Veins
After leaving the capillaries, the blood enters a network of small venules, which feed into veins. These, in turn, carry the blood back to the atria of the heart. The walls of veins are thinner and less elastic than arteries, but they are also more flexible.
Veins tend to run between the muscle blocks of the body and nearer to the surface than arteries. The larger veins contain valves that maintain the direction of blood-flow. The flow of blood in veins is helped by contractions of the skeletal muscles, especially those in the arms and legs. When muscles contract they squeeze against the veins and help to force the blood back towards the heart. Once again, this is known as secondary circulation.
What is heart recovery?
Heart rate recovery is the length of time is takes for your heart rate to return to its baseline after a period of exercise.
Training and the heart
As your heart muscle becomes stronger, it can pump blood more effectively. Each contraction of the muscle is stronger to force more blood through your circulatory system. As you continue to train, your blood volume increases. This increase allows more oxygenated blood to reach your muscles and gives your heart more volume. So a stronger contraction with higher volume of blood increases the amount of oxygen and nutrients circulating in your body.
Resting heart rate
As the conditioning of your heart muscle increases, your resting heart rate decreases. The amount of oxygen and nutrients that your body uses at rest does not change when you are in top condition. The heart does not have to pump as many times to deliver fresh blood to your cells, organs and tissues when it is contracting more forcefully.
Factors which affect recovery time:
Several factors affect heart rate at rest and during exercise. In general, the main factors affecting heart rate at rest are fitness and state of recovery. In general, fitter people tend to have lower resting heart rates. For example, Martin Hanford has an average heart rate of 28 bpm. The reason for this is that, with appropriate training, the heart muscle increases in both size and strength. The stronger heart moves more blood with each beat and therefore can do the same amount of work with fewer beats. This means that in my experiment, someone with a higher BMI (of 30, for example) may have a lower resting heart rate than those with a lower BMI.
An extremely important factor affecting exercise heart rate is temperature. Warmer temperatures cause the heart to beat faster and place considerable strain on the body. For example, when it is hot, the body must move more blood to the skin to cool it while also maintaining blood flow to the muscles. The only way to do both of these things is to increase overall blood flow, which means that the heart must beat faster. The temperature in our investigation cannot be controlled and will therefore affect the outcome of the results.
The health of the individual determines the length of the recovery time after exercise. A heart rate which does not decline quickly after an exponential increase in BPM has poor physical health. There are various factors which can affect the time of recovery. For example, weight loss due to increased exercise would mean that the muscles of the heart are stronger than before and can thus pump blood around the body faster. Someone that is less likely to conduct exercise will have a weaker heart and thus will take more energy to pump blood around the body; increasing heart-rate recovery.
To calculate BMI is will use the flowing formula:
Factors that affect the recovery time
BMI- Body Mass Index correlates with weight, the higher your BMI is, the faster you heart pumps blood to the body, thus affecting recovery time.
Body mass – The constituent weight of the body.
Muscle tone – How muscle an individual is, which determines their lifestyle.
Stamina – The ability to maintain prolonged physical effort
Weight – The weight of a person
Diet – The diet of a person, affects body mass.
Strength – The physical power and energy of a person.
Lung capacity – The total amount of air an individual can breathe out after completely filling their lungs.
Height – May affect the strength of the heart and how many times it beats per minute.
Lifestyle - Those that undertake regular exercise are more likely to have a lower recovery time than those that do not.
Stress – Stress affects the cardiovascular system which affects blood flow; which therefore affects recovery time.
Glucose levels – The amount of glucose present (artificially stimulated or natural)
Medical issues – People with certain diseases such as asthma may not be capable of undertaking prolonged exercise.
Variables
Control variable (what will be kept constant):
Undertaken exercise location – All of the test subjects will run the same distance.
Measured time after exercise – The equipment used throughout the experiment will not be changed.
Age - All of the test subjects are of the same age. The information obtained from the experiment will represent each of the BMI groupings for our age group.
Dependant variable
Recovery time after exercise - As the BMI of an individual changes so will the time taken to recover.
Weight of clothing – Each subject will have different weights of clothing that will contribute to their overall weight; to overcome this I will subtract 0.5KG off the total value when calculating BMI.
Independent variable
BMI (body mass index) – We need to change the BMI of the test subjects so that I can evaluate the effects it has on recovery time and moreover will contribute to the answer of my hypothesis.
