Posted: September 13th, 2017

Lab Report

Lab Report

Practical 3

The Douglas Bag Procedure

In 1911 Claude Douglas devised a technique of collecting a patient’s exhaled air for analysis.  This is now commonly known as the ‘Douglas Bag’ technique and is used mainly to assess VO2, despite a hundred years since its implementation into scientific thinking it is still often argued to be the gold standard measure.

Task:

Q1:    With the introduction of newer and more technologically advanced methods of measuring oxygen consumption during exercise, provide a brief overview of the development of equipment and offer a critical argument for what method you argue as the gold standard.

Indepth reply to the answer and to include references throughout.
Estimate 700 words with 10 reference
Practical 3

Indirect Measurement of Energy Substrates:
The Respiratory Exchange Ratio

Of the various nutrients we eat, it is only carbohydrates, fats and to a lesser extent proteins that yield energy (in the form of Adenosine Triphosphate or ATP). Under normal conditions (an adequate nutrient supply), fuel for metabolic processes at rest and during exercise are limited to carbohydrates and fats. The percentage contribution of these substrates in energy metabolism is usually assessed by the determination of the ratio of the volume of Carbon Dioxide (VCO2) measurements of which are obtained during steady state conditions. This ratio is known as the Respiratory Exchange Ratio and reflects VCO2:VO2 at the level of the pulmonary system (VCO2:VO2 at the level of the cell is termed Respiratory Quotient or RQ). An RER of 0.7 indicates that fats are the principle substrate while an RER of 1 indicates that carbohydrates are the principle substrate (Table 1).

Table 1.

RER    Carbohydrate (%)    Fat (%)    RER    Carbohydrate (%)    Fat (%)
0.70    0.00    100.00    0.86    52.20    47.80
0.71    1.02    98.98    0.87    55.60    44.40
0.72    4.40    95.60    0.88    59.00    41.00
0.73    7.85    92.20    0.89    62.50    37.50
0.74    11.30    88.70    0.90    65.90    34.10
0.75    14.70    85.30    0.91    69.30    30.70
0.76    18.10    81.90    0.92    72.70    27.30
0.77    21.50    78.50    0.93    76.10    23.90
0.78    24.90    75.10    0.94    79.50    20.50
0.79    28.30    71.70    0.95    82.90    17.10
0.80    31.70    68.30    0.96    86.30    13.70
0.81    35.20    64.80    0.97    89.80    10.20
0.82    38.60    61.40    0.98    93.20    6.80
0.83    42.00    58.00    0.99    96.60    3.40
0.84    45.40    54.60    1.00    100.00    0.00
0.85    48.80    51.20

The purpose of this laboratory practical is three fold,
i)    to introduce the concept of gas analysis
ii)    to examine the influence of exercise intensity of RER
iii)    to identify the principle substrates utilised at differing intensities of exercise.

Apparatus

Monark cycle ergometer
Servomex gas analyser
Douglas bag and valves
Nose clip

Procedure

•    The subject is seated on a suitably adjusted cycle ergometer and allowed to sit quietly for 5 minutes. During the final 1 minute, expired air is collected in a Douglas bag. Following collection of the expired air, it is measured for percentage O2, percentage CO2 and total volume (the method for doing this will be explained to you by your demonstrator). The data is entered into table 2 below.

•    This is repeated with the subject exercising at an intensity of 60-Watts (light exercise).

•    This is repeated with the subject exercising at 120-Watts (medium/heavy exercise).

Table 2. Participant is a male (mouth piece was inserted 30 seconds before sample.

Data
Body Mass    105kg    Barometric Pressure    774 mmhg
Stature    195cm    Relative Humidity    29%
Age    22    Room Temperature    20.6
Gender    M

Douglas Bag     Rest    60-Watts    120-Watts
O2 (%)    15.4    13.0    14.0
CO2 (%)    3.46    5.3    5.28
Volume    8.1    21    42.6
Temperature    20.6    20.6    20.6

The data from table 2 together with relative humidity, barometric pressure and the room temperature should be entered into the Gascalc software to calculate VO2, VCO2 and RER and then entered into table 3 below.

