Content uploaded by Daniel A. Boullosa
Author content
All content in this area was uploaded by Daniel A. Boullosa
Content may be subject to copyright.
1 23
Sports Medicine
ISSN 0112-1642
Volume 43
Number 10
Sports Med (2013) 43:909-917
DOI 10.1007/s40279-013-0086-1
Do Olympic Athletes Train as in the
Paleolithic Era?
Daniel A.Boullosa, Laurinda Abreu,
Adrián Varela-Sanz & Iñigo Mujika
1 23
Your article is protected by copyright and
all rights are held exclusively by Springer
International Publishing Switzerland. This e-
offprint is for personal use only and shall not
be self-archived in electronic repositories. If
you wish to self-archive your article, please
use the accepted manuscript version for
posting on your own website. You may
further deposit the accepted manuscript
version in any repository, provided it is only
made publicly available 12 months after
official publication or later and provided
acknowledgement is given to the original
source of publication and a link is inserted
to the published article on Springer's
website. The link must be accompanied by
the following text: "The final publication is
available at link.springer.com”.
CURRENT OPINION
Do Olympic Athletes Train as in the Paleolithic Era?
Daniel A. Boullosa
•
Laurinda Abreu
•
Adria
´
n Varela-Sanz
•
In
˜
igo Mujika
Published online: 20 August 2013
Ó Springer International Publishing Switzerland 2013
Abstract Every 4 years, approximately 10,000 athletes
participate in the Olympic Games. These athletes have
dedicated several years of physical training to achieve the
best possible performance on a given day. Their prepara-
tion has been supported by expert coaches and an army of
sport scientists, whose overall responsibility is to ensure
that the athletes are in peak condition for their event.
Although every athlete prepares specifically for the unique
physiological challenges of their event, all athletes have
one common characteristic: they are Homo sapiens. They
share a unique genome, which is the result of evolutionary
forces beyond their individual control. Although studies on
the influence of different genetic polymorphisms on
selected athletic events have been proven to be of limited
utility, a body of evidence—from molecular biology to
whole-body measures—suggests that training adaptations
are enhanced when the stimulus closely resembles the
activity pattern of human ancestors. Because genetic evo-
lutionary changes occur slowly in Homo sapiens, and the
traditional physical activity and dietary patterns of Homo
sapiens have undergone rapid and dramatic changes in
previous centuries, we propose that modern humans are
physiologically better adapted to training modes and
nutritional strategies similar to the ones that their hominid
ancestors evolved on, rather than those supported by
modern societies. Such an ancestral pattern was mainly
characterized by the prevalence of daily bouts of pro-
longed, low-intensity, aerobic-based activities interspersed
with periodic, short-duration, high-intensity bursts of
activity. On some occasions, such activity patterns were
undertaken with low carbohydrate availability. Specific
activities that enhanced strength and power were typically
performed after aerobic activities. We present scientific
evidence to support the appropriateness of this model, and
we propose that future studies should address this
hypothesis in a multitude of different sporting activities, by
assessing the genetic responses to and performance-based
outcomes of different training stimuli. Such information
would provide data on which sport scientists and coaches
could better prepare athletes and manage their training
process.
1 Introduction
The evolution of biological complexity beyond single-
celled organisms was linked temporally with the develop-
ment of an oxygen-rich atmosphere, combined with the
capacity of selectable replicating organisms to transfer free
energy, which is obligatory for transformation [1]. Subse-
quently, the impact of natural selection modelled the role
of oxidative metabolism for survival in our Homo
D. A. Boullosa (&)
Post-Graduate Program in Physical Education, Catholic
University of Brasilia, QS 07, LT1 S/N-Sala 111-Bloco G,
71966-700 A
´
guas Claras, DF, Brazil
e-mail: d_boullosa@yahoo.es
L. Abreu
Independent Researcher Lavadores, Vigo, Spain
A. Varela-Sanz
Department of Physical Education and Sport,
University of La Corun
˜
a, Bastiagueiro, Oleiros, Spain
I. Mujika
Department of Physiology, Faculty of Medicine and Odontology,
University of the Basque Country, Leioa, Spain
I. Mujika
School of Kinesiology and Health Research Centre, Faculty
of Medicine, Finis Terrae University, Santiago, Chile
Sports Med (2013) 43:909–917
DOI 10.1007/s40279-013-0086-1
Author's personal copy
predecessors, who perpetuated their genes under selective
environmental pressure [2]. Furthermore, it has been sug-
gested that the hunter–gatherer lifestyle adopted by our
human ancestors required a large increase in aerobic
activity, which could also have influenced human neuro-
biology [3]. Thus, the hereditary characteristics of our
species are theoretically those that fit better with environ-
mental demands for survival, as they are the product of
millions of years of gene–environment interaction.
A robust body of evidence suggests that modern car-
diovascular and metabolic diseases are linked to the change
in human lifestyle that occurred in recent centuries, as a
consequence of the Industrial Revolution [4–8]. This in
turn promoted a sedentary lifestyle, which is opposite to
the physically active way of life of our ancestors. There is
some consensus about the greater impact of physical
inactivity and its role in the prevalence of modern pan-
demics and life expectancy [9]. The new lifestyle could
have disrupted the genetic pool and environmental
requirements for survival, as our genetic endowment has
not been significantly altered since the Paleolithic Era,
when Homo sapiens were still hunter–gatherers [10]. In
fact, our ancestors lived as hunter–gatherers for approxi-
mately 84,000 generations [4, 5]. The adaptations selected
for survival during that wide timeframe may have become
maladaptations under the current environmental change of
physical inactivity [11]. This dissonance between Stone
Age conditions and modern environments is the basis of
the so-called mismatch hypothesis [8, 12, 13], which has
been used by evolutionary medicine to explain most cur-
rent diseases. The mismatch hypothesis has also been uti-
lized in discussion of the controversy surrounding barefoot
running [14].