Not all variables can be controlled throughout the experiment. I will not be able to control the temperature of the day (which may alter recovery time as mentioned in the introduction) and the weights of the test subjects. Another issue is that some individuals may not push to their physical forces when running the 100m.
Methods
- The first step is to gather all of the equipment needed for the experiment.
- The second step is to make sure all of the equipment works and can be used in the experiment.
- I then need to measure the heights of each test subject, in doing this I will make sure they are shoe-less so I get an accurate measurement.
- I will then use a scale to measure the individuals’ weight. I will also subtract 0.5KG to make up for the weight of the clothing on the individual.
- The next step would be to use the formula (previously mentioned) to calculate the BMI of all the test subjects.
- I then will take the subjects to the 100 metre racing track and prepare all of the equipment and the roles. I will be giving a demonstration on how each individual can measure their heart rate.
- After the demonstration, all of the test subjects will measure their resting heart rates for the duration of one minute (BPM), this will be repeated three times to ensure that the information provides a true representation for the BMI grouping.
- Upon obtainment of the resting heart rates I will send one of the subjects to run the 100m (in order of BMI, from lowest to highest).
- On finishing the exercise the subject will need to measure their heart rate for the thirty seconds, the reason for doing this is because the resting heart rate will eventually start to decrease after a longer time period and would therefore not represent the true value.
- I will multiply the results that I obtained after thirty seconds by two and record the data down in a table.
- The next step will be to repeat steps 8*-10 for each of the test subjects.
- After I have completed this I will make each of the subjects conduct the experiment two more times, to accumulate an overall of three attempts, by doing this I can obtain and corroborate each of the values to produce an accurate, average value which would then be used to draw a graph upon.
I believe the methods that I have chosen will be the most effective when conducting my experiment; this is because I have pre-planned the area and the variables which might affect the BMI (weight).
Outliers
The results shown above suggest that my prediction is accurate. As there are 2.4 differences in the BMI and the average recovery time is 24 seconds difference. Therefore I can conclude that the results I obtained are accurate and reliable. Also increasing my confidence on the prediction I made.
The result above is an outlier, due to the fact that his recovery time is much lower than the other person for that group. Due to this result, there does not really seem to any particular correlation between the two variables.
There is a strong positive correlation between a higher BMI, and an increase in recovery time after exercise, this suggestion worked for all except one subject, of a normal- average- BMI who’s recovery time was 53 seconds (as shown above). This could possibly be due to that the particular subject above could have a healthy diet outside of school, which has little fats building in his arteries, or he undergoes exercise frequently, this affects physical fitness.
The subject’s recovery time increases enormously after each test. The experiment was consisting of a 100m run. I think the reason for the three results to vary enormously is because the subject didn’t recover fully from the last run, therefore resulting in an enormous increase between the repeating.
Evaluation a)
As a whole, I think that the experiment was a success. My prediction was correct and I managed to obtain accurate and reliable results, even though the last result seen on the temperature change graph is an outlier. I followed safety precautions to make sure that I wasn’t injured during the conductment of the experiment. The first precaution that I conducted was I made sure we chose a dry area of land (under a tree) to conduct our experiment on as on the day it had previously rained and I didn’t want the risk of a test subject slipping over (Refer to risk assessment).
I also made sure that the chosen location was clear of any stones as during the experiment the test subject could have fallen over, which could have resulted in a sprain or even a broken bone. The next precaution was that I instructed the runners to exert a medium amount of force when running, the reason why I did so as I believe some of the runners would have tried to run too hard and this could have resulted in tearing a muscle.
I encountered several minor issues during my experiment, luckily, the only delayed the time in which the experiment was carried out and did not affect the results directly:
- The main issue was that people from other members of our class kept walking through the 100m area whilst our test subjects were running, to overcome this we had to wait for five minutes (for the subjects’ BPM to return to normal) and tried again.
- Another issues was that the clouds moved from the front of the sun and the test subjects could not see where they were running (the sun burnt their eyes); to overcome this issue we conducted the experiment in the opposite direction.
- One of our test subjects fell over when running and hurt their leg, to overcome the issue we tested the other subjects, whilst they had a time to recover so the incident did not limit the run they had conducted.
