TRAINING VARIABLES OF THE LIVE LOW-TRAIN HIGH TRAINING MODEL: A METAANALYSIS
Michael David Wortman
B.S., California State University, Sacramento, 2008
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
KINESIOLOGY
(Exercise Science)
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
FALL
2011
TRAINING VARIABLES OF THE LIVE LOW-TRAIN HIGH TRAINING MODEL: A METAANALYSIS
A Thesis
by
Michael David Wortman
Approved by:
__________________________________, Committee Chair
Daryl Parker, Ph.D.
__________________________________, Second Reader
Roberto Quintana, Ph.D.
____________________________
Date
ii
Student: Michael David Wortman
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator
Daryl Parker, Ph.D.
Department of Kinesiology
iii
___________________
Date
Abstract
of
TRAINING VARIABLES OF THE LIVE LOW-TRAIN HIGH TRAINING MODEL: A METAANALYSIS
by
Michael David Wortman
Athletes and coaches are always searching for new training techniques to improve
endurance performance. One of the more popular techniques to enhance endurance
performance has been hypoxic training. Recent meta-analytic studies of the multiple
hypoxic training models have suggested that the live low-train high (LLTH) model has
the most potential to improve endurance performance (Bonetti & Hopkins, 2009).
However, it is unclear how to best achieve the benefits of this form of hypoxic training.
Purpose: To model an optimal LLTH training regimen, a meta-analytic analysis of
current hypoxia research literature was performed. Method: A literature search was
performed on LLTH training studies of trained athletes and coded for the following
variables: training altitude, length of training cycle, frequency of exercise, length of
training session, training intensity, and time to post-hypoxic peak performance, with the
dependent variable as reported exercise performance. Effect sizes (ES) for each variable
on peak performance were calculated using Cohen’s d, utilizing means and standard
deviation from each study. Studies that had fewer than 20 subjects were corrected for
small sample size using the formula supplied by Thomas & French (1986). Scatter plots
for each variable were generated and regression curves of best fit were applied to each
iv
graph using least-squares. The peak of each curve was interpreted as the value of the
variable that would provide the optimal effect. Results: The peak ES for training altitude
occurred between 2500-3000m. The peak ES for training cycle occurred at 15 days. The
peak ES for frequency of hypoxic exercise occurred at 6 days per week. The peak ES for
duration of hypoxic training session occurred at 97 minutes. The peak ES for training
intensity occurred between 60 and 65% of Sea Level VO2max. The peak ES for time to
post-hypoxic performance peak occurred at 8 days. Conclusion: It appears that the LLTH
method of altitude training can optimally increase performance if completed in
approximately 2 weeks with one rest day/week at moderate altitudes and moderate
training intensities.
_______________________, Committee Chair
Daryl Parker, Ph.D.
_______________________
Date
v
ACKNOWLEDGMENTS
Thanks to all of my friends, family, classmates, teammates, colleges, coaches, teachers
and anyone else who I may have missed for all the help you have provided me with not
only this project, but the academic endeavor I have spent the greater part of a decade
working on.
vi
TABLE OF CONTENTS
Page
Acknowledgments........................................................................................................ vi
List of Figures .............................................................................................................. ix
Chapter
1. INTRODUCTION .........………………………………………………………… 1
Purpose of Study ................................................................................................3
Significance of Study ........................................................................................ 3
Limitations ........................................................................................................ 3
Delimitations ......................................................................................................4
Assumptions.......................................................................................................5
Definition of Terms............................................................................................5
Hypothesis..........................................................................................................6
2. REVIEW OF LITERATURE ................................................................................. 7
Changes in Physiology at Altitude.................................................................... 7
Acute ......................................................................................................8
Chronic...................................................................................................8
Training Methods at Altitude ...........................................................................10
LHTH ...................................................................................................10
LHTL ...................................................................................................11
LLTH ...................................................................................................12
Meta-Analysis ..................................................................................................13
Previous Altitude Meta-Analysis .....................................................................15
Summary ..........................................................................................................17
3. METHODOLOGY ................................................................................................18
Journal Article Selection ..................................................................................18
Data Extraction ................................................................................................18
vii
Data Analysis ...................................................................................................19
4. RESULTS ............................................................................................................. 21
Hypoxic Training Altitude ...............................................................................22
Duration of Training Cycle ..............................................................................23
Length of Training Session ..............................................................................24
Average Exercise Intensity ..............................................................................25
Frequency of Exercise......................................................................................26
Time to Post-Hypoxic Peak in Performance ....................................................27
5. DISCUSSION ........................................................................................................28
Training Protocol .............................................................................................28
Comparing Protocols .......................................................................................29
Limitations .......................................................................................................32
Summary ..........................................................................................................33
References ................................................................................................................... 35
viii
LIST OF FIGURES
Page
1.
Effect Sizes for Hypoxic Training Altitude ...................................................... 22
2.
Effect Sizes for Duration of Training Cycle ..................................................... 23
3.
Effect Sizes for Length of Training Session ..................................................... 24
4.
Effect Sizes for Average Exercise Intensity ..................................................... 25
5.
Effect Sizes for Frequency of Exercise ............................................................. 26
6.
Effect Sizes for Time to Post-Hypoxic Peak in Performance ........................... 27
ix
1
Chapter 1
INTRODUCTION
Ever since the 1968 Mexico City Olympic Games (held at 2,260m altitude)
scientists and endurance athletes alike have been intrigued with altitude training. This
was the first time the games had been held at altitude. What brought the attention of
many to altitude training during this event was the dominance of Eastern African athletes,
who lived and trained at altitude. Intrigue grew as these athletes continued to dominate in
middle- and long-distance races even when held at sea level. The conclusion was then
drawn that athletes who lived and trained at altitude had an unfair advantage when racing
at either sea level or altitude (Noakes, 2000).
At higher altitudes, such as where these Eastern Africans reside, the body’s
physiology is slightly altered. With an ascent to higher altitudes, there is a progressive
decline in the partial pressure of oxygen in the ambient air (PIO2). Because of this,
performing at the same absolute workload will elicit a greater relative exercise intensity
when compared to sea level (Mazzeo, 2008). Wehrlin and Hallen (2006) have shown the
effect of hypoxic exposure on performance as a linear relationship, the greater increase in
altitude the greater decline on performance.
