The relationship between Resting Pulse Rate, Fitness Index, and BMI in determining Cardiovascular Health of Humans
Abstract
The body’s cardiovascular system assists the body in maintaining homeostasis when the body is engaged in physical activities. In today’s world, so many individuals have a poor cardiovascular system; therefore, this study tries to find factors that affect a cardiovascular system and discuss the relationship between these factors. For this study, factors identified included; fitness index, BMI, and the fitness index from a sample size of 17. The hypotheses tested were a negative relationship between the resting pulse and the fitness index, a positive relationship between resting pulse and BMI, and a negative relationship exists between fitness index and BMI.
Introduction
Homeostasis is an automated process where biological systems maintain stability by adapting to the best possible conditions that can guarantee survival. In animals, specifically mammals, some of the complex body organs usually regulate the homeostatic balance. For instance, the cardiovascular system ensures that blood is pumped to different parts of the body, and as a result, these body parts can function effectively. Therefore, the cardiovascular system has to work efficiently for the body to maintain a good homeostatic balance. When the body is engaged in physical activities, the body muscles will require more oxygen. For the body to get the much-needed oxygen, the heart will have to pump more blood and ensure that oxygen flow is increased.
There are two measures for the state of the cardiovascular and they include; Body Mass Index and the Fitness Index. The BMI is the ratio between an individual’s height in meters and the individual’s weight in kilograms. The BMI is used to measure the health category of an individual; for instance, below the recommended BMI is considered underweight, and above the recommended BMI is considered overweight. Fitness Index measures an individual’s recovery rate from the high pulse rate to the normal pulse rate when the individual is engaged in physical activities.
An individual with an efficient cardiovascular system would return to their normal homeostatic levels faster than an individual with an inefficient cardiovascular system. An individual with a high Fitness Index would go back to their resting heart rate faster hence the hypothesis that there is a negative relationship between the Fitness Index and the resting heart rate. An increase in BMI decreases an individual’s overall health, hence the hypothesis that a negative relationship exists between Fitness Index and BMI. My third hypothesis is that there exists a positive relationship between resting pulse and BMI. An increase in BMI will make it difficult for an individual to return to their normal homeostatic balance meaning an individual with a higher BMI will have a higher resting pulse. These hypotheses can be tested using the coefficient of correlation and the t-test.
Methods and Materials
The experiment had a sample size of 17 students. The students were first asked to measure the resting heart rates by pressing on the superficial artery for 20 seconds. The resting heart rate was then multiplied by 3 to get the resting heart rates. The Harvard step test was used to determine the physical fitness of the students. All the participants were asked to step up and down a staircase for about five minutes. After completing the tests, their recovery heart rates were measured after they had rested for 1 minute. Their pulse rates were observed for 20 seconds before they were recorded. They were given a minute’s rest, and their pulse rate was observed for 20 seconds again before being recorded. The fitness index of the participants was also measured by dividing the sum of the three resting pulses by 15000 and calculated their BMIs by dividing their weights in kilograms to their heights in meters (lab manuscript 2021). After data collection was complete, the http://www.alcula.com/calculators/statistics/linear-regression/ website was used to create the graphs and calculate the coefficients of correlation while the website https://www.danielsoper.com/statcalc/calculator.aspx?id=44 calculated the p-values to be used in the t-test.
Results
| Variables | Coefficient of correlation | p-values |
| Resting pulse vs. BMI | 0.384 | 0.128 |
| FI vs. BMI | 0.846 | 0.051 |
| Resting pulse vs. FI | -0.343 | 0.177 |
This graph represents the relationship between FI and BMI, with FI on the y axis while BMI on the x-axis. The correlation coefficient is 0.051, close to zero, and the p-value for this case is 0.85.
This second graph represents the resting pulse rate and the BMI. The BMI is represented on the x-axis, while the resting pulse rate is represented on the y-axis. The coefficient of correlation is 0.38 while the p-value is 0.13
This third graph represents the resting pulse rate and the FI. The FI is represented on the x-axis, while the resting pulse rate is represented on the y-axis. The coefficient of correlation is -0.34, while the p-value is 0.18.
Discussion
The first hypothesis to be tested is that there is a negative relationship between resting heart rates and FI. The coefficient of correlation is -0.34, meaning we fail to reject the hypothesis. The p-value (0.18>0.05) means that it is statistically significant. The graph is negatively sloped, meaning there exists a negative relationship.
The second hypothesis tested is that there is a positive relationship between resting pulse rates and BMI. The correlation coefficient is 0.38 and is not closer to 1. The graph is positively sloped, and the p-value (0.13) is greater than 0.05. Therefore, we fail to reject the null hypothesis, meaning a positive relationship exists between the two variables.
The third hypothesis tested is that there exists a negative relationship between FI and BMI. The correlation of coefficient is 0.85, and the p-value is 0.051. The slope of the graph is downward sloping. We, therefore, reject the null hypothesis since there exists a relationship between the two variables. This could be due to discrepancies in observations or recording data. The data obtained and analyzed can be used to assist in future research on cardiovascular issues, which is a global problem.
Works Cited
Wilkins, 1984. Tharp GD, Experiments in Physiology (4th edition) Burgess Publishing Company, 1980.
Arcidiacono, G. Statistics Calculator: Linear Regression. 2020. Alicia. 10/16/2020.
http://www.alcula.com/calculators/statistics/linear-regression/
Soper, Daniel. “Calculator: p-Value for Correlation Coefficients.” Free p-Value Calculator for
Correlation Coefficients – Free Statistics Calculators, 2020, 10/16/20
www.danielsoper.com/statcalc/calculator.aspx?id=44.