please this paper is for my exercise science class i need you to pick a topic and write my research paper on it .
this is an example of the paper i need you to write
Running
Header: Predicting Professional Road Cycling Performance
Predicting Professional Road Cycling Performance
Hugo
M. Zambrano
CUNY Lehman College
The Grand Tours
is bicycle road racing’s version of the Triple
Crown. Composed of the Giro d’Italia, Tour de France and the Vuelta a Espana
these events are stage races. Held annually in July, the Tour de France (Tour)
is the oldest (it started as a publicity stunt in 1903 between 2 rival sporting
newspapers), and most prestigious of the
three. Today’s Tour is one of the most widely attended events in sports with
millions lining the race route, and an advertising/ sponsor caravan following
the riders. The race is made up of 3
stage types: flat terrain (FT), high
mountain (HM), and time trial (TT).Today, roughly 200 cyclists compete in the Tour annually, with the winner covering
3650 ± 208 km in 92 ± 6 hours on average (data from 1990 to 2011) (Santalla,
Earnest, Marroyo & Lucia, 2012). Speeds
of 40–60 km/h reached on flat terrains are often done in a group, known as a
peloton, which demands high technical skills. TT experts are fast, maintaining
speeds of about 50 km/h without drafting, which requires high, sustainable
power output and aerodynamics to be successful. Finally, the HM stages often include
several steep mountain
passes, demanding several
bouts of 45 minutes or more at high intensities (Santalla et
al., 2012). Because of the nature of the course and the differing ambient
conditions encountered daily, only the most elite endurance athletes compete in
the Grand Tours. Pro cyclists ride an average of 35,000km and compete 90 days
per year. This pushes the athlete to the extremes of endurance performance as
they must be resistant to fatigue at high submaximal intensities for extended
periods of time. The years of training required to acquire this adaptation is
unknown, however there are certain widely recognized determinants of cycling
performance. Furthermore there are numerous tests that are used to determine the
cyclist’s training status, and to predict performance. Accordingly, the purpose of this
paper will be to discuss the top five determinants of
performance for professional road cyclists: gross efficiency, VO2max and
certain metabolic thresholds (including blood lactate landmarks), peak power
output (PPO), and breathing patterns
(Faria, Parker, & Faria, 2005). The different types of tests used to
predict performance will also be discussed, with an emphasis on a new submaximal test developed by Lamberts, Swart, Noakes, & Lambert (2011).
Gross Efficiency
GE
measures effective work and is expressed as a percentage of total energy
expended that results in external
work. In well-trained male cyclists, GE was reported
as ranging from 10% to 25%. GE for Tour winners hovers in the 25% range
(Bell, Furber, Van Someren, Anton-Solanos,
& Swart, 2017). Interestingly, it has been found that as intensity rises,
the rate of VO2 rise slows and that the GE of pro riders actually goes up.
– the theory being that the higher GE accounts for the
slower rise in VO2max (Lucia, Hoyos, Perez, Santalla, & Chicharro, 2002).
GE has also been found
to have a positive
correlation with the percentage of type 1 muscle fibers in the vastus
lateralis. Working muscles with a higher percentage of type 1 muscle fibers
have a lower submaximal oxygen cost, resulting in a higher GE (Faria et al.,
2005).t , This adaptation allows for
extremely high workloads to be continued for extended periods of time.
VO2max and Metabolic Thresholds
Successful pro cyclists have high VO2max values in the 74ml/kg
range. On its own, VO2max is not considered a strong
predictor of performance in elite cyclists.
However, when coupled with other markers of exercise
performance such as blood lactate, power output, metabolic thresholds and
efficiency, VO2max gains predictive validity (Wilber, Zawadzki, Kearney,
Shannon, & DiSalvo, 1997). Padeilla, Mujika, Cuesta, & Goiriena,
(1999), is one of the few studies that has grouped riders into fields of
expertise (i.e. uphill, flat, TT specialists), and compared their physiological
attributes with regards to blood lactate levels. Lactate threshold 1 (LT1) is
the exercise intensity that causes a 1mmol/L increase in blood lactate
concentration. LT1 is also closely associated with the ventilatory threshold
and in practice is the point of hyperventilation with regards to VO2 (breathing will be discussed further in the “breathing” section.
Using a graded cycling ergometer step test protocol (35-W increments
every 4 minutes to exhaustion) the authors demonstrated that the power
associated with 4 mmol/L blood lactate concentration is highly predictive for
the maximal effort that the athlete was able to sustain for 1 h during the Hour
Record performance (subject was a multiple Tour winner and record holder).
Power output at lactate threshold has been shown to be a valid predictor
(r=.88) of cycling potential. For this subject, the power recorded at 4 mmol/L blood lactate concentration was 505 W
(6.23 W·kg), which is an extremely high value exhibited by only the most elite
riders (see peak power output section) (Faria et al., 2005). Furthermore, in an earlier study, Coyle et
al.(1991), showed that lactate threshold VO2 has a strong correlation (r=.96)
with performance of trained cyclists.