The body tries to defend against the stress of hypoxia in a number of ways. Acute
adaptations include adjustments in ventilation (VE) and renal diuresis. These two
adaptations cause secondary effects on body physiology, eventually affecting VO2max
(Parker, 2004).
2
Beyond the acute adaptations, the body needs to adapt in a more long term way
though chronic adaptations. Because O2 is limited, the body then has to become more
efficient in energy production. This is accomplished by increasing the glucose utilization
both at rest and during exercise (Brooks et al., 1992). Beyond this the body also increases
the production of Erythropoietin, which increases the production of red blood cells, to
increase the transportation of O2 through the body to the cells (Tipton, 2006).
With the adaptations that occur with altitude training, which give the possibility
for increasing athletic performance, there have been a few methods produced for utilizing
hypoxic training. There are three major hypoxic training methods: Live High-Train High
(LHTH), Live High-Train Low (LHTL), and Live Low-Train High (LLTH). LHTH
involves both living and training at moderate to high altitude. This was based on the
Eastern Africans’ training, who dominated at the 1968 Mexico City Olympic Games
(Noakes, 2000). The LHTL method requires athletes to live at high altitudes and then
return to a lower altitude where they complete their normal training regimen (Chapman &
Levine, 2007). Finally, the LLTH model has the athletes reside at a lower altitude and
train in a hypoxic environment, either naturally at altitude or artificially with a hypoxic
tent (Wilber, 2007).
There have been two previous meta-analyses that have reviewed the various
hypoxic training methods to determine which has the greatest potential for athletic
improvement. Both came to similar conclusions finding the LLTH method to have the
greatest potential for athletic performance improvement.
3
However, the optimal way to use this method is unclear.
Purpose of Study
The purposes of this study was to gather a comprehensive list of hypoxic training studies
using the Live Low-Train High (LLTH) model, run a meta-analysis, and determine an
optimal training protocol. The protocol included the following variables: training altitude,
duration of training cycle, length of training sessions, frequency of exercise, average
exercise intensity, and time to post-hypoxic peak in performance to elicit an optimal
response in athletic performance.
Significance of Study
Previous research has well documented the effects of hypoxic training, including the
various training models, on increases in performance markers, but it has not been well
documented how individual variables (extent of altitude, length of training cycle,
intensity, etc.) can affect those performance markers, or in what combination can produce
the greatest effect. By analyzing these individual effects, we may be able to gather a
better understanding how each aspect of hypoxic training affects the human physiology.
Limitations
1. Only the studies meeting the inclusion criteria were included into this analysis.
4
2. The results from this meta-analysis can only produce values within the ranges in
existing literature.
3. A meta-analysis combines results from studies with different experimental designs,
known as “comparing apples to oranges.”
4. By using words such as hypoxic, altitude, training and performance, the literature
search may be limited on finding articles pertaining to this meta-analysis.
5. Multiple variables within a shared performance result are being used in comparison,
thus some effect sizes’ (ES) may be drawing from skewed results.
Delimitations
1. The current analysis includes only studies using trained athletes.
2. The current analysis includes only studies using the LLTH model.
3. Since results of the experimental studies were reported in means and standard
deviations, those results were scaled to a standard unit. Since they were scaled to the
standard unit, one can compare multiple results of different research designs as long as
they have the same underlying topic (LLTH).
4. ES will be calculated using Cohen’s d. Standardizing the effect to the SD and lessening
the apples and oranges effect.
5. ES will be corrected for small sample size limiting the inflation of effect.
5
Assumptions
1. The terminology of the studies using LLTH is the same within each other and the
current study.
2. The researchers of the experimental studies included in this analysis used the
appropriate statistical analysis for their data.
3. The corrected effect sizes will correspond to a real effect.
Definition of Terms
VO2: The volume of oxygen consumed by the body to produce energy, usually
represented in absolute terms (L/min) or relative terms (ml/kg/min).
Partial Pressure: The pressure of a specific gas for a given volume of space.
Hypoxia: A decrease in oxygen (O2) availability resulting from a decrease in PO2.
Live Low-Train High (LLTH): Living at low altitude and training at moderate to high
altitude.
Session: Number of minutes required to complete a single exercise bout.
Training Cycle: Number of days included as a grouping of training sessions.
Intensity: A value labeled as the percentage of an individual’s VO2max.
Frequency: The number of training sessions per a 7-day week.
Post-Hypoxic Peak: The point where performance reaches its optimal increase after a
hypoxic training cycle.
Sea Level: Altitude of <1200m.
6
Low Altitude: Altitude of 1200m – 2000m.
Moderate Altitude: Altitude of 2000m – 3000m.
High Altitude: Altitude of >3000m.
Hypothesis (Bonetti & Hopkins, 2009)
H1: A training altitude of 2440m will show to have the greatest increase in performance
while using the LLTH altitude-training model when compared to normoxic training.
H2: A hypoxic training cycle of 18 days will show to have the greatest increase in
performance while using the LLTH altitude-training model when compared to normoxic
training.
H3: 47 minutes of hypoxic training per session will show to have the greatest increase in
performance while using the LLTH altitude-training model when compared to normoxic
training.
H4: A training intensity below an athletes’ anaerobic threshold will show to have the
greatest increase in performance while using the LLTH altitude-training model when
compared to normoxic training.
H5: A training frequency of 6 days per week will show to have the greatest increase in
performance while using the LLTH altitude-training model when compared to normoxic
training.
H6: An optimal post-hypoxic peak in performance will occur at 6 days post hypoxic
exposure while using the LLTH altitude-training mode.
7
Chapter 2
REVIEW OF LITERATURE
Changes in Physiology at Altitude
When discussing the physiological changes when training at altitude there are a
number of factors one should consider. One is the extent of hypoxia incurred. With an
ascent to higher altitudes, there is a progressive decline in the partial pressure of oxygen
in the ambient air (PIO2). Because of this when performing at the same absolute workload
a greater relative exercise intensity will be reached at altitude than at sea level (Mazzeo,
2008).