While the relationship between genetic endowment,
lifestyle and health seems clear, researchers have paid less
attention to the possible influence of our genetic pool on
the physiological adaptations to athletic training and sub-
sequent sport performance. Sport genetics is a growing
field, which has provided some gene candidates for greater
responsiveness to various training modalities [15, 16].
However, the link between those genes, the training pro-
cess and, more importantly, sport performance could be
limited [17]. Alternatively, it may be interesting to analyze
the evidence linking training stimuli and physiological
responses with the pattern of physical activity that mod-
elled the human genome via selection pressure. In other
words, an appealing hypothesis could be that activities that
favoured survival before and during the Paleolithic Era
may evoke greater physiological adaptations and sub-
sequent performance than other training stimuli [18]. If this
assumption is correct, a more robust genetic response to
training modalities more similar to the Paleolithic pattern
of physical activity could be expected. Thus, the training
load and the specific physiological responses to such
training stimuli should be defined. This approach would
contribute to more precise characterization of this activity
pattern and the subsequent limits of human training adap-
tation and performance.
The aim of this review was to analyse the available
evidence for the hypothesis that humans respond better to
training stimuli that are similar to the exercise patterns of
our ancestors than they respond to other training
interventions.
2 Exercising in the Paleolithic?
2.1 Energy Intake
The most important characteristic of human physical
activity is its intimate link to the energy requirements for
survival [12]. That is, caloric expenditure is intended to
guarantee caloric availability [19]. However, the niche of
early hominins—and thus the nutritional composition of
the early human diet—are still heavily debated. Integration
of data from various disciplines suggests that for a long
time period in evolution, hominins derived large amounts
of energy from (terrestrial and aquatic) animal fat and
protein [20]. The majority of carbohydrates were obtained
from fresh fruits and vegetables, together with roots and
tubers, and very little intake came from cereal grains or
refined carbohydrates [21]. This was reversed by the onset
of the Neolithic agricultural revolution—a remarkable
economic transformation that could have been preceded by
expanding diet breadth in the later Paleolithic, according to
the ‘Broad Spectrum Revolution‘ hypothesis [22]. Little is
known about what our Paleolithic ancestors ate each day or
in each season in any specific habitat, but it is suggested
that anatomically modern Homo sapiens relied on a variety
of food sources in varying environments. This nutritional
flexibility may have been central to human evolution across
seasonal variations, during climatic fluctuations and
through famine times. Hominins moved to eat, whereas
modern athletes eat to move.
Technological advances favoured a great increase in
hard food—including proteins, which have been linked to
the important increase in brain mass of our species. More
importantly, Cordain et al. [23] reported that the diets of
studied hunter–gatherer populations were higher in protein
(19–35 %), lower in carbohydrates (22–40 %) and equiv-
alent or even higher in dietary fat (28–58 %) than current
diets. The lesser presence of carbohydrates in the prehis-
toric diet than in the Western diet could be one of its most
relevant characteristics. From an exercise perspective,
maintenance of prolonged, intense exercise (i.e. above the
lactate threshold) could not be expected in those ancient
910 D. A. Boullosa et al.
Author's personal copy
times, as glycogen availability would have been limited,
resulting in greater reliance on fat metabolism [19]. Thus,
lipolytic and phosphagen metabolic pathways would have
been less limited by food availability than glycolysis [24].
2.2 Physical Activity for Survival
The main activities for survival could have involved low-
intensity tasks performed on a regular basis. These daily
activities could have included social interactions; mainte-
nance of shelter and clothing; and gathering of wild plants,
grains, and fruits, among other vegetables, for eating or for
making tools [5, 11, 19]. As these raw materials were more
abundant in the forest, our ancestors probably walked med-
ium-to-long distances to look for them, as well as for hunting.
This issue is important, since the prevalence of open envi-
ronments in the proximity of hominin fossil sites has been
documented recently, suggesting that woods covered less
than *40 % of hominin habitats [25]. There is anecdotal
evidence of daily game pursuits of 10–15 km, with estimated
and measured energy expenditures of *3,000–5,000 kcal/
day in modern hunter–gatherers [5, 10, 19, 26].
It appears that multiple hominoid lineages evolved in
African highlands at altitudes of 1,000–2,000 m. Thus,
evolution of human locomotor physiology may have
occurred under conditions of mild hypobaric hypoxia [27],
with hominid locomotion probably involving intermittent
activities [28]. The greater reliance on aerobic pathways
and the coupling efficiency between energy production and
energy demand observed in high-altitude natives and
endurance-trained athletes could partly account for the
functional advantages of the so-called lactate paradox
phenomenon (i.e. the attenuation of lactate accumulation
despite maintained hypoxia) [29]. This controversial aspect
of the metabolism of lactate could be interpreted as the
result of directional selection for the early emerging
hypoxia tolerance/endurance performance phenotype in
human phylogeny [30].
An interesting hypothesis is that endurance running is a
particular adaptive characteristic of the Homo genus [31].