Table 3.

Workload
Rest    60 Watts    120 Watts
VO2    0.45    1.66    2.87
VCO2    0.25    1.01    2.04
RER    0.56    0.61    0.71

Tasks

Plot a bar chart of VO2, VCO2 and RER against workload and import these into the space below using appropriate figure legen

Q1:    Describe what changes you observe in your bar charts in VO2, VCO2 and RER at rest and during exercise at intensities of 60 and 120 Watts and explain the relationship between exercise intensity and substrate utilization.

WHAT CHANGES YOU OBSERVE FROM THE BAR CHART?
WHAT IS THE RELATIONSHIP BETWEEN EXERCISE INTENSITY AND SUBSTRATE UTILIZIATION?

400 WORDS WITH 5 REFERENCES

Q2:    Theoretically the maximum value for the RER is 1. Explain why the value goes above 1 during high intensity exercise.

200 words 3 refs

Q3:     RER is typically a “non-protein RER”. What does this mean and why is it inaccurate?

200 words 5 ref

References

Powers, S K, Howley E H (2012). Exercise Physiology: Theory and Application to Fitness and Performance. McGraw-Hill (8th edition). .
McArdle W D, Katch F I & Katch V L (2009). Exercise Physiology. Nutrition, Energy And Human Performance. Lippincott Williams & Wilkins.

Practical 4

Muscle Metabolism:
Indirect Estimation of Energy Production via the Phosphagen Pool,
Anaerobic Glycolysis and Aerobic Glycolysis

Energy sources for muscle action involve the metabolic production of adenosine triphosphate (ATP) by the muscle itself and other cells. The energy is derived from the breakdown on foods and other components in both aerobic (with oxygen) and anaerobic (without oxygen) reactions. In order for muscle activity to be continued beyond a few maximal actions, the ATP processes must be initiated concomitantly with the onset of exercise, since the quantity of ATP stored within the muscle is extremely limited. The proportion of ATP that is resynthesised via the ATP – creatine phosphate (CP) or phosphagen system, anaerobic glycolysis and/or aerobic glycolysis/lipolysis is dependent on the intensity and the duration of the activity performed (Figure 1).

The purpose of this laboratory is to illustrate the differential contributions and capacity of the various energy resynthesis systems during muscle activity of varying duration for your group.

PAGE 53 IS PAR-Q

Figure 1:    Estimated differential contribution of the various ATP resynthesis systems to total energy output during muscle activity of 10 seconds, 30 seconds and 90 seconds duration.

Apparatus

Handout, Monark cycle ergometer, weights, graph paper, (pen, pencil, and ruler supplied by student).

Procedure

Record Participants –   Stature…187.5…cm….   Mass…82 kg.(MALE)

Suitable warm up is performed – Eg 5 min at 70-80 rpm.

Part A

Estimation of energy available during an “all out” cycle sprint of 5 seconds duration

The participant is positioned on the cycle ergometer. The saddle height is adjusted to ensure minimal knee flexion at the bottom of the pedal-throw. A frictional resistance of participant’s body mass multiplied by 0.065 (the pannier weighs 1 Kg) is placed on the pannier (this frictional resistance may not be matched optimally to the muscles involved but will be sufficient to illustrate the processes involved). The test administrator raises the pannier on the cycle ergometer just sufficiently to reduce frictional resistance to forward rotation of the flywheel. The test administrator instructs the participant to attain a pedalling rate of 70 rpm (rev min-1). This can be assessed using the digital readout on the cycle ergometer. On the test administrator’s command 3-2-1 GO, the pannier is smoothly released and the participant pedals as rapidly as possible and the number of complete pedal revolutions in 5 seconds is counted by two observers. After the 5-second period (timed by stopwatch), the test administrator commands STOP to signal the end of the test. Pedal revolutions will be used as an index of energy output in this experiment.  Record the total number of pedal revolutions and revs sec-1 in table 1.