Wehrlin and Hallen (2006) have shown the effect of hypoxic exposure on
performance to be a linear relationship, the greater increase in altitude the greater decline
in performance. When comparing VO2max between sea level and various altitudes they
found there is a 6.3% decrease in VO2max per 1,000m of altitude achieved. This
reduction of 6.3% in VO2max per 1,000m and the 5.5% decrease in SaO2 per 1,000m,
also reported by Wehrlin and Hallen (2006), fits the conclusion of Powers, Lawler,
Dempsey, Dodd and Landry (1989) that a reduction of 1% in SaO2 below 92-93% causes
a decrease in ~1% of VO2max. Hence, the main mechanism for a hypoxic-induced
decrease in VO2max at moderate altitudes is most likely a decrease in SaO2.
8
Acute.
With exposure to altitude, the body exhibits various adaptations in an attempt to
defend against the drop in SaO2. A few prominent physiological changes occur at initial
exposure to hypoxia including renal diuresis and increased ventilation. VO2max and
performance at altitude may not be directly altered by these two adaptations, but may be
altered by some of their secondary effects.
Renal diuresis will lead to dehydration, which leads to a reduction in plasma
volume, and finally a reduction in SV (Parker, 2004). With stroke volume being a
component of cardiac output (Q), this leads to a reduction of VO2max. This reduction in
VO2max can be detected in altitudes as low as 600-800m (Mazzeo, 2008).
The increase of ventilation will cause a reduction to the partial pressure of CO2 in
the blood. With a reduction of arterial CO2, there is an increase in arterial pH. Trying to
return to homeostasis the kidneys will begin to excrete bicarbonate to return the pH back
to normal. An increase in alkalinity will cause a leftward shift in the oxy-hemoglobin
dissociation curve (Parker, 2004). The shift in the curve will cause both muscle PO2 and
alveolar PO2 to be on the steep portion of the curve. This leads to a diffusion limitation
effecting both pulmonary oxygen loading and muscle oxygen unloading to occur; causing
a problem with oxygen transport at both the lungs and tissues (Tipton, 2006).
Chronic.
When O2 is limited for energy production, it is important for the body to improve
the efficiency in its utilization of O2, not only for the reason of there being a lower partial
9
pressure of oxygen, but also because after acclimatization to altitude basal metabolic rate
also increases which increases the energy requirements at rest. One way of doing this
would be to shift toward a greater glucose utilization as opposed to fat utilization because
glucose yields a greater energy production per liter of oxygen when compared to that of
fat or protein (Tipton, 2006). This is one reason it has been shown in studies done at
Pikes Peak (Brooks et al., 1992) that, short term exposure to high altitude results in an
increase in blood glucose utilization both at rest and during the same absolute exercise
intensity when compared to sea level.
Erythropoietin (EPO) is the primary hormone that stimulates red blood cell (RBC)
production within the bone marrow. The production of EPO is based on the body’s
oxygen tension determined by the balance of oxygen delivery and oxygen consumption
within the renal tissues, where a majority of this hormone is produced. Within the first
day of exposure to altitude, EPO can begin accelerating the production of RBCs and can
last for weeks with continued residence at altitude. This adaptation is hypothesized to be
one of the slowest to occur during acclimatization and can vary significantly between
subjects and between various altitudes (Tipton, 2006).
Along with the benefits listed above, there are also inherent risks associated with
exposure to a hypoxic environment. The first is an increased risk of either upper
respiratory or gastrointestinal tract infections while at altitude (Noakes, 2000). Prolonged
exposure to a hypoxic environment can also cause considerable deterioration of skeletal
muscle. Decreases of 10-15% in muscle volume with a 20-25% decrease in muscle fiber
10
size are associated with typical mountaineering expeditions to the Himalayas (5-6 weeks)
without a change in muscle fiber-type distribution (Hoppeler et al., 1990).
Due to the EPO adaptation, the question has arisen whether altitude training
should be considered a form of blood doping. The World Anti-Doping Agency has
banned blood transfusions and altitude training simulation systems due to the potential
adverse cardiovascular outcomes following a substantial increase in red blood cell count
and blood viscosity. rHuEPO, a synthetic variation of EPO, has also been banned for
similar cardiovascular symptoms, but is also associated with a serious risk of venous
thrombosis and a development of life-threatening epoetin-associated pure red cell aplasia.
Therefore, if training at high altitude can produce similar effects on erythropoiesis the
argument could be made that it should be treated equally and be banned by the World
Anti-Doping Agency (Lippi & Frachini, 2010).
Training Methods at Altitude
LHTH.
Traditional altitude training consists of living and training at moderate altitude for
usually 2 to 4 weeks (Millet, Roels, Schmitt, Woorons & Richalet, 2010). The first
altitude training studies were based on the concept of the Eastern Africans both living and
training at altitude (live high-train high, LHTH). They were developed to determine how
to best acclimatize, and thus maximize racing performance in a hypoxic environment
(Chapman & Levine, 2007).
11
With the hematological changes that occur with the LHTH model it should be
noted that these changes might not be the only factors involved in improving
performance in endurance sports. The correlation for the change in VO2max and the
change in red blood cell volume yields an r2 = 0.137. This means 86% of the variance in
the change in VO2max could be attributed to factors other than those which are
hematological (Gore, Clark & Saunders, 2007).
There are a number of issues concerning this training method. First, participating
in the LHTH method can be quite expensive and cumbersome to relocate for a given
duration in order to both live and train at moderate to high altitude. Secondly, as
previously mentioned by Mazzeo (2008), at altitude absolute intensities are sacrificed
when compared to the same relative intensity at sea level, therefore, training intensities
cannot be maintained as they would during sea level training.
LHTL.
A second method was developed in the mid-1990s where athletes would live at
altitude and drop back to a lower altitude to train (live high-train low, LHTL) (Chapman
& Levine, 2007). LHTL can also be achieved by living in what is referred to as a nitrogen
house, where the entire house simulates high altitude by diluting the air with a controlled
flow of nitrogen to reduce the partial pressure of oxygen (Bermon, 2008). This was a
variation developed from the original LHTH method because as noted above while at the
higher altitudes the partial pressure of oxygen decreases, thus causing a reduction in the
absolute intensity while at a given relative intensity when compared to that at sea level
12
(Mazzeo, 2008). This new method will allow for an athlete to live at altitude long enough
to induce erythropoiesis, but still allow the athlete to train at higher intensity closer to that
achieved with sea level training.