The evidence supporting this unique adaptation may
include various anatomical and functional characteristics
[32–34], suggesting that the Homo genus is both an
excellent endurance runner and a bad sprinter when com-
pared with other species. Some authors have suggested that
running could be important for scavenging and/or predator
pursuit [31, 35]. Moreover, unlike other species, humans
exhibit a constant energy cost of locomotion at different
velocities [36], which allows good locomotor economy,
independent of the frequent changes in velocity that are
necessary for the aforementioned activity pattern. A key
component is the probable link between human locomotion
and access to a protein-rich diet, which could allow the
important brain growth of our species. In this respect,
endurance running could have increased the chance to
encounter new environments, thus favouring the need for
superior cognitive abilities for adaptation [36]. This
assertion supports the role of physical activity as a medi-
ator of neuronal plastic adaptations that could have had an
important evolutionary role [3], with cognitive and motor
skills considered as complementary within a general con-
cept of ‘activity’ [37].
As hunting was the best source of energy intake and
nutrients of quality, most of the habitual activities of our
ancestors were probably related to hunting. More specifi-
cally, hunting could be broken down into various activities
such as searching for and pursuing animals, throwing,
sprinting, and carrying the game after taking the prey [5,
19]. Overall, such a pattern could be interpreted in terms of
a polarized intensity distribution, with the predominance of
prolonged low-intensity activities interspersed with some
energy bursts of explosiveness in a predictable sequence in
most cases (see Fig. 1). This polarized profile of physical
activity could also be mediated by the aforementioned
metabolic limitations associated with food availability in
those ancient times.
It should be pointed out that the physiological differ-
ences between men and women could be related to dif-
ferences in daily physical activity, with women being
excluded from hunting large game animals [5, 38]. It is
important to note that the division of labour has been linked
to the origin of humankind [5, 8, 38, 39], and this salient
characteristic of the human condition may have involved
specific environment–gene interactions resulting in differ-
ent age- and sex-dependent adaptations during millions of
years of evolution. However, further comparative evidence
supporting this hypothesis is needed.
3 ‘Paleolithic Training’ for Athletic Success
The physiological demands of any sport are not necessarily
similar to the physical activity demands of the prehistoric
niche. More importantly, the principle of allocation pre-
dicts that excellence in one task can be attained only at the
expense of average performance in all other tasks, which
has been previously confirmed by Van Damme et al. [40]in
an analysis of the records of top-level decathletes. Our
main hypothesis suggests that the physical demands of our
ancestors modelled our genome and therefore our capacity
to respond better to training stimuli, independently of the
physiological demands of competition. This means that
although our ancestors’ activities would be more similar to
the current training activities of endurance athletes, other
competitive athletes (e.g. team sport players, sprinters)
would also benefit from this phylogenetic profile
Paleolithic Training for Athletic Success 911
Author's personal copy
considering the specific demands of their competitive
events in accordance with the principle of allocation. For
instance, it is well known that various physiological
adaptations related to higher aerobic capacity would also
enhance repeated sprint ability [41].
The concept of dose response establishes the necessity
to elaborate on what factors determine the better training
load for a better performance. In this respect, Kiely [42]
has recently pointed out the absence of strong evidence
supporting the validity of widely used periodization models
to plan and organize athletic training. We suggest that
training programmes should take into consideration our
ancestors’ activity pattern, in which they probably self-
regulated their daily physical activity, depending on their
caloric requirements [19]. It may be expected that our
predecessors naturally decided to rest or perform light
activities after hard days to be better prepared for the next
hard day(s) [5, 19]. This approach is in agreement with
recent studies that have described a better training outcome
in subjects who regulated their training load, depending on
the state of their autonomic nervous system [43, 44]. Fur-
thermore, previous exercise training studies have reported
that low-stress participants experienced a significantly
greater increase in performance [45, 46]. Therefore, pres-
ervation of homeostasis in the face of different sources of
stress plays a pivotal role in chronic training adaptations.
This could also explain why periodization models often fail
to be effective, as they are not adapted to individual
responses to training, which mainly depend on homeostatic
control for subsequent adaptations.
Elite athletes may represent an artificial selection, with
endurance athletes having a more adapted genotype for
survival that is suitable for health and longevity [47–49].
However, the hypothesis of a lower risk of disease has not
been confirmed [50]. On the other hand, O’Keefe et al. [5]
have pointed out that the current training loads undertaken
by athletes are far beyond those required by our ancestors
for survival. While there is no doubt that greater cardio-
respiratory fitness is related to better health and maybe
longevity [51], it appears there is an optimum level of
physical activity that might not be very different from that
performed by our ancestors. In contrast, modern elite
runners train for more than *20 km a day, with daily
energy expenditures of *6,000–8,000 kcal. Furthermore,
the limits of human endurance are far beyond these exer-
cise levels, with extreme caloric expenditures estimated at
*1 million kcal over a 159-day Antarctic expedition [52].
Therefore, the physical activity levels of hunter–gatherers
were presumably far below those currently performed in
elite sport. However, this is not necessarily a contradiction,
as Olympic athletes could be considered very specialized
‘Homos’, who waste minimal resources in other stressful
activities different from that of their training and
competition.