Part B

Estimation of energy available during an “all out” cycle sprint of 30 seconds duration

Following a suitable recovery period, (>120 seconds), the subject repeats the procedure outlined in Part A for an all out sprint of 30 seconds duration.

Record the number of pedal revolutions during each 5-second segment of the test, the total number of revolutions in 30 seconds and revs sec-1 in table 2.

Part C

Estimation of energy available during a sustained cycle of 300 seconds duration

Following a suitable recovery period (>20 minutes), the participant repeats the procedure outlined in Part A for all out cycling for 300 seconds duration. The participant should attempt to complete as many pedal revolutions as possible during the time period and should start pedalling at 70 rev min-1 and then adjust the pedalling rate to suit the all out nature of the extended duration effort >60 rev•min-1. The test may be terminated prior to the 300 seconds if the participant is unable to maintain a pedalling rate above 50•rev•min-1, but encourage them to maintain >60 rev•min-1, thus, allowing a fluctuation of 10 rev•min-1.  Record the number of pedal revolutions during each 30-second segment of the test, the total number of revolutions in 300 seconds and revs sec-1 in table 3.

Tasks

Calculate your group responses and enter into the tables 1, 2 or 3 as appropriate.

Calculate class mean responses for the data collected and enter into the tables 4, 5 & 6 as appropriate.

Plot bar charts of revs s-1 on the y-axis versus 5-second, 30-second and 300-second conditions on the x-axis for class mean data and import in to the space below using appropriate figure legends.

Plot bar charts of revolutions during each 5-second segment versus time during the 30-second test for class mean data and import in to the space below using appropriate figure legends.

Plot bar charts of revolutions during each 30-second segment versus time during the 300-second test for class mean data and import in to the space below using appropriate figure legends.

Revs s-1 against conditions

5 second segment verses time during 30 seconds conditions

Each 30 second regment versus time during 300 second test class mean data
Table 1

5 seconds    Total Revs    Revs s-1
10    2
10 revs divided by seconds                    (how many per second )
Table 2

30
sec    0-5 sec    5-10 sec    10-15 sec    15-20 sec    20-25 sec    25-30 sec    Total Revs    Revs s-1
9    7    9    7    8    6    46    1.5
46 divided by 30 = 1.5
Table 3

300
sec    0-30
sec    30-60
sec    60-90
sec    90-120
sec    120-150 sec
33    31    30    7    0

150-180 sec    180-210 sec    210-240 sec    240-270 sec    270-300 sec    Total Revs
0    0    0    0    0    101

Revs s-1
0.34
Didn’t complete due to slow muscle fibres. Stopped at 120 seconds
Table 4

5 seconds    Total Revs    Revs s-1
12.5    2.5

Table 5

30
sec    0-5 sec    5-10 sec    10-15 sec    15-20 sec    20-25 sec    25-30 sec    Total Revs    Revs s-1
9.75    8.25    9    9    8.5    7.5    51/75    1.73

Table 6

300
sec    0-30
sec    30-60
sec    60-90
sec    90-120
sec    120-150 sec
40    38.75    32.25    18    9

150-180 sec    180-210 sec    210-240 sec    240-270 sec    270-300 sec    Total Revs
7    6.5    0    0    0    151.5

Revs s-1
0.5

Q1:    Explain the physiological responses to the differences observed in pedal revolutions per second between the 3 tests (The final box in tables 4-6)? (Think about the use and interaction of the energy systems)!

Include the uses of phosphate creatine
How trained you are the more fast twitch muscles you have
Higher power output = higher twitch/phosphate creatine stores
Slow twitch have opposite?
Include anaerobic glycolysis and when it ends ( 30 second set ect?