A running study was performed consisting of 6 weeks of a lead in at sea level and
4 weeks in one of three training modes (LHTH model, LHTL model and a sea level). It
found both the LHTH and sea level training produced a slower time in a 5,000m time
trial (3.3 ± 9sec & 26.7 ± 13sec respectively) and the LHTL model had significant gains
(13.4 ±10sec) (Levine & Stray-Gundersen, 1997). A second variation of LHTL was
developed where only high intensity training was done at sea level, but normal training
was still performed at altitude (Chapman & Levine, 2007).
LLTH.
Live Low-Train High (LLTH), is a training method where the subject lives at sea
level and is exposed to a hypoxic environment during bouts of exercise. In the LLTH
method, altitude tents can be a simple way of attaining the necessary training
environment without having to relocate to altitude for each training session. These tents
can either dilute the air with a controlled flow of nitrogen to reduce the partial pressure of
oxygen, or filter the ambient air through molecular sleeves to decrease the oxygen partial
pressure to simulate altitude (Bermon, 2008).
This can become a more desirable method because in the LHTH method you must
relocate yourself for a given period to a moderate to high altitude, which can be
cumbersome and expensive. The LHTL method can be difficult logistically, requiring
13
you to be transported from high altitude down to a lower altitude to train, which in many
cases can be hours of travel per a single training session, or be expensive with the use of
nitrogen houses.
Because the duration spent in a hypoxic state is much shorter than other hypoxic
models, it may not be sufficient to elicit a raise in RBC. Furthermore, because training is
performed at altitude, absolute training intensities will be decreased from when at sea
level (Millet et al., 2010).
This model has show significant improvements compared to sea level training. 9
days after a 10-day cycling protocol, sea level training improved VO2max by 3.5% (not
significant) where the LLTH improved by 7.0% (p<0.05) (Meeuwsen, Hendriksen &
Holewijn, 2001).
This method of hypoxic training can be used not only as an effective way of
acclimatization prior to competing at altitude, but also as an effective way of preacclimatization for other forms of hypoxic training because of its simpler logistics
(Wilber, 2007).
Meta-Analysis
A single research study has little value to the field. The value of a study is only
fully realized when it is combined with other related studies (Rhea, 2004). The body of
literature in the exercise sciences has grown in such proportion it may become difficult to
arrive at accurate conclusions, especially when much of the literature contains conflicting
14
data. For many years, a review article was the primary way to bring large quantities of
data of a similar topic together and relate it as a body of research rather than the data of
individual studies. However, as a qualitative method of analysis it could be difficult to
analyze extremely large quantities of research properly. A quantitative method, referred
to as meta-analysis, was developed in conjunction to the older qualitative review method
(Glass, 1977). In this quantitative method, the findings of a large number of research
studies on a particular topic may be integrated to achieve a general conclusion. A metaanalysis provides a set of procedures for the researcher to follow. There are six steps in
the process of a meta-analysis: (1) identification of a problem, (2) review of literature, (3)
reading and coding of each study, (4) quantify the study findings (finding effect sizes),
(5) statistical analysis of the effect size data, and (6) interpretation of results (Thomas &
French, 1986).
Traditionally the effect size (ES) is defined as the difference between the
experimental mean and the control group mean, divided by the control group standard
deviation. This ES represents a common measurement as to compare among studies and
displays the magnitude of a treatment effect in an individual study. However in cases
when the means and standard deviations of a study are not reported when published it is
possible to determine an effect size from other statistical tests such as t, ANOVA, and
Pearson r (Thomas & French, 1986). The formulas for these calculations are available in
Glass (1977).
15
The use of meta-analysis poses a number of other benefits. In some fields of
study, the data extruded from a meta-analysis can provide a benchmark in which further
studies can compare themselves. It also has the opportunity to create a sophisticated
“report card” of where literature stands and where fruitful research questions remain,
because of this meta-analysis can be viewed, not simply as a new way of conducting
research reviews, but as a new way of viewing the meaning of data (Noar, 2006).
Previous Altitude Meta-Analyses
There have been two previous comprehensive meta-analyses done on the topic of
hypoxic training. The first (Bonetti & Hopkins, 2009) was a comprehensive metaanalysis which compared Natural LHTH, Natural LHTL, Artificial LHTL with < 1.5
hrs/day exposure, Artificial LHTL with 1.5-5 hrs/day exposure and Artificial LHTL with
8-18 hrs/day exposure. This meta-analysis came to a number of conclusions. The first
came directly from the effect sizes of the included studies. For the elite endurance athlete
population the only model of hypoxic training that produced a statistical difference from
normoxic training was the Natural LHTL (4.0 ± 3.7%). The greatest increases for the
sub-elite endurance population was again the Natural LHTL model (4.2 ± 2.9%),
followed by the Artificial LHTL with < 1.5 hrs/day exposure model (2.6 ± 1.2%), and
finally the Artificial LHTL with 8-18 hrs/day exposure (1.4 ± 2.0%).
These findings were contradicting to those found in a recent Master’s Thesis
(Salgado, 2010). Salgado did not distinguish between elite and sub-elite populations, and
16
only distinguished between LHTH, LHTL, LLTH and IHE. The two training models that
produced the greatest statistical difference between hypoxic and normoxic training was
LHTH (d = 0.38 ± 0.18) and LLTH (d = 0.37 ± 0.15).
At first glance, these two studies appear to differ. However, in an additional
analysis by Bonetti and Hopkins (2009) they found in what they referred to as an
“enhanced protocol,” where they altered variables such as altitude, training intensity and
length of exposure by a single standard deviation of the calculated ES. The single model
that produced the greatest hypothetical increase in this situation was LLTH, similar to
Salgado’s (2010) findings.
