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GUJHS. 2004 April; Vol. 1, No. 3
Jaclyn Artuso ‘04, MiJin Kang ‘04, Julee Pulverenti ‘04, and Samantha Tryon ‘05
Department of Human Science, School of Nursing and Health Studies
Purpose: This investigation is designed to evaluate human simulation technology to study the physiologic response of the body to the stress of exercise in a diseased individual and a healthy individual.
Hypothesis: The healthy individual will be able to adapt to the stress of the exercise. The diseased individual will only be able to adapt for a limited amount of time before he is unable to tolerate the stress of exercise.
Methods: The METI® human simulator was programmed to increase to specific heart rates at three-minute intervals. Heart rate, blood pressure, respiratory rate, percent oxygen, and ECG changes were recorded at each interval. The evaluation continued until the maximum predicted heart rate was successfully surpassed or until ECG changes and the other physiological data indicated an inability to adapt.
Conclusion: The results of this experiment indicate that health of an individual has an effect on their ability to overcome external stress and that the response of individuals with differing physiologic profiles to extreme environments can be studied using human simulation.
Physiological Adaptation: Background
Physiological adaptation is the internal adjustment that that the body undergoes in response to changing environmental conditions (Morris, 1999). Adaptability to the stress of a given environment varies from person to person (O’Neil, 2002). Successful adaptation is dependent on a variety of variables, including the health of the individual. In studying extreme environments, one can observe the limits of physiological adaptation. The human body cannot adapt to all levels of stress. Once subjected to the extremes in such areas of temperature or altitude, the body is bound to reach its limits. At this point, the body will either begin to adapt in a way that is pathogenic to the individual or its once helpful adaptations will fail to compensate which again causes harm to the person.
Much of the physiological adaptation literature is both prospective and theoretical. The adaptation in various extreme environments is difficult to measure, both because of the environment itself and in finding participants. There are ethical concerns of pushing a body past its limits to study its adaptability and consequently any studies of this kind are subject to intense scrutiny and are not generally accepted.
Adaptation in Exercise
Exercise is generally thought of as something that is beneficial to the body; however, it can become an overwhelming stress that the body might not be able to handle. The stress of exercise is a useful environment to study because it can be easily created and measured in a lab. A healthy body should be able to adapt to the stress of exercise at least up to a point. During physical activity, the body must adapt to meet the changing needs of the body. The oxygen demand of the muscles increases leading to an increased heart rate. This is accompanied by an increase in heart contractility, increasing the stroke volume and consequently the cardiac output (HR x SV). Exercise may also increase the levels of circulating epinephrine and norephinephrine, which stimulate the heart and add to the previously mentioned changes. The systolic blood pressure of the exercising patient increases; however the diastolic may decrease slightly or remain unchanged. Blood volume decreases due to loss of fluid in sweat. Respiratory rate also increases to accommodate the increased oxygen demand.
Clinical Exercise Testing
In clinical exercise testing, a patient is subjected to increasing exercise intensity in order to determine the patient’s ability to adapt to the demands of the exercise Wasserman et al., 1994). Various measures are made to determine the limits of the patient’s adaptability. Exercise tests are usually performed for one of two reasons. The first is to measure functional capacity. In this case, the test is performed in healthy, sedentary, and/or asymptomatic individuals to evaluate exercise tolerance and cardiac function (Hill & Timmis, 2002). The second use is as a diagnostic test performed to quantify heart and/or other chronic diseases (Robergs and Keteyian, 2002). Exercise increases heart rate and shortens diastolic time, which decreases the time for coronary perfusion. Therefore it is easier to detect coronary artery disease during exercise than it is at rest (Wasserman, et al, 1994). Diagnostic exercise testing is used to evaluate patients, who present with chest pain, have chest pain on exertion, and in patients with known ischemic heart disease (Hill & Timmis, 2002). In this use, exercise testing has a sensitivity of 78% and specificity of 70% for detecting coronary artery disease. It cannot therefore be used to rule in or rule out ischemic heart disease unless the probability of coronary heart disease is taken into account. It is of greatest diagnostic value in patients with an intermediate risk of coronary artery disease. In low-risk patients, a positive result for heart disease is probably a false positive and a negative result didn’t generate any new information and they were already low risk. In high-risk patients, a negative result would not be sufficient to rule out heart disease and a positive result would be as useful as a negative result in low risk patients (Hill & Timmis, 2002).