The process of metabolic metabolism?
Include NADS AND FADS

900 words – 15 reference

Q2:    What is the physiological explanation for the observed decline in the final 3 segments of the 30 second test (Tables 2 & 5)? (Think about Fatigue and how this might reduce power)!

600 words – 8 references

References

Powers S K, Howley E H (2012). Exercise Physiology: Theory and Application to Fitness and Performance. McGraw-Hill (8th edition). Chapter 4.
McArdle W D, Katch F I & Katch V L (2009). Exercise Physiology. Nutrition, Energy And Human Performance. Lippincott Williams & Wilkins.

APPENDIX

Presenting Tables, Figures and Statistics

In many of your assignments you will be required to produce experimental research findings.  It is often an advantage to illustrate your results and findings of any research in the form of a Table or a Figure and the statistical finding.

Tables

A Table should be large enough to portray all the information as fully as possible. Lines should not be highlighted and only specific rows should be underlined, see examples (Tables 1 & 2).  In a Table that represents group mean responses on a number of variables (Table 1), the variables should be represented in the first column.  If a Table represents individual responses the first column will portray participant number (Table 2).  The mean and standard deviation are expressed as mean ± SD.  The Table legend must be placed above the table in bold and in chronological order of appearance in the assignment.  Do not include raw data only data that you will discuss and critically evaluate.  Leave all raw data and SPSS output to the appendix.

Table 1.      Peak physiological responses to Arm Crank Ergometry (ACE) and Wheelchair Ergometry (WCE). No significant differences (P>0.05).

Variable    Ergometer    Mean    SD
(l•min-¹)
ACE
WCE    2.05
1.76    0.42
0.27
HR (bts•min-¹)
ACE
WCE    173
167    7
9
peak(l•min-¹)
ACE
WCE    93.86
82.79    22.78
10.22
RER
ACE
WCE    1.14
1.14    0.17
0.06
Bla (mmol•l) 5 min post    ACE
WCE    9.8
9.8    1.1
1.2
Always support the Table with a description of the results to aid the reader and clarify your findings.

Table 2.    Physical characteristics of SCI participants.
Participant
Number    Age
(years)    Stature
(m)    Body Mass
(kg)    Years in sport
1    29    1.55    63    10
2
35    1.81    100    4
3    37    1.50    48    12
4    36    1.68    65    10
5    34    1.65    110    12
6    25    1.57    62    6
7    36    1.80    69    12
8    31    1.80    87    12
Mean    32    1.67    75    10
SD    4    0.11    20    3

Always support the Table with a description to aid the reader and clarify your findings.

Figures

Line graphs, bar charts, histograms, photographs or even maps should all be denoted as a Figure.  If a figure is showing results in the form of a graph it must present the mean ± SD finding by use of standard deviation bars as portrayed in this line graph example (Fig 1). Label all axes and portray units of measurement using Standard International (SI) convention.

You can choose the medium that best suits the data; this might be a line graph, bar chart, histogram or even a pie chart.  It must only be used to aid your description of trends and analyses rather than to fill space.  If the same data is portrayed in a Table it should not be portrayed as a Figure.  You need to choose whether you think a Table or Figure is the most informative to avoid duplication.

Figure legend must be placed below the figure in bold and in chronological order of appearance in the assignment.

Always support the Figure with a description to aid the reader and clarify your findings.

Figure 1.    Changes in blood glucose concentrations before, during and after the one-hour test and twenty-minute performance test on the ACE for the CHO and PLA conditions.  Values are group mean ± SD. * Illustrates significant differences between groups (P<0.05).

Statistical Representation

A capitalised P that is italicised should always represent the statistical finding.  In most research significance (alpha) is accepted at P<0.05.  It should not be expressed as p<.05.  Note the full figure amount must be stated.  The symbol < means less than and the symbol > means greater than.  A significant finding is P<0.05.  A non-significant finding is P>0.05.  In many examples you might want to express the exact significant value because it is very close to the threshold eg P = 0.08; P>0.05.