One reason these two studies may have initially differed was Salgado (2010) only
included studies that meet the following criteria: (1) adequate use of a control group in
research design, (2) published performance, or VO2max, or [Hb], or Hct results with
means and standard deviations (SD) and (3) use of trained athletes. Bonetti and Hopkins’
(2009) criteria included all studies that included (1) a performance measurement at or
near sea level (<1000m), (2) studies that included oxygen consumption measurements
directly corresponding to endurance performance, studies that included hematological
measurements or other measurements that did not directly relate to endurance
performance or lacking a performance measurement were excluded. With the tougher list
of criteria for inclusion into the study, Salgado (2010) may have been able to come to a
more accurate conclusion.
17
Although some of the physiological alterations appear small for elite level track
athletes it has been shown enhancements as small as 0.3–0.5% is enough to produce a
worthwhile improvement (Hopkins, 2005).
Summary
The topic of hypoxic training is one that has been well researched, however there
is still much controversy regarding what training model elicits the greatest increase in
performance, or to what extent hypoxic training improves performance over normoxic
training. With previous meta-analyses hinting toward LLTH to have the greatest potential
for improvements in endurance performance, the next logical step would be to review the
literature to manipulate several factors of hypoxic training to determine an optimal
generic LLTH protocol.
18
Chapter 3
METHODOLOGY
Journal Article Selection
A search was performed to obtain pertinent LLTH peer-reviewed journal articles
through online databases: SpringerLink, ScienceDirect and Pubmed. Keywords
including: altitude training, hypoxic/hypoxia, performance, live low-train high,
intermittent hypoxic training and LLTH were used in the search, as well as limiting the
search to articles published from 1965-2010. To expand the search further the reference
lists of the included peer-reviewed articles were reviewed to find additional pertinent
LLTH studies.
The articles obtained were reduced to only ones fitting the following criteria: (1)
adequate use of control group in the research design, (2) published performance with
reported means and standard deviations, (3) use of trained athletes and (4) the use of
LLTH model (may also be referred to as IHT).
Data Extraction
If standard error was reported rather than SD, the SD was calculated using the
following formula:
SD  SE  n (Vincent, 2005)
where; SE is the reported Standard Error, and n is the sample size of the group.
19
If the journal article met all inclusion criteria, it was read and coded for the
following variables: hypoxic training altitude (meters), duration of training cycle (days),
length of training sessions (minutes), frequency of exercise (sessions per week), average
exercise intensity (%VO2max), and time to post-hypoxic performance testing (days). In
cases where intensity of warm up or cool down was not reported it was assumed to be
self selected and coded as 62% (Mandengue et al., 2005).
In the case of reported performances, if more than one was included in the journal
article, only one was included in the analysis. The reported performance was selected
based on the following ranking (1) time trial, (2) peak power output, and (3) total work
capacity. The ES was extracted from the reported performance in each research article.
The ES was calculated as Cohen’s d in the following equation:
Cohen’s d =
( POSTM  PRE M )
(Thomas & French, 1986)
PRE SD
where; POSTM is the post-test mean, PREM is the pre-test mean, and PRESD is the pre-test
SD. If a single performance mode was performed multiple times post-hypoxic exposure
the ES was calculated for each measurement of given performance mode.
Data Analysis
Thomas and French (1986) reported that small sample sizes (n < 20) have a
positive bias; therefore, ES of studies consisting of small sample sizes were corrected for
20
the positive bias. Studies that had small sample sizes were each corrected using the
following formula:
Correction factor = 1 
3
(Thomas & French, 1986)
4n  1
where; n is the sample size. The correction factor was multiplied by the calculated ES to
determine the corrected ES (ESCorr).
A scatter plot was constructed for each coded variable using the ESCorr as the
dependent variable and the coded category as the independent variable. Regression
curves of best fit were overlaid to each graph, fit was assessed using least squares.
Regression curves were calculated using Microsoft Excel® 2007. Once regression curves
were constructed, they were used to determine the peak ES for each variable. Peak ES
had to occur within the limits of those tested in the studies included in this analysis. The
value for each coded category that corresponded to the category’s peak ES was combined
with each of the other coded categories and used to determine an optimal training
protocol for the LLTH model. Category labeled “time to post-hypoxic performance
testing” was renamed “time to post-hypoxic peak in performance” in the protocol.
21
Chapter 4
RESULTS
A search was performed to gather pertinent research articles on the LLTH model.
ES were calculated from each research article using Cohen’s d, utilizing their means and
standard deviations. The ES were separated into six categories for analysis: hypoxic
training altitude, duration of training cycle, length of training sessions, frequency of
exercise, average exercise intensity, and time to post-hypoxic performance testing. Small
sample sizes (n < 20) were corrected using the equation from Thomas & French (1986).
The ES were plotted in relation to their corresponding training variable. A regression line
was fitted using least squares to find predicted peak ES and the corresponding “optimal”
training variable value.
Of the six studies included in this analysis there were a combined nine ES
extracted. However, one ES was calculated as an outlier and removed from analysis. The
remaining eight ES were able to be applied to five categories (hypoxic training altitude,
duration of training cycle, length of training sessions, frequency of exercise, and posthypoxic performance testing). The remaining category, average exercise intensity,
included seven ES in its analysis.
22
Hypoxic Training Altitude
The regression line that best fit the plotted ESs followed a second order
polynomial curve with the equation y = 0.0000055x2 - 0.030414x + 41.8239 and an R =
0.7582. With both ends of the regression line producing similar ES and the middle
dipping down to a single data point, the greatest ES was estimated to occur at a point
corresponding to altitudes between 2,500m – 3,000m.
0.7
Effect Size
0.6
0.5
0.4
0.3
0.2
0.1
0
2400
2500
2600
2700
2800
2900
3000
3100
Hypoxic Training Altitude (meters)
Figure 1: Effect Sizes for Hypoxic Training Altitude. A regression line to estimate the hypoxic training
altitude where the greatest ES occurs.
23
Duration of Training Cycle
The regression line that best fit the plotted ES followed a third order polynomial
curve with the equation y = 0.000171x3 - 0.013949x2 + 0.313475x - 1.492383 and an R =
0.8234. The third order polynomial peaked at an ES corresponding to a training cycle of
15 days.