Before a clinician performs an exercise stress test he must evaluate the risk that the test will pose to the patient. Several medical organizations have developed categories that group individuals according to their current health conditions and their potential risk for injury during exercise testing or training. The American College of Sports Medicine has established three categories of risk stratification: Apparently Healthy, Increased Risk and Known Disease (Robergs and Keteyian, 2002). In essence, risk stratification before exercise testing is used to determine whether or not the benefits outweigh the potential risks to the patient.
Once a clinical exercise stress test is prescribed, the next step is to choose the test protocol. The choice of protocol is based on the clinical and functional status of the patient. All protocols should have a gradual warm-up period, and progressively increase in workload. The Bruce protocol (Bruce, 1972) as illustrated in Table 1 is a popular protocol in clinical settings as well as the modified Bruce protocol (Myers, Sapin and O’Rourke, 1993) for patients at higher risk.
Results of Exercise Testing
The key to exercise testing is being able to interpret the results. The main points of analysis that provide useful information are the heart rate, blood pressure, and the electrocardiograph (ECG).
The maximum heart rate is predicted for each individual prior to beginning the test. This is easily calculated by taking the number 220 for men and 210 for women and subtracting the patient’s age. The result is the maximum predicted heart rate. This value is then used as an indicator in determining cardiac function throughout the test. A satisfactory heart rate response occurs when the subject is able to reach 85% of the maximum predicted heart rate (Hill & Timmis, 2002). Reaching this value and being able to maintain activity at this level or higher is a positive indication for heart function. Subjects who are unable to reach 85% of the maximum predicted heart rate do not successfully complete the test and this may be an indication of heart conditions or problems.
Blood pressure is another indicator useful in analyzing cardiac function during exercise. A baseline blood pressure value is obtained and used for comparison to later recordings. It is typical to see the blood pressure fall or remain constant during the initial stages of exercise. These changes most often reflect the nervous subject relaxing as the test begins (Hill & Timmis, 2002). As exercise levels increase, the systolic blood pressure should also rise. Normal blood pressure range should not exceed 225 mm Hg; however, athletes tend to have higher readings (Hill & Timmis, 2002). The diastolic blood pressure should fall slightly. The increase in systolic and slight decrease in diastolic is a normal adaptation that should occur to help the body reach and maintain its maximum heart rate. Signs that indicate abnormal cardiac functioning can be seen if the systolic drops 20 mm Hg, increases to greater than 300 mm Hg, and/or if the diastolic rises to 130 mm Hg or higher (Hill & Timmis, 2002).
The other significant marker to analyze is the ECG. Resting ECGs are recorded as baseline data and used for comparison, along with ECG recordings of a subject during hyperventilation. These readings will be useful data to compare to the ECGs obtained during exercise. In order to successfully identify the critical changes that occur in the ECG, it is imperative to have a good understanding of a normal ECG and what it indicates. A representation of a normal ECG highlighting the significant points referenced in this investigation is shown in Figure 1.
Figure 1. Significant points in a normal ECG. (Jenkins & Gerred, 2002)
The ST segment is a strong indicator of cardiac functioning. Normally, the ST segment has no elevation or depression. An elevated ST segment is an indicator of an acute myocardial infarction, left bundle branch block, or acute pericarditis (Jenkins & Gerred, 2002). However the ECG of an athletic individual may show an elevated ST segment indicating a normal variation that occurs in an athletic heart (Jenkins & Gerred, 2002).
During exercise it is normal to see the J point become depressed, and the maximum depression occurs at peak exercise levels (Hill & Timmis, 2002). As a result of the J point depression, the ST segment slopes up. Other ECG changes that are characteristic of exercise include P wave height increase, R wave height decrease, shortened QT interval, and T wave height decreases as illustrated in Figure 2.
