Analysis & Visualization Project (written)

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Dec 6, 2023

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29/04/2022 1 Disclaimer: data set and the way in which the data was collected was made available by a previous high school professor (Italy). Resting Heart Rate (RHR) vs. Change in Percentage (%) Research Question (at least 2): 1. How does someone’s resting heart rate (±1, bpm) correlate to their heart rate change in percentage (%) after one minute of working out? 2. How Athletic Heart Syndrome (AHS) affects percent change in RHR 1-minute after a standardized workout compared to non-athletes. Introduction & ‘description of the problem’: The heart is the key organ in the circulatory system. As a hollow, muscular pump, its main function is to propel blood throughout the body. It usually beats from 60 to 100 times per minute, but can go much faster when necessary (Durani, Rady Children's Hospital San Diego). The human heart is the most essential organ in the human body; without it, humans would not be able to live. Athletic Heart Syndrome (AHS), is when the resting heart rate is lower than normal and the human heart is enlarged. It is not a pathological condition. When repetitive cardiac loading is present, AHS is often linked with physiological remodeling. Physiological remodeling refers to the change in structure, function, shape, and size of the heart. This is due to an excessive amount of training. AHS is common between athletes who exercise for over an hour a day, however it can be overlooked, and sometimes can transform into greater issues. Athletes that compete in sports that require an abundance of cardiovascular endurance, such as soccer and basketball athletes regularly have a lower resting heart rate (RHR). According to Sally Edwards, a lower RHR means a faster heart rate recovery time. And therefore a lower percentage change in heart rate after a workout. Thus, leading to my research question: How does someone’s resting heart rate (±1, bpm) affect their heart rate change in percentage (%) after one minute of working out? The most simplistic way to investigate this question is to gather a sample of athletes and non-athletes and measure their RHR. Then, instruct them to undergo a basic workout routine, and record their heart rate after one minute of completing the exercise. Then use the change in percentage formula, calculating the change in percentage of one’s heart rate one minute after the completion of the exercise. Forty-five participants were used during the experiment, which is an adequate amount of data points in order to regard this experiment as credible and reliable due to the wide and diverse range of test subjects. The test subjects were all within the same age group (born between 2002-2003), but varied in physical ability and gender; therefore increasing the investigation's efficacy. Personally, my interest for this subject is due to the fact that I myself am an athlete, and am interested in exploring the effects of physical training on the heart. I am also considering adding a major in Biology. Goals of my research/prediction:
29/04/2022 2 A drop in resting heart rate usually equates to an increase in fitness levels, and your heart will recover quicker as you get fitter (Edwards, pg. 1). Thus leading to the hypothesis that if one’s resting heart rate is low, then their percentage change is also low because the lower one’s resting heart rate, the fitter they are and less time is needed to recover. Methodology: - Method Gave test subjects the contract to read and sign. Explained the procedure to them. Placed two markers 80cm apart on the floor (leg width of the jumping jacks). Proceeded to disinfect the apple watch. Then disinfected the subject's wrist. Strapped the apple watch onto the test subjects wrist. Measured the subject’s RHR Set the timer for 1 minute, and began the timer. Instructed the test subject to begin doing the jumping jacks. Once the timer stopped, test subjects immediately did eight push ups. Once done, their heart rates were measured and recorded. Set time for 1 minute again. When the timer ended, their heart rate was measured and recorded again. Finally, reconfirmed orally if the test subject still wanted their anonymous numbers to be used in the experiment. Repeated 45 times with 45 different voluntary test subjects. Findings: Quantitative Data : Data Table 1 RHR vs. % Change in HR after workout Test Subject RHR (±1) HR After Workout (±1) HR After 1 min (±1) % Change 1 55 100 58 5.45% 2 56 105 60 7.14% 3 65 112 72 10.77% 4 63 115 71 12.70% 5 67 109 78 16.42% 6 74 117 92 24.32%