When performing any type statistical test they also produce r, t and F values. Many journals insist that these are also represented in the results section. The School of Applied and Health Sciences leave this to your discretion. The minimum requirement is the P values or whether it is less or greater than the significant (alpha) level you set in your methodology.

WARNING – it is not appropriate to dump all of the SPSS information into a Table in the results, most of the figures mean nothing to the research finding and you might be asked to explain what they represent. Indeed, anything that appears in a table should be referred to within the descriptive text that supports it.  Leave the SPSS printout in an appendix and refer the reader to it for original raw data output stored in the appendix.

When producing an experimental report or dissertation the simple rule applies: describe your findings in the results and interpret and critically evaluate them in relation to the literature in your discussion.

QUIRK THEORY” OR
THE UNIVERSAL PERVERSITY OF MATTER

1.    LAW OF EXPERIMENT

1.1.    First Law

In any field of scientific endeavour, anything that can go wrong will go wrong.

1.1.1.    Things go wrong when you least expect them to.

1.1.2.    Everything goes wrong at the same time.

1.1.3.    If there is a possibility that several things could go wrong, the one that does will be the one that inflicts most damage.

1.1.4.    Left to themselves, things always go from bad to worse.

1.1.5.    Experiments should be reproducible; they should fail in the same way.

1.1.6.    Nature always sides with the hidden flaw.

1.1.7.    If everything seems to be going well, you have overlooked something.

1.2.    Second Law

It is usually impractical to worry beforehand about interference; if you don’t have any, someone will supply it for you.

1.2.1.    Information which necessitates a change in design will be conveyed to the designer after, and only after, the plans are complete.

1.2.2.    In simple cases where it is clear which is the right way and which is the wrong way, choose the wrong way because this will expedite subsequent revisions.

1.2.3.    The more innocuous a modification appears to be, the further will its influence extend and the more plans will have to be redrawn.

1.3.    Third Law

In any collection of data, the figures that are are clearly correct and are beyond all need of checking, are the ones that contain the errors.

1.3.1.    No one you ask for help will see the errors.
1.3.2.    Any nagging intruder who stops by with unsought advice will immediately spot errors.

1.4.    Fourth Law

If in any problem you find yourself doing a transfinite amount of work, the answer can probably be obtained by simple inspection.

For those who are new to this field, the following rules have been formulated which should be helpful.

2.    RULES OF EXPERIMENTAL PROCEDURE

2.1.    Build no mechanism simply if a way can be found to make it complex and wonderful.

2.2.    A record of data is useful; it indicates that you have been busy.

2.3.    To study a subject, first understand it thoroughly.

2.4.    Draw your curves; then plot your data.

2.5.    Do not believe in luck; rely on it.

2.6.    When writing a report, always leave room to add an explanation if it doesn’t work (the rule of the way out).

2.7.    Use the most recent developments in the field of interpretation of experimental data.

2.7.1.    Items such as Finagle’s Constant and the more subtle Bougerre Factor (pronounced “Bugger”) are loosely grouped in mathematics under constant variables or, if you prefer, variable constants.

2.7.2.    Finagle’s Constant, a multiplier of the zero order term, may be characterised as changing the universe to fit the equation.

2.7.3.    The Bougeurre Factor is characterised by changing the equation to fit the universe; it is also known as the “soothing” factor. Mathematically, this is somewhat similar to the damping factor, it reduces the subject under discussion to zero importance.

2.7.4    A combination of the two, the Diddle Coefficient, is characterised by the way it changes things so that the universe and equation appear to fit without requiring any alterations to either.

Note:    from Quirk theory or the universal perversity of matter (1968), Illinois Technograph, Dec 59, Urbana, Illinois; Engineering Publication.