A depressed ST segment is a sign of myocardial ischemia, digoxin effects, ventricular hypertrophy, acute posterior myocardial infarction, pulmonary embolus, and left bundle branch block (Jenkins & Gerred, 2002). In exercise testing, a depressed ST segment is the “most reliable indicator of exercise-induced ischemia” (Hill & Timmis, 2002). Other ECG changes that are abnormal during exercise include an ST elevation of greater than 1 mm, especially if there are no Q waves present (Hill & Timmis, 2002).
During the test, physicians should look for any signs that the body can no longer adapt to the exercise. They may decide to stop the test because of pain, fatigue or other symptoms, or the physician may choose to stop the test because of some abnormal findings, or a predetermined end-point was reached (Robergs and Keteyian, 2002).
Limitations in Exercise Testing
The specificity of ST segment depression as main indicator of myocardial ischemia is limited. ST segment depression has been estimated to occur in up to 20% of normal individuals on ambulatory ECG monitoring. There are causes of ST segment changes apart from coronary artery disease. If the resting ECG is abnormal, the usefulness of an exercise test may be limited (Hill & Timmis, 2002).
Other limitations affect the use of exercise testing to learn about the limits of physiological adaptation. These stem from the fact that as the body tries to adapt outside the limits of normal functioning, the patient is at risk for major harm and even death. In clinical practice, patients rarely exercise the full duration of the exercise test. The test is usually stopped after the patient reaches 85% of maximum predicted heart rate. This is a satisfactory stopping point during a clinical exam; however it appears to hinder the study of what happens when the body is pushed past the point of normal adaptation, at least in humans.
The use of human simulators in healthcare education is rising. Simulation is an effective teaching tool for clinical practice at the graduate and undergraduate level particularly to develop critical thinking skills (Rauen, 2001). It is a beneficial way to allow students to work in “lifelike situations that provide immediate feedback about questions, decisions, and actions” (Issenberg et al., 1999). These benefits also can be utilized for students studying health sciences beyond just the clinical aspects of medicine. The experiment described in this paper strives to take simulation one-step further and promote the use of simulation at the undergraduate level for investigative purposes. Using simulation in this manner allows students to ask questions, conduct experiments, and get immediate answers that they are able to visualize, ultimately bringing their books to life.
In this experiment simulation is ideal for exercise testing because the objective is to be able to see and analyze what physiological adaptations occur when the body is put under the extreme stress of exercise. This cannot be done with humans because of ethical concerns that arise when a subject’s health is put at risk. Only the simulator allows students to stress the human body to the max and be able to watch the natural adaptations that will occur. Simulation is critical to this experiment because here, exercise testing is not being used for diagnostic purposes, but to see how the body would adapt to this extreme environment. The simulator allows students to “integrate basic and clinical science in a risk-free environment” (Friedrich, 2002). Using human subjects would be less beneficial because as the subject gets to increased levels of stress, they may not be able to tolerate the stress and the test will be discontinued. This will hinder students from being able to observe how the body adapts to extreme stress, which is the goal of this experiment.
In doing this experiment it is hypothesized that the healthy individual will be able to adapt to the stress of the exercise test. The diseased individual will only be able to adapt for a limited amount of time before he is unable to tolerate the stress of exercise. The diseased individual will show abnormal changes in ECG, heart rate, and blood pressure.
This original investigation was designed and conducted by the authors in partial fulfillment of the course “Physiological Adaptation to Space Flight and other Extreme Environments,” (HEST255 in the Human Science Curriculum of the School of Nursing and Health Studies). The human simulation data were obtained using the METI® version 6 human patient simulator (Medical Education Technologies, Inc., Sarasota, FL) located in the Human Simulation Center within the School of Nursing and Health Studies at Georgetown University in Washington, D.C.
