Data from graded exercise tests, including heart R-wave intervals, oxygen consumption, and mechanical power output on a beat-to-beat basis. It involves 18 teenage athletes (average age 15.2 years) from CREPS in the French West Indies, participating in sprint kayak, triathlon, or fencing. Clinical data, such as age, weight, height, and power at ventilatory thresholds, is available in a separate file.
Objectives:
● Analyze the VO2 max and power output generated by various athletes and glean insights
● Calculating HR and HR max
● Calculating vo2 max
● Relationship between height, weight, age with Heart rate,VO2
● Relation between VO2 , HR and power
● Analyzing VO2 max based on the type of sport
● Decrease of heart rate variability during exercise
● Maximal endurance performance related to ventilatory thresholds and VO2 max
Dataset overview:
This dataset comprises two files for a study involving 18 participants undergoing exercise testing. Here's a summary of each file:
1. subject-info.csv:
- Contains participant information at the time of the test.
- Variable "ID" identifies each participant.
- Variables "P_vt1" and "P_vt2" represent power output in watts measured at ventilatory thresholds 1 and 2.
-Additional variables provide information on age, weight, height, and the sport discipline of participants.
- This file contains 18 lines, one for each subject.
2. test_measure.csv:
- Contains cardiorespiratory measurements during exercise tests.
- Data is in long format, with one line for each beat measurement across all 18 effort tests.
- The "time" variable identifies the seconds elapsed from the start point of the effort test.
- "ID" identifies participants, "time" measures seconds elapsed since the test's start.
- Variables include "RR" for heart rate intervals, "VO2" for oxygen consumption, and "power" for mechanical power output.
- The dataset captures median and interquartile range statistics for effort test measurements, including duration and oxygen consumption.
- Additional variables in this file provide more detailed cardiorespiratory measurements.
We used python to understand and clean and do the analysis and tableau to make the visualizations.
Data preparation:
● We converted the data type of the "ID" column to the "category" (string format) since we do not perform calculations with the "ID" values.
● We identified 1148 null values in the "RR" column. To address this, we applied interpolation to fill these null values because "RR" is a continuous variable, and using mean or median values would not be appropriate in this context.
● We chose to handle missing values before addressing outliers in our data preprocessing pipeline.
● This sequence of operations was selected to prevent the premature loss of data, as outliers can sometimes be identified and handled more effectively once the missing values have been imputed. Furthermore, this approach ensured that we didn't unintentionally remove data points due to the presence of both missing values and outliers simultaneously.
● Finding Outliers in RR and VO2 columns using Tukey method and using box plots.
● Dropped the outlier rows because data 75% of data lies 484.
● Calculated Heart Rate value based on RR using formula.
HR=60000/RR.
After cleaned the data in the python and calculated VO2 max Used tableau to analyze and to make visualizations.
Analysis of the data:
The following are the verifiable correlations we were able to find within our dataset:
Correlation between VO2 and RR is negative
BMI shows a stronger correlation with power and VO2 than with HR.
VO2 and RR exhibit a negative correlation.
Heart Rate (HR) and Power demonstrate a positive correlation.
Heart Rate (HR) and VO2 show a positive correlation.
Over time, there is a gradual increase in power, VO2, and HR during exercise. However, during the recovery period after exercise, both HR and VO2 gradually decrease.
VO2max Analysis :
Classified VO2 max ranges based on the values for each participant and thus categories athletes whether they are fit or not. In this classification we consider ranges applicable for men. There are different VO2 max ranges for male and female. Since our dataset did not cover the gender part, we consider all participants to be male.
Cardiorespiratory fitness test is to identify whether the athletes are in fitness state. This is done by VO2 max of athletes instead of their maximum heart rate. There is a correlation between VO2 and HR.
Age , height, weight plays an important role in cardiorespiratory analysis as power tolerance level increases with age, weight and height.
When power increases, heart rate also increases. Heart rate reaches its maximum when power is at its peak.
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