The training volumes performed by elite endurance
athletes could well be related to the intensity distribution of
their training loads. Observational studies on various
endurance sports [53–57] have systematically reported that
polarized intensity is the most frequent training intensity
distribution and the optimal way to attain sporting
Fig. 1 Hypothetical
distribution of Homo sapiens’
physical activities during the
Paleolithic Era
912 D. A. Boullosa et al.
Author's personal copy
excellence in world-class elite athletes, as well as perfor-
mance improvements in well-trained athletes [58, 59]. This
model implies that *80 % of the training sessions are
dedicated to exercise below the lactate threshold, with the
remaining *20 % targeting high-intensity training (HIT).
In this sense, various studies [56, 60] with endurance
runners have demonstrated that the volume of training
performed below or very close to the first ventilatory
threshold is directly related to sport performance. This is
apparently a paradoxical finding, given that the vast
majority of competitive endurance events are performed at
intensities between the lactate threshold and maximal
oxygen uptake (VO
2max
). However, these observations fit
perfectly well with our hypothesis, with an intensity dis-
tribution similar to that undertaken by our Homo ancestors
(see Fig. 1). Interestingly, endurance athletes frequently
avoid training too often at the lactate threshold, with HIT
often performed at maximal [61], and supramaximal
intensities [62]. Within this picture, we speculate that the
intensities that correspond with the severe domain of
exercise may be poorly tolerated by athletes in comparison
with other training intensities. This could be a consequence
of the induced homeostatic crisis at these exercise intensity
levels, as inferred from previously reported metabolic,
autonomic, neuromuscular and psychological disturbances
[63–66]. This outcome would be interpreted in terms of a
genetic limitation for these metabolic demands [18], which
agrees with the previously suggested lesser importance of
the glycolytic pathway for survival of our ancestors [19,
24]. In this respect, the trainability of lactate dehydroge-
nase activity is lower than that of citrate synthase activity
[67].
The impact of daily low-intensity activities has also
been confirmed by Hautala et al. [68], who observed that
the amount of daily light physical activity correlated with
gains in VO
2max
in a group of physically active men who
performed a specific endurance training programme. This
outcome is in agreement with a previous study by Ross and
McGuire [69], who found a significant relationship
between incidental physical activity and cardiorespiratory
fitness. It is interesting to note that a polarized intensity
pattern of physical activity fits perfectly well with obser-
vational data on the spontaneous physical activity of chil-
dren [70, 71] and also team sport match activities [72, 73],
with low-intensity activities being predominant but fre-
quently interspersed with brief bursts of explosive actions.
Collectively, these observations reinforce the necessity to
take into account incidental physical activity, programmed
exercise and competitive efforts when comparing the out-
comes of different training regimes in athletes.
From a molecular point of view, the greater effective-
ness of both light and very intense exercise on aerobic
phenotypic adaptations [61] could be linked to activation of
intracellular signalling cascades, which are well-charac-
terized upstream modulators of peroxisome proliferator-
activated receptor-c coactivator-1a (PGC-1a) expression in
skeletal muscle. In this respect, HIT can induce skeletal
muscle metabolic and functional performance adaptations
comparable to traditional low-intensity training [74, 75],
confirming the key role of PGC-1a in aerobic phenotypic
adaptations after both low- and high-intensity training
stimuli [61].
3.1 Concurrent Activity Patterns
The ancestral physical activity pattern included a mixture
of different activities, which is in contrast to the high
specialization level required for elite sport. This issue has
been pointed out by Cordain and Friel [19] who suggested
that our ancestors were the historical equivalent of cross-
training athletes. In this respect, we have suggested [18]a
possible explanation, based on an interference phenomenon
during concurrent strength and endurance training [76].
This model suggests that there is an adaptive conflict
during concurrent training performed at intensities in the
severe and heavy domains for aerobic exercise, and heavy
submaximal loads for resistance exercise, thereby exhibit-
ing a greater level of interference and a weaker dose–
response relationship. Conversely, aerobic exercise below
the lactate threshold performed concurrently with strength
training at maximum intensities would exhibit a lower level
of interference and thus a stronger dose–response rela-
tionship. Additionally, this interference phenomenon could
be associated with training modes that produce low levels
of blood lactate as a consequence of lesser activation of the
glycolytic pathway.
Endurance athletes improve their performance signifi-
cantly when they perform high-intensity strength training
based on heavy resistance and plyometric exercises, or a
combination of both. Such improvements have also been
observed in spite of an important reduction (*30 %) of the
sport-specific training volume [77, 78]. It seems that during
concurrent training, the intra-session sequence of aerobic
exercise ? resistance exercise is better for aerobic perfor-
mance than resistance exercise ? aerobic exercise [79].
The same could be concluded when comparing both
between-session training sequences, as running perfor-
mance was better maintained following an endurance–
strength sequence training day [80]. It is interesting to note
that high-intensity strength training favours greater muscle
power mainly via neural adaptations with no or very little
hypertrophic response. From an evolutionary point of view,
this could be a key phenotypic adaptation, as greater
skeletal muscle mass is more energy demanding and
therefore less suited for survival. Moreover, an improve-
ment in power performance immediately after different
Paleolithic Training for Athletic Success 913
Author's personal copy
endurance exercises has been described [81, 82], maybe
suggesting that acute muscular potentiation after endurance
running may be also an adaptive characteristic.
