Disclaimer about this Blog: February is American Heart Month. Heart disease is the leading cause of death in the United States. Data from 2022 shows that 1 in 5 deaths were caused by heart disease, and according to the American Heart Association, over 121 million adults in the United States are living with cardiovascular disease.
Over eight years ago, I joined the 121 million when I suffered an episode of Atrial Fibrillation (A-Fib).
As a young adult, I was extremely nervous about what this meant for my future. Since then, I have made it a priority to stay active and remain conscious of my heart health. I made it a goal to run a half-marathon. Understanding the relationship between heart health and running, I decided to map out how to best plan my runs to keep my heart rate at an optimal level.
Fortunately, as an employee of Minitab, I get to take advantage of some of the most powerful data analytics solutions to do this.
As I started running, I recorded data on various factors that can impact my average heart rate, such as run distance, elevation gain, average pace, day of the week (maybe some days are consistently more stressful than others), and temperature. Using the data set below, I'll explain how I used Minitab Solutions to analyze this data and plan the best runs for my heart health.
Importing Data into Minitab: I started by importing my running data into Minitab using Minitab Connect forms. It was a seamless and easy way to import data without needing to open Minitab after every single run.
Descriptive Statistics: Then, I used the Descriptive Statistics tool to get an overview of my data. This provided me with measures such as mean, median, standard deviation, and range for each variable just to get a basic idea of where my data currently stands.
Correlation Analysis: Next, I performed a correlation analysis to see how different variables are related to my average heart rate. This helped me understand which factors have the most significant impact on my heart rate.
Interval Plot: As I could see that temperature has a larger role than I anticipated, I wanted a clear visualization. So, I created an interval plot of Avg HR vs. temperature.
Regression Analysis: Now that I had my insights, it was time to predict and plan how and when I should run. I conducted a regression analysis to create a model that predicts my average heart rate based on the other variables. I could see how changes in pace, distance, elevation gain, and temperature affect my heart rate.
Going into this exercise, I fully anticipated my average pace to have the greatest impact on my average heart rate.
I was wrong.
A variable I have much less control over plays a larger role in my average heart rate while running, temperature. This is incredibly helpful for me when planning runs though. I don’t want to avoid running just because it’s warmer outside, but as someone with a heart condition, I need to be cautious. It also allows me to take charge of the variables I can control, like average pace.
An example of how I can plan to move forward: If I am planning a 5-mile run with an elevation gain of 32, and it’s 75 degrees outside, according to my regression equation, I need to slow my avg pace down to a 13-minute mile. This would give me a predicted average heart rate of 155.
As a competitive person, that seems so slow! But my goal in the coming year is to maintain heart health, not so much a fast mile. I am not a statistician, but the ease-of-use Minitab solutions provide makes it possible for anyone to make data-driven decisions.
By using Minitab to analyze my running data, I can make informed decisions about my training plan to optimize my heart health. Understanding the impact of pace and temperature on my heart rate allows me to tailor my runs to keep my heart rate within a healthy range. As we observe American Heart Month, let’s prioritize our heart health by staying active and making data-driven decisions to support our well-being.