0.8
Effect Size
0.6
0.4
0.2
0
-0.2
-0.4
0
10
20
30
40
Duration of Training Cycle (days)
50
60
Figure 2: Effect Sizes for Duration of Training Cycle. A regression line to estimate the duration of training
cycle where the greatest ES occurs.
24
Length of Training Session
The regression line that best fit the plotted ES followed an inverted second order
polynomial curve with the equation y = -0.000075x2 + 0.0146x - 0.26058 with an R =
0.7046. The inverted parabolic peaked at an ES corresponding to a training cycle length
of 97-minutes.
0.7
0.6
Effect Size
0.5
0.4
0.3
0.2
0.1
0
0
10
20
30
40
50
60
70
80
90 100 110 120 130
Length of Training Session (minutes)
Figure 3: Effect Sizes for Length of Training Session. A regression line to estimate the length of training
session where the greatest ES occurs.
25
Average Exercise Intensity
The regression line that best fit the plotted ES followed a second order
polynomial curve with the equation y = 0.0506x2 – 6.3181x + 197.23 and an R = 0.8080.
With both ends of the regression line producing similar ES and the middle dipping down
to a single data point the greatest ES was estimated to occur at a point corresponding to a
training intensity of 60 – 65% VO2max.
0.7
0.6
Effect Size
0.5
0.4
0.3
0.2
0.1
0
59
60
61
62
63
64
65
66
Average Exercise Intensity (%VO2max)
Figure 4: Effects Sizes for Average Exercise Intensity. A regression line to estimate the average exercise
intensity where the greatest ES occurs.
26
Frequency of Exercise
The regression line that best fit the plotted ES followed an inverted third order
polynomial curve with the equation y = -0.0489x3 + 0.6693x2 – 2.7503x + 3.634 and an R
= 0.7765. The inverted third degree polynomial peaked at an ES corresponding to a
frequency of exercise of 6 days per week.
0.8
0.7
Effect Size
0.6
0.5
0.4
0.3
0.2
0.1
0
0.00
2.00
4.00
6.00
8.00
Frequency of Exercise (sessions per week)
Figure 5: Effect Sizes for Frequency of Exercise. A regression line to estimate the frequency of exercise
where the greatest ES occurs.
27
Time to Post-Hypoxic Peak in Performance
The regression line that best fit the plotted ES followed a fourth order polynomial
curve with the equation y = -0.0062x4 + 0.1159x3 - 0.6704x2 + 1.2651x - 0.3723 and an R
= 0.9860. The forth degree polynomial produced two peaks, the greatest peak occurred at
an ES corresponding to a post-hypoxic peak in performance at 8 days.
1
0.8
Effect Size
0.6
0.4
0.2
0
-0.2
-0.4
0
2
4
6
8
10
Time to Post-Hypoxic Peak in Performance (days)
Figure 6: Effect Sizes for Time to Post-Hypoxic Peak in Performance. A regression line to estimate the
time to post-hypoxic peak in performance where the greatest ES occurs.
28
Chapter 5
DISCUSSION
Training Protocol
Based on the articles used in the analysis of this study a theoretical optimal
training protocol was developed for the LLTH training model based on derived effect
sizes and cursory analysis. The LLTH model consists of a training model where the
athlete would reside at sea level or low altitude and ascend to high altitude or other
hypoxic environment for training. The analysis of this data recommends the following
parameters: training at an altitude of 2500-3000m, during a 15-day training cycle,
consisting of 97-minute training sessions, 6 days a week, at 60-65% VO2max. An athlete
should expect to see the greatest improvement at 8 days post-hypoxic exposure.
In Bonetti and Hopkins’ (2009) initial statistical analysis, they derived the
protocol consisting of a training altitude of 2750m, using a 14-day training cycle, with
47-minute training sessions at intensity between anaerobic threshold and VO2max. After
completing the hypoxic training cycle, the athlete using the protocol should expect to see
the greatest improvement after 2.8 days. These parameters differed from those found in
this analysis. However, their protocol only produced small changes in performance, only
yielding ES of 0.9 ± 2.4%. With an ES of <1.0 they concluded that the LLTH model
would have ≥ 50% chance of producing a trivial effect, therefore would be an ineffective
form of altitude training.
29
In the same study, Bonetti and Hopkins (2009) also reported an “enhanced
protocol” where they adjusted their original results by altering each variable by a single
standard deviation, either up or down, in attempt to develop a better protocol. The
enhanced LLTH protocol yielded a much larger ES, 6.8 ± 4.9%, which was the largest of
the entire study including all training models and both enhanced and not enhanced. Their
enhanced protocol consisted of a training altitude of 2440m, using an 18-day training
cycle, with 47-minute sessions at intensity below anaerobic threshold. After completing
the hypoxic training cycle, the athlete using the protocol should expect to see the greatest
improvement after 5.3 days. This protocol better mimicked the values found in the
current study than that of the original statistical analysis of Bonetti and Hopkins (2009).
Comparing Protocols
In Bonetti and Hopkins’ (2009) enhanced protocol, intensity of exercise was
described as being just below anaerobic threshold; which literature shows to occur at
values between 55-65% VO2max in untrained athletes (Palka & Rogozinski, 1986). This
value corresponded well with the current study’s finding of 60-65% VO2max. However,
the current study’s analysis was performed using studies of trained endurance athletes.
Endurance training will shift the lactate threshold to a higher percent of VO2max (Tipton,
2006). As shown in literature this can exceed 80% VO2max, and remain at these same
relative values even with acute exposure to altitude (Friedmann, Frese, Menold &
Bartsch, 2005). Therefore, the values reported in this analysis are well below the
30
expected values as reported from Bonetti and Hopkins (2009). It would be worth
investigating higher %VO2max in trained athletes closer to their anaerobic threshold to
determine if these values produced greater ES.
The post-hypoxic peak in performance trend line produced two peaks in ES. The
first was a smaller ES occurring between one and two days post-hypoxic exposure, and
the other with a larger ES occurring at about eight days post-hypoxic exposure, which we
included in our protocol. The initial analysis from Bonetti and Hopkins (2009) reported a
value of 2.8 days, which would correspond closely to the smaller ES peak in our analysis.