29/04/2022 3 7 55 99 61 10.91% 8 76 121 93 22.37% 9 59 106 67 13.56% 10 63 111 69 9.52% 11 68 114 79 16.18% 12 68 114 78 14.71% 13 69 110 82 18.84% 14 57 104 62 8.77% 15 58 101 62 6.90% 16 72 120 84 16.67% 17 79 117 103 30.38% 18 67 110 78 16.42% 19 53 98 56 5.66% 20 61 108 69 13.11% 21 63 108 72 14.29% 22 65 113 76 16.92% 23 68 116 79 16.18% 24 64 110 72 12.50% 25 70 117 81 15.71% 26 71 118 83 16.90% 27 64 111 73 14.06% 28 68 119 80 17.65% 29 72 121 83 15.28% 30 65 113 76 16.92% 31 64 109 78 21.88% 32 67 118 81 20.90% 33 64 111 73 14.06% 34 59 102 66 11.86% 35 66 115 78 18.18% 36 68 120 80 17.65% 37 69 119 82 18.84% 38 65 113 73 12.31% 39 62 114 71 14.52% 40 67 118 77 14.93% 41 57 105 63 10.53% 42 71 121 84 18.31% 43 72 119 84 16.67% 44 54 98 60 11.11% 45 66 116 78 18.18% Percentage Change: - (HR % change after exercise - resting HR)/HR
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29/04/2022 4 a) Step 1: ( 58 55 )/ 55 = 0.0545.. . b) Step 2: 0.0545 100% = 5.45% - R Correlation: a) Excel Graph: Description of Data Source: As mentioned above in the disclaimer, this data was provided by one of my high school professors in Italy. The data set consists of 45 test subjects (students), who all willingly took part in this experiment. Analysis: The graph above very clearly indicates a strong correlation between the independent (RHR) variable and the dependent variable (% change in heart rate after workout). The correlation is as follows: 0.8991836156. In other words as the RHR deacreses, so does the % change, and vice versa. The r 2 value is 0.703 which indicates how accurately the points follow the trendline. In this instance the r 2 value is strong; therefore the points closely follow the positive trendline. There is an uncertainty of ±1 when relating to the RHR. However, this is not an issue due to how small the value is. The dependent variable would not be affected by the slight uncertainty of ±1 RHR. Furthermore, there are very few anomalies, if any. Other than the point with 79 bpm as its RHR with a % change of 30.38%, there are no other outliers. Description of Tests R-Squared Test The r-squared test measures how precisely your data fits the trendline on the graph.
29/04/2022 5 The x- axis represents the RHR (resting heart rate) and the y axis represents the percentage change of the heart rate one minute after the workout is completed. The line of best fit is a linear function, which is appropriate for this study as it shows the direct correlation between x and y. The R squared number, 0.703 is moderately strong. The boundaries are as follows: The r squared coefficient varies between 0 and +1 (0<r2<+1) if the value of r 2 >0.8, then the relationship is strong if the value of r 2 <0.5 the relationship is weak. If 05< r 2 <0.8, then there is a moderate correlation. In conclusion the use of the R squared test is beneficial in the understanding of how one’s resting heart rate is related to the % change and how well the data points fit the trend line in the graph. Pearson correlation (r coefficient) Correlation coefficient tests are utilized to effectively find the correlation between the independent variable and the dependent. The pearson’s coefficient allows one to acquire the linear relationship between the x-axis and the y-axis. Assuming that the two variables are normally distributed, the r coefficient is appropriate for the study because two variables are being compared; and therefore the correlation coefficient between the two variables is desired. In addition the trendline that is being examined is linear. The r coefficient found was 0.8991836156 which is very strong. The formula used to find the correlation is: where n is the sample size, and x and y are the individual sample points. The formulas return a value between -1 and 1, where: 1 indicates a strong positive relationship. 0 to 0.5 a weak relationship 0.5-0.8 a moderate relationship 0.8-1 a strong positive relationship -1 indicates a strong negative relationship. A result of zero indicates no relationship at all. All in all, the Pearson correlation test indicates that the relationship between one’s RHR and the percentage change of their heart rate recorded one minute after the workout is strongly correlated with 0.8991836156 as the r coefficient. Conclusion: According to Sally Edwards, the CEO and Founder of Heart Zones, Inc, and professional triathlete, a drop in resting heart rate usually equates to an increase in fitness (Edwards, 1). Edwards later goes on to explain how your heart will recover quicker as you become fitter. Thus, hinting at the fact that these two are correlated. My hypothesis states that if one’s resting heart rate is low, then their heart rates percentage change recorded after one minute of completing the workout is also low because the lower one’s resting heart rate, the fitter they are and less time is needed to recover. The linear function in the graph shows a positive correlation between the two variables, x and y. In
