be significant.

## Introduction:

Homeostasis is when cells have a stable internal environment. To achieve homeostasis in multicellular organisms, there needs to be a constant coordination of functions between specific organ systems. The common measures of cardiovascular health calculated in the lab were resting heart rate and pulse, systolic and diastolic blood pressure, body mass index, and fitness index (Laboratory Manual). The purpose of measuring these factors is to estimate and find the statistical relationship between each of the factors between 23 people. The cardiovascular system pumps blood to different tissues throughout the body to maintain homeostasis by the pumping of the heart, and this is what gives us diastolic and systolic blood pressure. Diastole is when the heart chambers are relaxing, and systole is when the heart chambers are contracting (Laboratory Manual). Body mass index (BMI) is a weight-to-height ratio of the amount of body fat in someone’s body and fitness index (FI) is a good way to measure fitness and a person’s ability to recover after exercising. There will be a negative relationship between BMI and FI, BMI and resting systolic blood pressure, FI and resting systolic blood pressure, and FI and resting pulse. The only relationship that will be positive is BMI and resting pulse, and none of these relationships will be statistically significant.

## Materials and Methods:

In this lab, 23 people recorded their resting blood pressure using a digital blood pressure monitor, then stepped up and down a stairstep for five minutes. After the step exercise, blood pressure was recorded after 1, 2, and 3 minutes after resting. From the sum of those three pulse counts, fitness index was calculated by dividing 15,000 by the sum of the three pulse counts. After that, personal Body Mass Index (BMI) was calculated by multiplying weight in pounds by 703, and then dividing that number by height in inches, squared. Everyone’s resting pulse, resting blood pressure, fitness index, and body max index values were put into a data set to be able to calculate linear relationships between each of these factors. To calculate the linear relationships, alcula.com was used to make graphs for each relationship, linear regression equations for each relationship, and the correlation coefficient for each relationship. Then to find out if the data was significant or insignificant, danielsoper.com was used to calculate the p-values of each relationship using the correlation coefficients.

## Results:

Figure 1. BMI vs FI

y=52.105769013064-0.010811239160459x

Figure 2. BMI vs resting Systolic Blood Pressure

y=116.86873683972-0.028539407249757x

Figure 3. BMI vs resting pulse

y=77.082120134942+0.08590123848122x

Figure 4. FI vs resting Systolic BP

y=133.14085994999-0.32822129098144x

Figure 5. FI vs resting pulse

y=133.28126210515-1.0415008627672x

Table 1. Correlation Coefficients

BMI vs. FI BMI vs. resting Systolic Blood Pressure BMI vs. resting pulse FI vs. resting Systolic Blood Pressure FI vs. resting pulse

-0.013 -0.0106 0.0699 -0.0985 -0.680

Table 2. P-Values

P- Value Significant or Insignificant

BMI vs. FI 0.953 Insignificant

BMI vs. resting Systolic Blood Pressure 0.962 Insignificant

BMI vs. resting pulse 0.751 Insignificant

FI vs. resting Systolic Blood Pressure 0.655 Insignificant

FI vs. resting pulse 0.000357 Significant

## Discussion:

In the data found, most of the data turned out to be insignificant, which is reasonable due to the data being completely random between 23 people. Only one relationship came out to be significant which means that there’s something statistically significant in the numbers for the relationship between fitness index and resting pulse. This does not support my hypothesis that none of the relationships will be insignificant because I believed that the data for all 23 people would be completely random or that someone could use the blood pressure monitor wrong when taking their blood pressure. This mistake could have happened, and that could be a possible reason why the data came out to be significant for that relationship. The results do support my hypotheses about each linear relationship. There’s a negative relationship between BMI and FI, BMI and resting systolic blood pressure, FI and resting systolic blood pressure, and FI and resting pulse. The only relationship that is positive is BMI and resting pulse. For a more accurate experiment, a group of people who are half physically fit and half obese could do this experiment and have their blood pressures taken by licensed nurses, to ensure accuracy. This might give more accurate data than the random group used in this lab and would most likely give more significant p-values.

## Literature Cited:

- Department of Ecology, Evolution, and Organismal Biology. (2019, Fall). Biology 1108L Laboratory Manual. Correlation between Common Measures of Cardiovascular Health. Kennesaw State University, GA.
- Arcidiacono, G. Statistics Calculator: Correlation Coefficient. 2019. Alcula. 10/6/2019.
- Arcidiacono, G. Statistics Calculator: Linear Regression. 2019. Alcula. 10/6/2019.
- Soper, D. P-Value Calculator for Correlation Coefficients. 2019. Free Statistics Calculators. 10/8/2019.