Peak Power Output (PPO)
Peak power output
(Wpeak) can be defined as the highest workload sustained for a given amount of
time during a progressive incremental ramp test to exhaustion, and is usually
expressed relative to body mass (Faria et al, 2005). Research shows that Wpeak values
during a maximal incremental cycling test are highly predictive of cycling
performance. Hawley & Noakes (1992),
found a significant correlation (r = -.91, p 5.5W/kg is considered a prerequisite for pro cyclists,
with the most elite cyclists
exhibiting a PPO of
≥ 6.34W/kg.
Breathing Patterns
Ventilatory
efficiency (Ve), is the interaction
between pulmonary ventilation,pulmonary perfusion and cardiac output.
Professional riders exhibit unique breathing patterns at high workloads whereby
instead of a tachypnoeic shift (the onset of hyperventilation and a good
indicator of lactate threshold), Ve continues to improve via an increase in
tidal volume instead of breath frequency. This breathing adaptation results in
enhanced breathing efficiency and metabolic cost of breathing. Because the
oxygen cost of breathing is approximately 15% of VO2max, this characteristic
may account for the VO2 kinetics
of pro cyclists. It has also been shown that heavy breathing during exercise can potentially
compromise leg blood flow; therefore a more efficient breathing pattern may
reduce this effect (Lucia, Caravajal, Calderon, Alfonso, & Chicharro, 1999).
Tests to Predict
Cycling Capacity
The two most common
tests used to predict cycling
capacity are the Peak Power
Output (PPO) test (discussed earlier), and the 40km Time Trial (TT) test. However,
the maximal and high
submaximal nature of these tests make them unsuitable for weekly monitoring as
it could cause interference with normal training and race schedules. To address
this shortcoming submaximal tests such as the
Astrand Test and the Physical Work Capacity 170 Test were introduced
beginning in the 1950s. These tests however focused on VO2 max, and as
discussed earlier, on it’s own VO2max has limited accuracy to determine small
meaningful performance changes in well trained cyclists (Capostagno, Lambert,
& Lamberts, 2016).
Lambet et al., (2011), developed the Lambert and Lamberts
Submaximal Cycle Test (LSCT) that allows for a wider
range of variables such as power,
cadence, cycling efficiency
and heart rate recovery to be analyzed simultaneously. This strategy was
developed to not only predict performance, but also to monitor changes in
training status and detect
symptoms of overreaching. The LSCT is a 17 minute test that can be
used as a warm up to traditional tests (indeed in their initial study the
researchers conducted the PPO & 40km TT immediately after the LSCT). The
authors had 17 well trained male pro cyclists (subsequent research by the same
lead author confirmed results using a much larger sample of 102, (Lambert 2014)),
cycle at intensities which elicited target heart rates of 60% (stage 1), 80%
(stage 2), and 90% (stage 3), of their maximal heart rate. HR max, heart rate
recovery (HRR), power, speed, cadence and
RPE were all recorded at appropriate intervals or
continuously where possible. Additionally, there is a lower typical error of
measurement (TEM), in some of these variables making the LSCT more reliable.
The researchers state that the LSCT is an excellent predictor of performance for a number
of reasons. First
it is a true submaximal test (as defined by the stages
and time to complete. Secondly, the performance parameters of the test are highly
repeatable with very low TEMs for mean power output, mean speed and heart rate
recovery (HRR) – which is a marker of autonomic function and can be used to
adjust training prescription and determine training status. Lastly, there is a
very strong correlation between mean power and heart rate recovery during LSCT
and peak power (PPO) and endurance (40km TT), especially during stage 3 (90% of
HRmax), (r=.91-.94) (Lamberts et al., 2011).
Summary and Practical Takeaways
In conclusion, Grand Tour
road cycling races represent the pinnacle of the sport and push athletes to
extreme limits. Traditional tests/ measurements are impractical to conduct
frequently because they interfere with training and race schedules. The more
frequently a test can be performed, the more data it can provide. The LSCT, a
true submaximal test addresses these issues and does not require invasive blood
samples to test lactate levels or costly gas analyzers to monitor respiration
to determine cycling efficiency. Lastly, the LSCT can be used as a standardized
warm-up before traditional performance tests or training sessions, and has the
potential to monitor performance and fatigue with enough precision
to detect small but meaningful performance changes.
References
Bell, P. G., Furber, M.