29/04/2022 6 addition, both the Pearson correlation (r coefficient) and R squared test supported my hypothesis by being strongly positive, and moderately strong. Discussion of Findings: On the positive side of the experiment, the trials ran smoothly. With all 45 test subjects completing the basic workout (1 minute jumping jacks with your feet opening 80cm, followed by eight pushups). All of the controlled variables were maintained which subsequently increases the legitimacy of the experiment. The jumping jacks width (80cm); the type of pushups (standard military push ups) - arms shoulder width, knees not touching the ground, chest to the ground and back up; time spent doing jumping jacks (1 minute); heart rate measuring device (Apple Watch series 4); and the age group of subjects (born all within 2003 and 2002). Moving onto the trendline, the range of the independent variable was more than enough to see the trend. In addition the controlled variables allowed the trendline to solely focus on the correlation between the independent variable and the dependent variable. Oppositely, there were some inconsistencies during the experimentation phase. For example, some test subjects did not maintain a steady pace, and therefore did less jumping jacks then the average person. Another limitation was the jumping jacks width. Many, as they got tired, were not able to open their legs the entire 80cm. However, the most inconsistent area of the experiment that I was able to observe was the pushup form. Many points that are higher up on the trendline did not know how to correctly do push ups. This makes sense because the one’s closer to the bottom are the one’s more in shape, and therefore should be more familiar with basic push up form. An uncontrolled variable that I would substitute into a controlled variable is the subject's clothing. Some had appropriate sports attire, meanwhile others did not and this may have caused greater fatigue, and therefore influenced their percentage change which is the dependent variable. Another small uncontrolled variable that I would include in my controlled variable section would be the subjects' food intake on the day of the testing to ensure that everyone is getting the same type of energy prior to their exercise. Lastly, although there were many inconsistencies with the physical workout, all 45 test subjects were tired at the end of the basic workout, especially those who had trouble completing it. Thus indicating that everyone’s heart rate increased, and no one underworked. References: "40.3A: Structures of the Heart." LibreTexts, MindTouch, 15 Aug. 2020, bio.libretexts.org/Bookshelves/Introductory_and_General_Biology/ Book%3A_General_Biology_(Boundless)/40%3A_The_Circulatory_System/ 40.3%3A_Mammalian_Heart_and_Blood_Vessels/ 40.3A%3A_Structures_of_the_Heart#:~:text=The%20heart%20is%20divided%20into,is%20p umped%20to%20the%20lungs. Accessed 29 Apr. 2022. "Athletic Heart Syndrome." Wikipedia, 9 Sept. 2020, en.wikipedia.org/wiki/ Athletic_heart_syndrome#:~:text=Athletic%20heart%20syndrome%20(AHS)%20is,conseque nce%20of%20repetitive%20cardiac%20loading. Accessed 29 Apr. 2022..
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29/04/2022 7 "Coefficient of Determination, R-squared." Newcastle University, 12 Mar. 2018, internal.ncl.ac.uk/ask/numeracy-maths-statistics/statistics/ regression-and-correlation/ coefficient-of-determination-r-squared.html#:~:text=R2%3D1%E2%88%92sum%20squared, from%20the%20mean%20all. Accessed 29 Apr. 2022. author unavailable Edwards, Sally. "HEART RATE TIPS WITHSALLY EDWARDS." ANT+, 2020, www.thisisant.com/consumer/news-info/ tips#:~:text=A%20recovery%20heart%20rate%20of,your%20fitness%20level%20is%20impro ving. Accessed 29 Apr. 2022. Fagard, Robert. "Athlete's Heart." National Center for Biotechnology Information, BMJ Publishing Group, Dec. 2003, www.ncbi.nlm.nih.gov/pmc/ articles/PMC1767992/. Accessed 29 Apr. 2022. "How To Calculate Percentage Change Or Difference Between Two Numbers In Excel?" Extended Office, www.extendoffice.com/documents/excel/ 3989-excel-calculate-percentage-change.html. Accessed 29 Apr. 2022. Vicinanza, Carla, et al. "Physiological cardiac remodelling in response to endurance exercise training: cellular and molecular mechanisms." BMJ Journals, BMJ Publishing Group, 2011, heart.bmj.com/content/98/1/5. Accessed 29 Apr. 2022. "2.6 - (Pearson) Correlation Coefficient R." PennState Eberly College of Science, Pennsylvania State University, 2018, online.stat.psu.edu/ stat462/node/96/.Accessed 29 Apr. 2022. Durani, Yamini. "Heart and Circulatory System." Rady Children's Hospital San Diego, Jan. 2013, www.rchsd.org/health-articles/ heart-and-circulatory-system/ #:~:text=The%20heart%20is%20the%20key,go%20much%20faster%20when%20necessary. Accessed 29 Apr. 2022.