These observations are reinforced by recent studies on
molecular responses to exercise. For instance, the complex
protein mammalian target of rapamycin (mTOR) is thought
to be another key factor that integrates signals of the
energetic status of the cell and environmental stimuli to
control cell growth. Although it has been proposed that
activation of PGC-1a and mTOR signalling pathways is
responsible for the specific adaptive responses that have
been shown after aerobic exercise and resistance exercise,
various recent studies have demonstrated that there is no
clear distinction in exercise-specific signalling pathways
[83–85]. Furthermore, Lundberg et al. [86] have recently
reported that the skeletal muscle anabolic environment was
reinforced under concurrent training conditions, as aerobic
exercise 6 hours prior to resistance exercise did not impair
signalling of mTOR-related proteins. Moreover, and in
contrast with the traditional notion, PGC-1a expression
was also increased 3 hours after resistance exercise. These
results suggest that the widespread idea that aerobic exer-
cise and resistance exercise responses could be incompat-
ible from a molecular point of view may be too simplistic
and could be better understood by considering the order in
which they are performed.
3.2 Train Low, Compete High
Chakravarthy and Booth [87] have suggested that the
oscillations of muscle glycogen and triglyceride levels with
physical activity–rest cycles during feast–famine cycles
throughout evolution selected some genes for oscillating
enzymatic regulation of fuel storage and efficiency during
fuel usage. In this sense, various studies have shown that
low levels of pre-exercise glycogen [88], as well as training
twice every second day, may be more efficient in
enhancing muscle glycogen stores and enzymatic activity
and in improving exercise performance than training daily
[89, 90]. Moreover, the current evidence suggests that there
is enhancement of intracellular signalling pathways (e.g.
5
0
adenosine monophosphate-activated protein kinase) and
reduced reliance on carbohydrate utilization when exer-
cising with low glycogen stores [91]. However this nutri-
tional strategy has not been effective in improving
performance and could compromise the health status of
athletes and their training and competitive performances in
high-intensity sports [92, 93]. Therefore, while the impact
of these adaptations on performance enhancement is yet to
be determined—especially when competing under normal
or supercompensated glycogen levels—it seems that low
pre-exercise glycogen levels favour better training
adaptations.
4 Conclusion and Future Perspectives
From the scientific evidence presented here, we suggest
that the genetic heritage of our species could strongly
influence the capacity of Olympic athletes to adapt to
diverse training. This could explain the difficulties in
achieving a consensus on the role of different polymor-
phisms in athletes’ performance. The existence of an
‘ideal’ physical activity pattern inherited from our ances-
tors does not exclude the need for training individualiza-
tion, with consideration of both athletes’ characteristics and
the specific demands of individual athletic events. However
it appears that the more similar a training regime is to our
ancestors’ activity profile, the greater the adaptations and
subsequent performance are. Further studies should address
this hypothesis, paying special attention to the link between
molecular responses and performance outcomes that take
place when long-lasting training regimes respect this phy-
logenetic template. Athletes’ dietary intake should be
prescribed to support the energy needs and adaptation to
training programmes that often mimic the activity pattern
of our ancestors. Additionally, studies on epigenetics [94]
could also help us to determine the extent to which the
mismatch between the early developmental environment
and that experienced during growth and mature life
accounts for interindividual variability in training-induced
adaptations.
Acknowledgments We would like to thank Professor John A.
Hawley (School of Medical Sciences, RMIT University, Melbourne,
VIC, Australia) for his valuable editorial comments and suggestions,
and Dr. Fa
´
bio Yuzo Nakamura (Department of Physical Education,
Universidade Estadual de Londrina, Londrina, Parana
´
, Brazil) for his
nice contributions to an earlier version of the manuscript. No funding
was used in the preparation of this article. The authors have no
conflicts of interest that are directly relevant to the content of the
article.
References
1. Koch LG, Britton SL. Aerobic metabolism underlies complexity
and capacity. J Physiol. 2008;586(1):83–95.
2. Wood B, Harrison T. The evolutionary context of the first hom-
inins. Nature. 2011;470(7334):347–52.
3. Raichlen DA, Polk JD. Linking brains and brawn: exercise and
the evolution of human neurobiology. Proc Biol Sci.
2013;280(1750):20122250.
4. O’Keefe JH, Vogel R, Lavie CJ, et al. Achieving hunter–gatherer
fitness in the 21st century: back to the future. Am J Med.
2010;123(12):1082–6.
5. O’Keefe JH, Vogel R, Lavie CJ, et al. Exercise like a hunter–
gatherer: a prescription for organic physical fitness. Prog Car-
diovasc Dis. 2011;53(6):471–9.
6. Booth FW, Chakravarthy MV, Spangenburg EE. Exercise and
gene expression: physiological regulation of the human genome
through physical activity. J Physiol. 2002;543(Pt 2):399–411.
914 D. A. Boullosa et al.
Author's personal copy
7. Booth FW, Chakravarthy MV, Gordon SE, et al. Waging war on
physical inactivity: using modern molecular ammunition against
an ancient enemy. J Appl Physiol. 2002;93(1):3–30.
8. Eaton SB, Konner M, Shostak M. Stone agers in the fast lane:
chronic degenerative diseases in evolutionary perspective. Am J
Med. 1988;84(4):739–49.
9. Booth FW, Gordon SE, Carlson CJ, et al. Waging war on modern
chronic diseases: primary prevention through exercise biology.
J Appl Physiol. 2000;88(2):774–87.
10. Cordain L, Gotshall RW, Eaton SB, et al. Physical activity,
energy expenditure and fitness: an evolutionary perspective. Int J
Sports Med. 1998;19(5):328–35.