This experiment’s goal is to study physiological responses to exercise testing. In order to see maximum results, it became ideal to look at both a healthy individual and a diseased individual. Using the METI® Human Patient Simulator (HPS) METI® profiles of Standard Man and Truck Driver were selected as study participants). Published literature was searched to find the appropriate reference data on human subjects who could be matched with the selected METI® human stimulation profiles. Two reference subjects were chosen. The profile of the subject chosen to fit with Standard Man is a 55-year-old executive. He is 182 cm tall and weighs 80 kg. His predicted maximum heart rate is 165 beats/min. He has complaints of decreased exercise tolerance, weakness, fatigue, and mild dyspnea after jogging (Wasserman, et al, 1994). He denies chest pain, syncope, palpitations, coughing, or wheezing. For ten years he smoked half a pack of cigarettes per day, but currently only smokes 3 or 4 cigarettes per week. He does not take any medications and after physical examination his ECG was normal during rest.
The profile chosen to fit with Truck Driver is a 58-year-old man with coronary artery disease, previous exposure to asbestos, sandblasting, and 35 pack years of cigarettes. He is 173 cm tall and weighs 82 kg. His predicted maximum heart rate is 162 beats/min. He has grinding chest pain starting in the midback and radiating around the left chest to the substernal area. The pain occurs when the subject walks on cold days, and is alleviated by resting for a few minutes. He denies shortness of breath. Physical examination reveals a resting ECG within normal limits (Wasserman, 1994).
The reference data that was used to set up the HPS to study physiological adaptation to exercise in this experiment came from a previous study in which individuals performed exercise on a cycle ergometer. The test protocol required subjects to pedal at 60 rpm without additional load. After three minutes the work rate was increased 20 Watts each minute until any pathologies were seen. The heart rate was recorded every three minutes and this reference data (Wasserman et al., 1994) was used in the simulation experiment reported here.
When performing an experiment using simulation it is necessary to choose a variable that, when manipulated, will put the simulator in a scenario that mimics the desired environment. Varying the heart rate (HR) causes the rest of the simulated body to adapt in a way that is similar to adaptations during exercise. Increasing the simulator heart rate over time intervals mimicks the natural heart rate increase observed in the study of exercise on the cycle ergometer. After controlling the heart rate, the body’s adaptation to the stress of exercise was measured in the current investigation by observing the ECG, blood pressure (BP), respiratory rate (RR), and the percentage of oxygen in the blood (PO2).
Before beginning exercise simulation, it was necessary to record baseline vital signs for the two study participants, Standard Man and Truck Driver. This included HR, BP, RR, PO2, and ECG. Ideally a print out of the resting ECG would have been produced; however, the necessary equipment was not available and in this experiment, all significant ECG changes were noted as they developed and their presence indicated in the investigational record. In addition, each study participant’s maximum predicted heart rate was calculated and recorded.
After recording baseline data, the simulator was programmed to increase to specific heart rates at three-minute intervals. Data was recorded at each interval and is reported in the Results section. The test continued until the maximum predicted heart rate was successfully surpassed or until all ECG changes and data indicated an inability to adapt.
Maximum Predicted Heart Rate (MPHR): 165 bmp
85% percent of Maximum Predicted Heart Rate: 140 bpm
|Heart Rate||Blood Pressure||Respiratory Rate||PO2||ECG Change|
|12||163||113/69||13||98||Successfully achieved MPHR with only minor ECG change|
Table 3. METI® human simulator physiologic values observed for Standard Man
Maximum Predicted Heart Rate: 162 bmp
85% percent of Maximum Predicted Heart Rate: 138 bpm
Table 4. METI® human simulator physiologic values observed for Truck Driver
|Heart Rate||Blood Pressure||Respiratory Rate||PO2||ECG Change|
|6||112||182/114||15||95||Depressed ST segment at HR of 132 (before MPH)|
|9||143||169/121||15||95||Depressed ST with Pre-ventricular contractions (PVCs)|
|10||146||183/123||15||94||The final rhythm continues to be that of myocardial ischemia|
Sample pictures of abnormal ECG changes are shown in Figure 2.
Prior to the experiment, it was hypothesized that the healthy individual would be able to adapt to the stress of the exercise test, yet the diseased individual, although he may initially show signs of normal adaptation, would show abnormal physiologic changes as the stress increased. From the results of this experiment, it can be concluded that the hypothesis was valid.