J. W., Van Someren, K.
E. N. A., Anton-Solanas,
A. N. A., & Swart, J. (2017).
The Physiological Profile of a Multiple Tour de France Winning
Cyclist. Medicine & Science in Sports & Exercise, 49(1). https://journals.lww.com/acsm-msse/Fulltext/2017/01000/The_Physiological
_Profile_of_a_Multiple_Tour_de.14.aspx
Capostagno, B., Lambert, M. I., & Lamberts, R. P. (2016). A Systematic Review of Submaximal Cycle Tests to Predict, Monitor,
and Optimize Cycling Performance. International Journal
of Sports Physiology and Performance, 11(6), 707–714. https://doi.org/10.1123/ijspp.2016-0174
Coyle, E. F., Feltner, M. E., Kautz,
S. A., Hamilton, M. T., Montain, S. J., Baylor, A. M., Abraham, L. D., & Petrek, G. W. (1991). Physiological and biomechanical factors associated with elite
endurance cycling performance. Medicine & Science
in Sports & Exercise, 23(1).
https://journals.lww.com/acsm-msse/Fulltext/1991/01000/Physiological_and_
biomechanical_factors_associated.15.aspx
Faria, E. W., Parker, D.
L., & Faria, I. E. (2005). The
Science of Cycling. Sports Medicine, 35(4), 285–312.
https://doi.org/10.2165/00007256-200535040-00002
Hawley, J. A., & Noakes, T. D. (1992). Peak power output predicts maximal oxygen uptake and performance time in trained cyclists. European journal of applied physiology and occupational physiology, 65(1),
79–83. https://doi.org/10.1007/BF01466278
Lamberts, R. P. (2014). Predicting Cycling Performance in Trained to Elite Male and Female Cyclists. International Journal of Sports Physiology and Performance,
9(4), 610–614.
https://doi.org/10.1123/ijspp.2013-0040a
Lamberts, R. P., Swart, J., Noakes, T.
D., & Lambert, M.
I. (2011). A
novel submaximal cycle
test to monitor fatigue
and predict cycling
performance. British Journal
of Sports Medicine, 45(10), 797 LP – 804. https://doi.org/10.1136/bjsm.2009.061325
Lucía, A., Hoyos, J., Pérez, M., Santalla, A.,
& Chicharro, J.
L. (2002). Inverse
relationship between
VO2max and economy/efficiency in world-class cyclists. Medicine and science in sports and exercise, 34(12), 2079–2084. https://doi.org/10.1249/01.MSS.0000039306.92778.DF
Lucía,
A., Carvajal, A., Calderón, F. J., Alfonso, A., & Chicharro, J. L. (1999).
Breathing pattern in highly competitive cyclists during incremental exercise. European journal of applied physiology and occupational physiology, 79(6), 512–521. https://doi.org/10.1007/s004210050546
Padilla, S., Mujika, I., Orbananos, J., Santisteban, J., Angulo F., & Jose Goiriena, J. (2001). Exercise intensity and load during mass-start stage
races in professional road cycling. Medicine & Science in Sports &
Exercise, 33(5). https://journals.lww.com/acsm-msse/Fulltext/2001/05000/Exercise_intensity_ and_load_during_mass_start.19.aspx
Padilla, S., Mujika, I., Cuesta, G., & Goiriena, J. J. (1999). Level ground and uphill cycling ability in professional road cycling. Medicine
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Santalla, A., Earnest, C.
P., Marroyo, J.
A., & Lucia, A. (2012). The
Tour de France: An
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of Sports Physiology and Performance, 7(3), 200–209.
https://doi.org/10.1123/ijspp.7.3.200
Wilber, R. L., ZAWADZKI, K. M., KEARNEY, J. A. Y. T., SHANNON, M. P., & DISALVO, D.
(1997). Physiological profiles of elite off-road and road cyclists.
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this is the link to the textbook its 1 source that needs to be used
Final Paper Rubric: Total – 110 points
Format: 10 points
3-5 pages (double-spaced) minimum
Grammar, Punctuation, Spelling must be up to par
12 size Font, Times New Roman
Page Numbers – Headers or Footers
MUST have these sections and they need to be labeled Title/Cover Page, Introduction, Body of the Paper (Arguments/Supporting Points), & Conclusion/Practical Considerations/Applications, Reference List
Purpose Statement: 10 points
The student has stated the purpose of the paper in the introduction section of the paper:
For instance: “The purpose of the study is to discuss the positive effect that resistance training provides for the obese population…” (What is obesity? What type of exercises can combat obesity?
or “The purpose of the study is to demonstrate the importance of exercise in the elderly population” (why do the elderly need exercises? to delay the rate of sarcopenia? What is that?)
Body of the Paper/Quality: 60 points
Whatever you choose to write about throughout the paper; the points must flow from one to the other.
Obviously, I will be looking for quality amongst the writing/sentences/paragraphs as everything must make sense.
Reference List/In-Text Citations: 25 points
The Reference list should be the last section of your paper (after the conclusion)
Outside sources should come from
https://pubmed.ncbi.nlm.nih.gov
https://scholar.google.com
https://www.acsm.org/education-resources/journals/exercise-sport-scieces-reviews
Must have at least 5 peer-reviewed sources (Not only the Textbook!)