The enhanced protocol reported 5.3 days post-hypoxic exposure, which was closer to our
value than the original protocol. Bosquet, Montpetit, Arvisais & Mujika (2007) discuss
an optimal taper should last 8-14 days beyond the end of a training cycle, corresponding
well with our value of 8 days. Duration of training session is another variable where there
is a great discrepancy between the current study (97 minutes) and the enhanced protocol
of Bonetti and Hopkins (2009) (47 minutes). Bosquet et al. (2007) discussed that the
overload procedure before a taper will result in higher performance gains, and that the
duration of the taper should be adapted to dissipate the additional fatigue of the greater
training load. This helps explain discrepancies between the current study and the
enhanced protocol of Bonetti and Hopkins (2009). Bonetti and Hopkins (2009) show both
a shorter length of training session and shorter post-hypoxic peak in performance (47
minutes & 5.3 days respectively) compared to the current study (97 minutes & 8 days
respectively).
31
With such a short training cycle, many of the physiological adaptations that are
associated with improvements in performance during altitude training would not apply,
due to the relatively long rate of adaptation (Noakes, 2000). Hematological adaptations
take three to four weeks to produce values that would make changes in endurance
performance (Chapman & Levine, 2007). Gore et al. (2006) suggests that improvements
in mitochondrial efficiency may take as long as 6 weeks. However, there is another
theory that could be used to help describe the performance improvements that occur
during the relatively short training cycle at altitude (Noakes, Gibson & Lambert, 2004).
This adaptation, sometimes referred to as central command, is neurological. The point of
central command is to prevent the body from exceeding its physiological limits and
reaching a state of catastrophic failure. Noakes (2004) describes this adaptation as a
“reset” of the central nervous system (CNS). During exercise, the CNS is constantly
monitoring the metabolic activity in the body, and regulating the sensation of fatigue.
During some bouts of exercise, in this case exercising at altitude, the physiological stress
on the body is pushed beyond the limit initially set by the central command and because
of this, the CNS “resets” itself to a new set of guidelines that prevents the body from
reaching a point of catastrophic failure. With his extensive research in altitude training,
Jack Daniels, renowned scientist and running coach, also agrees with this theory, but
explains it as “training at altitude teaches the body to hurt (J. T. Daniels, personal
communication, December 4, 2010).”
32
Limitations
One major limitation of meta-analysis studies is the values are limited to the
ranges presented in the previous research (Rhea, 2004). Therefore, it should be noted that
the optimal values might lie outside of those that have been previously researched.
However, until studies are executed in a greater range of test values this speculation
cannot be confirmed through meta-analysis.
Training altitude would be an example of this limitation. All the values observed
in the current research occurred at three points: 2500m, 2750m and 3000m. This leaves a
need for future studies to test training intensities outside the range of 2500 – 3000m in the
LLTH hypoxic training model. The values for duration of training cycle are also limited,
with all values analyzed being below 30 days and a single value at 50 days, the regression
line shows potential for an increased ES beyond the scope of this analysis. Without more
data beyond 30 days it cannot be determined if a larger ES would be produced at a
greater training cycle length, but does leave a need to test longer hypoxic training cycle
lengths within the LLTH training model. The values for intensity of exercise are very
limited as well. All the values observed in the current research occurred at three points:
60%, 62% and 65%. This leaves a need for future studies to test training intensities
outside the narrow range of 60 – 65% VO2max in the LLTH hypoxic training model.
With the use of trained athletes, these values of 60-65% VO2max are well below lactate
threshold, so findings at intensities below these relatively low intensities may not be
needed. However, with anaerobic thresholds of trained athletes being significantly higher
33
it may be worthwhile to investigate values between 65% VO2max and anaerobic
threshold. With the recommended duration of exercise to be 97 minutes, it is unlikely for
the intensity to move much above anaerobic threshold.
Summary
In the analysis of this data, it is recommended the following parameters be used in
the LLTH hypoxic training model: training at an altitude of 2500-3000m, during a 15-day
training cycle, consisting of 97-minute training sessions, 6 days a week, at 60-65%
VO2max. An athlete should expect to see the greatest improvement at 8 days posthypoxic exposure.
It should be noted that the values presented in this study are simply guidelines.
The values at which each individual will attain their greatest improvements may vary
from individual to individual. There is a strong inter-individual variation when referring
to altitude training (Chapman, Stray-Gundersen & Levine, 1998). This strong interindividual variation is associated with training at sea level as well (Erskine, Jones,
Williams, Stewart & Degens, 2010).
With small ranges of measurement tested in studies pertaining to this analysis it is
recommended that additional research be done within the LLTH hypoxic training model
with values outside hypoxic training altitudes of 2500 – 3000m, average exercise
intensities of 60 – 65% VO2max, and greater than a training cycle length of 30 days.
34
In conclusion, our data shows that it the LLTH method of altitude training can optimally
increase performance if completed in approximately 2 weeks with one rest day per week
at moderate altitudes and moderate training intensities with roughly 90 minutes of
training per session. Peaks in performance resulting from this training cycle should be
estimated to occur about one week after return from hypoxic training.
35
REFERENCES
Bermon, S. (2008). The use of hypoxic devices in sports. New Studies in Athletics, 23(1),
23-28.
Bonetti. D. L., & Hopkins, W. G. (2009). Sea-level exercise performance following
adaptation to hypoxia: A meta-analysis. Sports Medicine, 39(2), 107-127.
Bosquet, L., Montpetit, J., Arvisais, D., & Mujika, I. (2007). Effects of tapering on
performance: A meta-analysis. Medicine & Science in Sports & Exercise, 39(8),
1358-1365.
Brooks, G. A., Wolfel, E. E., Groves, B. M., Bender, P. R., Butterfield, G. E., Cymerman,
A., … Reeves, J. T. (1992). Muscle accounts for glucose disposal but not blood
lactate appearance during exercise after acclimatization to 4,300m. Journal of
Applied Physiology, 72(6), 2435-2445.
Chapman, R., & Levine, B. D. (2007). Altitude training for the marathon. Sports
Medicine, 37(4-5), 392-395.