11. Booth FW, Lees SJ. Fundamental questions about genes, inac-
tivity, and chronic diseases. Physiol Genomics. 2007;28(2):
146–57.
12. Eaton SB, Strassman BI, Nesse RM, et al. Evolutionary health
promotion. Prev Med. 2002;34(2):109–18.
13. Williams GC, Nesse RM. The dawn of Darwinian medicine.
Q Rev Biol. 1991;66(1):1–22.
14. Lieberman DE. What we can learn about running from barefoot
running: an evolutionary medical perspective. Exerc Sport Sci
Rev. 2012;40(2):63–72.
15. Puthucheary Z, Skipworth JR, Rawal J, et al. The ACE gene and
human performance: 12 years on. Sports Med. 2011;41(6):
433–48.
16. Puthucheary Z, Skipworth JR, Rawal J, et al. Genetic influences
in sport and physical performance. Sports Med. 2011;41(10):
845–59.
17. Timmons JA, Knudsen S, Rankinen T, et al. Using molecular
classification to predict gains in maximal aerobic capacity fol-
lowing endurance exercise training in humans. J Appl Physiol.
2010;108(6):1487–96.
18. Boullosa DA, Nakamura FY, Ruiz JR. Effectiveness of polarized
training for rowing performance (letter). Int J Sports Physiol
Perform. 2010;5(4):431–2.
19. Cordain L, Friel J. The Paleolithic athlete: the original cross
trainer. In: Sands RR, Sands LR, editors. The anthropology of
sport and human movement: a biocultural perspective. Lanham:
Lexington Books; 2010. p. 267–76.
20. Kuipers RS, Joordens JC, Muskiet FA. A multidisciplinary
reconstruction of Palaeolithic nutrition that holds promise for the
prevention and treatment of diseases of civilisation. Nutr Res
Rev. 2012;25(1):96–129.
21. Konner M, Eaton SB. Paleolithic nutrition: twenty-five years
later. Nutr Clin Pract. 2010;25(6):594–602.
22. Stiner MC. Thirty years on the ‘‘Broad Spectrum Revolution’’
and paleolithic demography. Proc Natl Acad Sci USA. 2001;
98(13):6993–6.
23. Cordain L, Miller JB, Eaton SB, et al. Plant-animal subsistence
ratios and macronutrient energy estimations in worldwide hunter–
gatherer diets. Am J Clin Nutr. 2000;71(3):682–92.
24. Hochachka PW. Fuels and pathways as designed systems for
support of muscle work. J Exp Biol. 1985;115:149–64.
25. Cerling TE, Wynn JG, Andanje SA, et al. Woody cover and
hominin environments in the past 6 million years. Nature.
2011;476(7358):51–6.
26. Pontzer H, Raichlen DA, Wood BM, et al. Hunter–gatherer
energetics and human obesity. PLos One. 2012;7(7):e40503.
27. Hochachka PW, Gunga HC, Kirsch K. Our ancestral physiolog-
ical phenotype: an adaptation for hypoxia tolerance and for
endurance performance? Proc Natl Acad Sci USA. 1998;95(4):
1915–20.
28. Dudley R. Limits to human locomotor performance: phylogenetic
origins and comparative perspectives. J Exp Biol. 2001;204(Pt
18):3235–40.
29. Hochachka PW, Beatty CL, Burelle Y, et al. The lactate paradox
in human high-altitude physiological performance. News Physiol
Sci. 2002;17:122–6.
30. Hochachka PW, Rupert JL, Monge C. Adaptation and conser-
vation of physiological systems in the evolution of human
hypoxia tolerance. Comp Biochem Physiol A Mol Integr Physiol.
1999;124(1):1–17.
31. Bramble DM, Lieberman DE. Endurance running and the evo-
lution of Homo. Nature. 2004;432(7015):345–52.
32. Lieberman DE, Raichlen DA, Pontzer H, et al. The human glu-
teus maximus and its role in running. J Exp Biol. 2006;209(Pt
11):2143–55.
33. Pontzer H, Rolian C, Rightmire GP, et al. Locomotor anatomy
and biomechanics of the Dmanisi hominins. J Hum Evol. 2010;
58(6):492–504.
34. Rolian C, Lieberman DE, Hallgrı
´
msson B. The coevolution of
human hands and feet. Evolution. 2010;64(6):1558–68.
35. Liebenberg L. Persistence hunting by modern hunter–gatherers.
Curr Anthropol. 2006;47(6):1017–25.
36. Lieberman DE, Bramble DM, Raichlen DA, et al. Brains, brawn,
and the evolution of human endurance running capabilities. In:
Grine FE, Fleagle JG, Leakey RE, editors. The first humans:
origin and early evolution of the genus Homo. New York:
Springer; 2009. p. 77–98.
37. Kempermann G, Fabel K, Ehninger D, et al. Why and how
physical activity promotes experience-induced brain plasticity.
Front Neurosci. 2010;4(189):1–9.
38. Hurtado A, Hawkes K, Hill K, et al. Female subsistence strategies
among Ache hunter–gatherers of eastern Paraguay. Hum Ecol.
1985;13(1):1–28.
39. Panter-Brick C. Sexual division of labor: energetic and evolu-
tionary scenarios. Am J Hum Biol. 2002;14(5):627–40.
40. Van Damme R, Wilson RS, Vanhooydonck B, et al. Performance
constraints in decathletes. Nature. 2002;415(6873):755–6.