I encountered several issues during my experiment during my experiment which has affected my results. First of all, before the exercise I measured all of the resting heart rates of my test subjects. In doing so, two of the subjects had trouble reading from the pulse meter and guessed their final heart rate based on what they had seen before, this means that the resting heart rates are not valid and so decrease the reliability of my other data. To overcome this we could have we measured the resting heart rate three times individually and taken an average, this would increase the confidence of my results and so their reliability as we could undermine any possibly faulty readings.
Another issue I faced which has affected my results was that when we took the final heart rate readings in BPM at the end of the experiment some of the test subjects still had trouble reading from the pulse meter and guessed their final heart rate based on what they had seen before, this again means that the resting heart rates are not valid and so decrease the reliability of my data results.
The pulse meter that we used in our experiment was not reliable as the result shown kept fluctuating so it was hard to get an accurate reading. I had used the most common result shown on the pulse meter as the final reading, making it a guess and not an actual reading. The actual readings could possibly be a few off at least. It would have been better to use a heart rate monitor which was strapped to the subject as this would have given me an exact reading. The strapped heart rate monitor could also record heart rate changes over short time periods, where the heart may be changing.
If I were to repeat the experiment I would first changed the equipment that I used, as mentioned before I have explained possibly improvements which would overcome the issues encountered. Another thing that I would change would be the time management in which the experiment was carried out. During the third repeat of my experiment we had run out of time so I had to rush it, making me feel as though I this has resulted in in-correct measurements, which would thus have affected my average results and the graph based upon it. Next time I would plan out my time to ensure that I do not rush my experiment which would thus increase the validity of the results and the conclusions based on them.
The next step that I would change about how I conducted my experiment was to increase the reliability of my results by repeating the outlier that I encountered on the graph. This would result in me having an accurate result set which I could calculate averages on. I have come up with an improved method list for if I was to conduct the experiment again (including the new equipment):
Step one – Find an ideal time and location to conduct the experiment (areas which had not previously been rained on) and that were of distance from those other than our group.
Step two - Collect all of the equipment needed for the test (measuring rulers, heart rate monitor, etc).
Step three – Make sure all of the equipment works and everyone is familiar with how to use them.
Step four – Take the height of the test subjects.
Step five – Take the weights of the test subjects.
Step six – Use the formula to calculate the BMI’s of each of the test subjects. The formula should be repeated three times on the calculator to ensure the final result was correct and had been entered correctly.
Step seven – Take the starting heart rate of all the test subjects three times and record the average as the final result.
Step eight - I then will take the subjects to the 100 metre racing track and prepare all of the equipment and the roles within the group, expressing the time taken for each testing, so that we do not go over and rush.
Step nine - Upon obtainment of the resting heart rates I will send one of the subjects to run the 100m (in order of BMI, from lowest to highest).
Step ten - On finishing the exercise the subject will need to measure their heart rate for the thirty seconds, the reason for doing this is because the resting heart rate will eventually start to decrease after a longer time period and would therefore not represent the true value. I will take the recording three times to get a consistent final result.
Step eleven - I will multiply the results that I obtained after thirty seconds by two and record the data down in a table.
Step twelve – Repeat the previous steps two more times for each test subject to have a total of three attempts which I can then corroborate each of the values to produce an accurate, average value which would then be used to draw a graph upon.
At a BMI of 18.2 and a recovery rate of 45 the average is around two seconds lower than the line of best fit, this suggests that the result is reliable. The other points on my graph (in terms of BMI and recovery rate) are on average two or three seconds below the line of best fit, this shows that my results are reliable, as they are not largely below or above the line of best fit. The outlier can be easily identified from the graph as both the repeating and the average are significantly lower than the line of best fit, the average is 19 seconds off the line of best fit, this proves that the result is in fact an outlier.
The reason for the large range bars during the last BMI classification I believe have been caused by rushing the experiment towards the end of the third repeat. The fact that the range bars of a large size for the subjects in the BMI classification of overweight size decreases the confidence I have of my results as it shows the extent of difference in the repeats. The range bar showed on my graph shows that the results for one subject who was in the overweight category, who had 118 seconds of recovery time, were different. The result I obtained from that particular subject was different through all three experiments. I also believe that because the two subjects are overweight he did not recovery fully from the last run, therefore resulting in an enormous increase between the tests.