Chapman, R., Stray-Gundersen, J., & Levine, B. D. (1998). Individual variation in
response to altitude training. Journal of Applied Physiology, 85(4), 1448-1456.
Erskine, R. M., Jones, D. A., Williams, A. G., Stewart, C. E., & Degens, H. (2010). Interindividual variability in the adaptation of human muscle specific tension to
progressive resistance training. European Journal of Applied Physiology, 110,
1117-1125.
36
Friedmann, B., Frese, F., Menold, E., Bartsch P. (2005). Individual variation in the
reduction of heart rate and performance at lactate thresholds in acute normobaric
hypoxia. International Journal of Sports Medicine, 26(7), 531-6.
Glass, G. V. (1977). Integrating findings: The meta-analysis of research. Review of
Research in Education, 5, 351-379.
Gore, C. J., Clark, S. A., & Saunders, P. U. (2007). Nonhematological mechanisms of
improved sea-level performance after hypoxic exposure. Medicine & Science in
Sport & Exercise, 39(9), 1600-1609.
*Hendriksen, I. J. M., & Meeuwsen, T. (2003). The effect of intermittent training in
hypobaric hypoxia on sea-level exercise: A cross-over study in humans. European
Journal of Applied Physiology, 88, 396-403.
Hopkins, W. G. (2005). Competitive performance of elite track-and-field athletes:
Variability and smallest worthwhile enhancements. Sportscience, 9, 17-20.
Hoppeler, H., Kleinert, E., Schlegel, C., Claassen, H., Howald, H., Kayar, S. R., &
Cerretelli, P. (1990). Morphological adaptations of human skeletal muscle to
chronic hypoxia. International Journal of Sports Medicine, 11(suppl. 1), S3-9.
Levine, B. D., & Stray-Gundersen, J. (1997). Living high-training low: Effect of
moderate-altitude acclimatization with low-altitude training on performance.
Journal of Applied Physiology, 83(1), 102-112.
Lippi, G., & Franchini, M. (2010). Intermittent hypoxic training: Doping or what?.
European Journal of Applied Physiology, 108, 411-412.
37
Mandengue, S. H., Seck, D., Bishop, D., Cisse, F., Tsala-Mbala, P., & Ahmaidi, S.
(2005). Are athletes able to self-select their optimal warm up?. Journal of Science
and Medicine in Sport, 8(1), 26-34.
Mazzeo, R. S. (2008). Physiological responses to exercise at altitude: An update. Sports
Medicine, 38(1), 1-8.
*Meeuwsen, T., Hendriksen I. J. M., & Holewijn, M. (2001). Training-induced increases
in sea-level performance are enhanced by acute intermittent hypobaric hypoxia.
European Journal of Applied Physiology, 84, 283-290.
*Messonnier, L., Geyssant, A., Hintzy, F., & Lacour, F.-R. (2004). Effects of training in
normoxia and normobaric hypoxia on time to exhaustion at the maximum rate of
oxygen uptake. European Journal of Applied Physiology, 92, 470-476.
Millet, G. P., Roels, B., Schmitt, L., Woorons, X., & Richalet, J. P. (2010) Combining
hypoxic methods for peak performance. Sports Medicine, 40(1), 1-25.
*Morton, J. P., & Cable, N. T. (2005). The effect of intermittent hypoxic training on
aerobic and anaerobic performance. Ergonomics, 48, 1535-1546.
Noakes, T.D. (2000). Altitude training for enhanced athletic performance. International
SportMed Journal, 1(2), 1-8.
Noakes, T. D., Gibson, A. S., & Lambert, E. V. (2004). From catastrophe to complexity:
A novel model of integrative central neural regulation of effort and fatigue during
exercise in humans. British Journal of Sports Medicine, 38, 511-514.
38
Noar, S. M. (2006). In pursuit of cumulative knowledge in health communication: The
roll of meta-analysis. Health Communication, 20(2), 169-175.
Palka, M. J., & Rogozinski, A. (1986). Standards and predicted values of anaerobic
threshold. European Journal of Applied Physiology, 54, 643-646.
Parker, D. L. (2004). Effect of altitude and acute hypoxia on VO2max. Journal of
Exercise Physiology, 7(3), 121-133.
Powers, S. K., Lawler, J., Dempsey, J. A., Dodd, S., & Landry, G. (1989). Effects of
incomplete pulmonary gas exchange in VO2max. Journal of Applied Physiology,
66(6), 2491-2495.
Rhea, M. (2004). Synthesizing strength and conditioning research: The meta-analysis.
Journal of Strength and Conditioning Research, 18(4), 921-923.
*Roels, B., Bentley, D. J., Coste, O., Mercier, J., & Millet, G. P. (2007). Effects of
intermittent hypoxic training on cycling performance in well-trained athletes.
European Journal of Applied Physiology, 101, 359-368.
*Roels, B., Millet, G. P., Marcoux, C. J. L., Coste, O., Bentley, D. J., & Candau, R. B.
(2005). Effects of hypoxic interval training on cycling performance. Medicine &
Science in Sports & Exercise, 101, 359-368.
Salgado, R. (2010). The effects of hypoxic manipulation on sea-level performance and
VO2max: A meta-analysis. (Unpublished Master’s Thesis). California State
University, Sacramento, Sacramento, CA.
39
Thomas, J. R., & French, K. E. (1986). The use of meta-analysis in exercise and sport: A
tutorial. Research Quarterly for Exercise and Sport, 57(3), 196-204.
Tipton, C. M. (Ed.). (2006). ACSM’s Advanced Exercise Physiology (1st ed.): Lippincott
Williams & Wilkins.
Vincent, W. J. (2005). Statistics in Kinesiology (3rd ed.). Champaign, IL: Human
Kinetics.
Wehrlin, J. P., & Hallen, J. (2006). Linear decrease in VO2max and performance with
increasing altitude in endurance athletes. European Journal of Applied
Physiology, 96, 404-412
Wilber, R. (2007). Application of altitude/hypoxic training by elite athletes. Medicine &
Science in Sports & Exercise, 39(9), 1610-1624