As the heart rate of Standard Man was increased, there were no major changes to any other vital signs indicating that he could successfully adapt to the simulated environment. He successfully achieved his maximum heart rate without any changes to his ECG that would indicate the need to stop the test. He adapted to the stress by becoming tachycardic, which demonstrates that a healthy individual can sustain body function under this level of stress.
As the heart rate of Truck Driver was increased, he was able to sustain normal function only for the first six minutes. After this point, the influence of his health history became significant and his ability to adapt weakened. At a heart rate of 132 bpm, which is less than 85% of his MPHR, his ECG showed a depressed ST segment. This is indicative of exercise-induced ischemia (Hill & Timmis, 2002). As the heart rate was increased further and therefore the rhythm became more tachycardic, it became increasingly difficult to distinguish between a depressed ST segment and a PVC. Although PVCs could not be clearly identified at the higher heart rates, hidden PVCs did occur. Because this was a simulation experiment, it was possible to reanalyze the rhythm at a slower rate. This reevaluation showed that although difficult to see at increased heart rate, PVCs were in fact present. This is yet another indication of his inability to adapt.
Although the experiment progressed as expected and predicted changes occurred, one abnormality was noted in the data. The RR remained constant despite the increased level of stress for both subjects. It was expected that as the oxygen demand of the body increased, as a result of the increased heart rate, the RR would increase as an adaptation. When this unexpected result was recognized, a repeat experiment was performed to look for a possible explanation for this finding. Upon reevaluation, it became apparent that the simulator compensated for the increased oxygen demand by increasing his tidal volume. The amount of air inspired increased as the stress level increased. This allowed him to increase his oxygen consumption while maintaining his resting RR.
The simulator demonstrated that health of an individual has an effect of their ability to overcome the stress of external environments; however, there are limitations to using a simulator. A simulator is unable to portray external physiologic changes. Effects of exercise such as perspiration, fatigue, and pain are not visible during simulation and the roles that they play in adaptation are absent from the data of this experiment.
Simulation proved very useful in looking at the body’s adaptation to the stress of exercise. This can be generalized in the future to look at the body’s adaptation to other extreme environments. It has been difficult thus far to study physiologic adaptation in some of these environments using actual human participants because there is a potential to cause harm to participants in such a study. Simulation seems to be an answer to this ethical dilemma. It allows students and researchers to go beyond the boundaries faced when working with humans. Additionally, simulated studies can look at the adaptation of a wide range of patient profiles to form unbiased comprehensive studies.
This study in particular is significant to those who cannot tolerate exercise stress. The information in this experiment can be used when developing exercise programs for non-healthy individuals. It is often recommended that people with cardiac disease incorporate exercise into their life to avoid more serious complications. Healthcare providers should be knowledgeable of the limits of such individuals and develop exercise regimens in conjunction with these limitations.
The authors express their gratitude to Lashaun Newton for his help in understanding the operation of the METI® human simulator and for his assistance in programming the simulator for use and in analysis of the data during this investigation.
Standard Bruce Protocol
|Stage||Time (min)||Km/hr||Tread Mill Slope|
Figure 2 ECG Patterns
METI® Human Patient Simulator Profiles
Age and Gender: 33-year old, male.
History of Present Illness: Healthy adult
Past Medical History: None
No known drug allergies
Denies tobacco, alcohol, and IV drug use
Runs two miles several times a week
General: Healthy adult male, average build, in no distress
Weight, Height: 70 Kg, 6’0’’
Vital Signs: HR: 73 bpm, BP: 113/52 mmHg, RR 13 br/min, PO2 97%
Age and Gender: 61-year old, male.
History of Present Illness: Complains of chest pain
Past Medical History:
Allergy: pennicillins cause hives and wheezing
Essential hypertension for 20 years
Coronary artery disease: MI five years ago with stable angina since, estimated EF of 37%
COPD: long history of cigarette smoking, chronic cough, dyspnea on exertion
Tobacco Use: two and a half packs of cigarettes a day for 41 years
Alcohol Use: Drinks two to four beers each night, Denies DTs, Denies IV drug use.
General: Healthy adult male
Weight, Height: 70 Kg, 6’0’’
Vital Signs: HR: 76 bpm, BP: 180/97 mmHg, RR 14 br/min, PO2 94%