41. Bishop D, Girard O, Mendez-Villanueva A. Repeated-sprint
ability—part II: recommendations for training. Sports Med.
2011;41(9):741–56.
42. Kiely J. Periodization paradigms in the 21st century: evidence-led
or tradition-driven? Int J Sports Physiol Perform. 2012;7(3):
242–50.
43. Kiviniemi AM, Hautala AJ, Kinnunen H, et al. Endurance
training guided individually by daily heart rate variability mea-
surements. Eur J Appl Physiol. 2007;101(6):743–51.
44. Kiviniemi AM, Hautala AJ, Kinnunen H, et al. Daily exercise
prescription on the basis of HR variability among men and
women. Med Sci Sports Exerc. 2010;42(7):1355–63.
45. Ruuska PS, Hautala AJ, Kiviniemi AM, et al. Self-rated mental
stress and exercise training response in healthy subjects. Front
Physiol. 2012;3:51.
46. Bartholomew JB, Stults-Kolehmainen MA, Elrod CC, et al.
Strength gains after resistance training: the effect of stress-
ful, negative life events. J Strength Cond Res. 2008;22(4):
1215–21.
47. Ruiz JR, Mora
´
n M, Arenas J, et al. Strenuous endurance exercise
improves life expectancy: it’s in our genes. Br J Sports Med.
2011;45(3):159–61.
48. Sanchis-Gomar F, Olaso-Gonzalez G, Corella D, et al. Increased
average longevity among the ‘‘Tour de France’’ cyclists. Int J
Sports Med. 2011;32(8):644–7.
49. Fiuza-Luces C, Ruiz JR, Rodrı
´
guez-Romo G, et al. Are ‘endur-
ance’ alleles ‘survival’ alleles? Insights from the ACTN3 R577X
polymorphism. PLoS One. 2011;6(3):e17558.
50. Go
´
mez-Gallego F, Ruiz JR, Buxens A, et al. Are elite endurance
athletes genetically predisposed to lower disease risk? Physiol
Genomics. 2010;41(1):82–90.
Paleolithic Training for Athletic Success 915
Author's personal copy
51. Kokkinos P, Myers J, Kokkinos JP, et al. Exercise capacity and
mortality in Black and White men. Circulation. 2008;117(5):
614–22.
52. Noakes TD. The limits of human endurance: what is the greatest
endurance performance of all time? Which factors regulate per-
formance at extreme altitude? Adv Exp Med Biol. 2007;618:
255–76.
53. Mujika I, Chatard JC, Busso T, et al. Effects of training on per-
formance in competitive swimming. Can J Appl Physiol.
1995;20(4):395–406.
54. Steinacker JM, Lormes W, Lehmann M, et al. Training of rowers
before world championships. Med Sci Sports Exerc. 1998;30(7):
1158–63.
55. Fiskerstrand A, Seiler KS. Training and performance character-
istics among Norwegian international rowers 1970–2001. Scand J
Med Sci Sports. 2004;14(5):303–10.
56. Esteve-Lanao J, San Juan AF, Earnest CP, et al. How do
endurance runners actually train? Relationship with competition
performance. Med Sci Sports Exerc. 2005;37(3):496–504.
57. Zapico AG, Caldero
´
n FJ, Benito PJ, et al. Evolution of physio-
logical and haematological parameters with training load in elite
male road cyclists: a longitudinal study. J Sports Med Phys Fit-
ness. 2007;47(2):191–6.
58. Ingham SA, Fudge BW, Pringle JS. Training distribution, phys-
iological profile, and performance for a male international
1500-m runner. Int J Sports Physiol Perform. 2012;7(2):193–5.
59. Neal CM, Hunter AM, Brennan L, et al. Six weeks of a polarized
training-intensity distribution leads to greater physiological and
performance adaptations than a threshold model in trained
cyclists. J Appl Physiol. 2013;114(4):461–71.
60. Esteve-Lanao J, Foster C, Seiler S, et al. Impact of training
intensity distribution on performance in endurance athletes.
J Strength Cond Res. 2007;21(3):943–9.
61. Laursen PB. Training for intense exercise performance: high-
intensity or high-volume training? Scand J Med Sci Sports.
2010;20(Suppl. 2):1–10.
62. Iaia FM, Bangsbo J. Speed endurance training is a powerful
stimulus for physiological adaptations and performance
improvements of athletes. Scand J Med Sci Sports. 2010;
20(Suppl. 2):11–23.
63. S
ˇ
kof B, Strojnik V. Neuromuscular fatigue and recovery
dynamics following prolonged continuous run at anaerobic
threshold. Br J Sports Med. 2006;40(3):219–22.
64. Seiler S, Haugen O, Kuffel E. Autonomic recovery after exercise
in trained athletes: intensity and duration effects. Med Sci Sports
Exerc. 2007;39(8):1366–73.
65. Faude O, Kindermann W, Meyer T. Lactate threshold concepts:
how valid are they? Sports Med. 2009;39(6):469–90.
66. Ekkekakis P, Parfitt G, Petruzzello SJ. The pleasure and
displeasure people feel when they exercise at different intensities:
decennial update and progress towards a tripartite rationale
for exercise intensity prescription. Sports Med. 2011;41(8):
641–71.
67. Issurin VB. Generalized training effects induced by athletic
preparation: a review. J Sports Med Phys Fitness. 2009;49(4):
333–45.