Evaluation part B
My results are good, even though they contained an outlier, but are not to what I expected. The size of the range bars for the last BMI classification shows that there is inaccuracy in my data. The main cause of the range bar sizes is due to the time frame we had to complete the third repeat and the fluctuations shown by the pulse monitor. A heart rate monitor as described before would have been more effective as an exact recording will be given based on the changes in heart rate as it would record the change and determine the final result.
I think my results are good considering the equipment I had available and the conditions of the experiment. This is because the trend shows a gradual increase and was not jumping, as I thought. In general my results are both reliable and accurate. However in my results there is an outlier. The outlier on my results has been easy to identify due to the fact that it can be seen in the results table clearly, the fact that Jamol, with a BMI of 20.9 and a classification of ‘normal’ as the testing with the other subject in the same, ‘normal’ classification showed the average recovery time of seventy-seven whereas Jamol with a BMI of 20.9 showed an average recovery time of 53, a 24 second difference. Moreover, the outlier can be identified by looking at the graph; the average and repeating of the experiment are below the line of best fit, located on page ____.
The most reliable results were for the classification of underweight. Both BMI’s, 18.2 and 18.4, this is down to the fact that the range bars for the two are a lot smaller than for other BMI readings, especially 27.1 and 27.4.
My graph shows a degree of scatter. The scatter is shown on the graphs through the representation of range bars. It shows that some of the results are less accurate; however it also shows that some are very accurate recordings. The less accurate results are the last two BMI’s (27.1 and 27.4). This means that there isn’t a large gap in the numbers from each of the tests. From the graph we can see that BMI’s 18.2, 18.4 and 20.7 are very accurate as the range bars are small and are close to the line of best fit; this therefore suggests that there was a greater degree of accuracy.
Overall, I think my project has progressed smoothly. I believe my project has continued as planned even though I encountered an outlier, the outlier itself was not of major problem as the graph and data still showed the eventual increase in heart rate recovery, as the BMI increases. I have confidence in my results as they are fairly accurate (from looking at the range bars and the distance from the line of best fit). I think I have a few un-reliable results as there were limitations to the equipment I could use and the quality of them. As explained before, the pulse monitor fluctuated a reading so the subjects had to guess the final reading based on what was displayed.
Review
The result shown on the secondary data sheet shows the same correlation trend as my results, therefore I have high confidence that my results are accurate. However, due to the outlier the trend shown on my results do not match up with the trend shown on the secondary data sheet. Nevertheless, this could be due to his average recovery rate was significantly lower than the average heart rate of the other subject in the same BMI classification. The same applies for their individual repeating. One had 77 whereas the outlier had 53. The reason the test subject was an outlier could be due to several factors. For example, the subject might undergo more exercise, better diet, better lifestyle etc. So if we despite this, the correlation shown on my graph is similar to the correlation shown on the secondary data sheet, thus increasing the confidence of my results.
The secondary data sheet that we were given in this coursework shows different results for different types of experiment. For example, marathon runners, sex, water or sports. The only experiment in the secondary data sheet that is similar to mine was the marathon runners, because it contained results of heart rate which is similar to mine. I could use this to identify any potential outliers.
The range bar showed on my graph shows that the results for one subject who was in the overweight category, who had 118 seconds of recovery time, were different. The result I obtained from that particular subject was different through all three experiments. There the results for that subject show a long range bars on the graph. The reason this particular subject had three different results each he did not recovery fully from the last run, therefore resulting in an enormous increase between the tests. If I look at the second and third results. The third results vary enormously from the average than the second results. So thereby I can conclude that result 3 is an outlier. Hence confirming that overweight subject had longer recovery time. This decreases my confidence on the result I obtain, because of the large range bars.
In order to back up my results, I obtained further evidence from another piece of secondary data, which has the same hypothesis as mine. The secondary data contains more test subject, which is something I should have done. The trend of the results is similar to the trend of my results. This also allows me to gain more confidence on my results. Although, the outlier makes the results in-accurate, making the correlation different thus decreasing the confidence of results.
To increase confidence of my result further, I used result from other people from our class who had the same hypothesis as mine. His result also showed the same trend as mine. The similarities I had with the result from other people in our class are shown as following:
- The results had similar trend, which therefore increases my confidence on my results.
Bibliography
2009 Macmillan Holdings, LLC.
Eric Loch ridge 2011