68. Hautala A, Martinmaki K, Kiviniemi A, et al. Effects of habitual
physical activity on response to endurance training. J Sports Sci.
2012;30(6):563–9.
69. Ross R, McGuire KA. Incidental physical activity is positively
associated with cardiorespiratory fitness. Med Sci Sports Exerc.
2011;43(11):2189–94.
70. Bailey RC, Olson J, Pepper SL, et al. The level and tempo of
children’s physical activities: an observational study. Med Sci
Sports Exerc. 1995;27(7):1033–4.
71. Duncan JS, Badland HM, Schofield G. Combining GPS with
heart rate monitoring to measure physical activity in children: a
feasibility study. J Sci Med Sport. 2009;12(5):583–5.
72. Rampinini E, Coutts AJ, Castagna C, et al. Variation in top level
soccer match performance. Int J Sports Med. 2007;28(12):
1018–24.
73. Dogramaci SN, Watsford ML, Murphy AJ. Time-motion analysis
of international and national level futsal. J Strength Cond Res.
2011;25(3):646–51.
74. Burgomaster KA, Howarth KR, Phillips SM, et al. Similar met-
abolic adaptations during exercise after low volume sprint
interval and traditional endurance training in humans. J Physiol.
2008;586(1):151–60.
75. Gibala MJ, Little JP, van Essen M, et al. Short-term sprint
interval versus traditional endurance training: similar initial
adaptations in human skeletal muscle and exercise performance.
J Physiol. 2006;575(Pt 3):901–11.
76. Docherty D, Sporer B. A proposed model for examining the
interference phenomenon between concurrent aerobic and
strength training. Sports Med. 2000;30(6):385–94.
77. Paavolainen L, Ha
¨
kkinen K, Rusko H. Effects of explosive type
strength training on physical performance characteristics in cross-
country skiers. Eur J Appl Physiol Occup Physiol. 1991;62(4):
251–5.
78. Paavolainen L, Ha
¨
kkinen K, Ha
¨
ma
¨
la
¨
inen I, et al. Explosive-
strength training improves 5-km running time by improving
running economy and muscle power. J Appl Physiol. 1999;86(5):
1527–33.
79. Chtara M, Chamari K, Chaouachi M, et al. Effects of intra-ses-
sion concurrent endurance and strength training sequence on
aerobic performance and capacity. Br J Sports Med. 2005;39(8):
555–60.
80. Doma K, Deakin GB. The effects of strength training and
endurance training order on running-economy and -performance.
Appl Physiol Nutr Metab. 2013;38(6):651–6.
81. Vuorimaa T, Virlander R, Kurkilahti P, et al. Acute changes in
muscle activation and leg extension performance after different
running exercises in elite long distance runners. Eur J Appl
Physiol. 2006;96(3):282–91.
82. Boullosa DA, Tuimil JL, Alegre LM, et al. Concurrent fatigue
and potentiation in endurance athletes. Int J Sports Physiol Per-
form. 2011;6(1):82–93.
83. Coffey VG, Zhong Z, Shield A, et al. Early signaling responses to
divergent exercise stimuli in skeletal muscle from well-trained
humans. FASEB J. 2006;20(1):190–2.
84. Coffey VG, Pilegaard H, Garnham AP, et al. Consecutive bouts
of diverse contractile activity alter acute responses in human
skeletal muscle. J Appl Physiol. 2009;106(4):1187–97.
85. Wilkinson SB, Phillips SM, Atherton PJ, et al. Differential effects
of resistance and endurance exercise in the fed state on signalling
molecule phosphorylation and protein synthesis in human mus-
cle. J Physiol. 2008;586(Pt 15):3701–17.
86. Lundberg TR, Fernandez-Gonzalo R, Gustafsson T, et al. Aerobic
exercise alters skeletal muscle molecular responses to resistance
exercise. Med Sci Sports Exerc. 2012;44(9):1680–8.
87. Chakravarthy MV, Booth FW. Eating, exercise, and ‘‘thrifty’’
genotypes: connecting the dots toward an evolutionary under-
standing of modern chronic diseases. J Appl Physiol. 2004;
96(1):3–10.
88. Pilegaard H, Saltin B, Neufer PD. Effect of short-term fasting and
refeeding on transcriptional regulation of metabolic genes in
human skeletal muscle. Diabetes. 2003;52(3):657–62.
89. Hansen AK, Fischer CP, Plomgaard P, et al. Skeletal muscle
adaptation: training twice every second day vs. training once
daily. J Appl Physiol. 2005;98(1):93–9.
916 D. A. Boullosa et al.
Author's personal copy
90. Yeo WK, Paton CD, Garnham AP, et al. Skeletal muscle adap-
tation and performance responses to once a day versus twice
every second day endurance training regimens. J Appl Physiol.
2008;105(5):1462–70.
91. Baar K, McGee S. Optimizing training adaptations by manipu-
lating glycogen. Eur J Sport Sci. 2008;8(2):97–106.
92. Hawley JA, Tipton KD, Millard-Stafford ML. Promoting training
adaptations through nutritional interventions. J Sports Sci.
2006;24(7):709–21.
93. Burke LM. Fueling strategies to optimize performance: training
high or training low? Scand J Med Sci Sports. 2010;20(Suppl.
2):48–58.
94. Ehlert T, Simon P, Moser DA. Epigenetics in sports. Sports Med.
2013;43(2):93–110.
Paleolithic Training for Athletic Success 917
Author's personal copy