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Is there any practical application of meta-analytical
results in strength training?
Paulo Gentil1*, Antonio Arruda2, Daniel Souza3, Jürgen Giessing4, Antonio Paoli5, James
Fisher6, James Steele6
1Faculdade de Educação Física e Dança, Universidade Federal de Goiás, Brazil, 2Campus
Petrolina, Universidade de Pernambuco, Brazil, 3Universidade Federal do Rio Grande do
Norte, Brazil, 4Institute of Sport Science, University of Koblenz-Landau, Germany,
5Department of Biomedical Sciences, University of Padova, Italy, 6Sport Science
Laboratory/Centre for Health, Exercise & Sport Science, Southampton Solent
University, United Kingdom
Submitted to Journal:
Frontiers in Physiology
Specialty Section:
Exercise Physiology
ISSN:
1664-042X
Article type:
Opinion Article
Received on:
23 Nov 2016
Accepted on:
03 Jan 2017
Provisional PDF published on:
03 Jan 2017
Frontiers website link:
www.frontiersin.org
Citation:
Gentil P, Arruda A, Souza D, Giessing J, Paoli A, Fisher J and Steele J(2016) Is there any practical
application of meta-analytical results in strength training?. Front. Physiol. 8:1.
doi:10.3389/fphys.2017.00001
Copyright statement:
© 2017 Gentil, Arruda, Souza, Giessing, Paoli, Fisher and Steele. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC BY). The use,
distribution and reproduction in other forums is permitted, provided the original author(s) or
licensor are credited and that the original publication in this journal is cited, in accordance with
accepted academic practice. No use, distribution or reproduction is permitted which does not
comply with these terms.
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Frontiers in Physiology | www.frontiersin.org
Provisional
Is there any practical application of meta-analytical results in
strength training?
Paulo Gentil1*, Antonio Arruda2, Daniel Souza3, Jurgen Giessing4, Antonio Paoli5, James
Fisher6, James Steele6
1 Faculdade de Educação Física e Dança, Universidade Federal de Goiás, Goiania, Brasil.
2 Universidade de Pernambuco-Campus Petrolina, Petrolina, Brasil.
3 Universidade Federal do Rio Grande do Norte, Natal, Brasil.
4 University of Koblenz-Landau, Institute of Sport Science, Landau, Germany.
5 Department of Biomedical Sciences, University of Padova, Padova, Italy
6Sport Science Laboratory/Centre for Health, Exercise & Sport Science/Southampton
Solent University, Southampton, United Kingdom.
*Correspondence:
Paulo Gentil
FEFD - Faculdade de Educação Física e Dança
Universidade Federal de Goiás - UFG
Campus Samambaia
Avenida Esperança s/n, Campus Samambaia- CEP: 74.690-900
Goiânia - Goiás - Brasil
Phone/Fax: +55 062 3521-1105
Email: paulogentil@gmail.com
Key words: strength training, exercise prescription, strength and conditioning, resistance
exercise, muscle hypertrophy, muscle strength.
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Introduction 1
2
Designing resistance training (RT) programs is a complex task that involves the 3
manipulation of numerous variables that interact with each other, influencing the program 4
outcomes (Paoli 2012, Tan 1999). The attempt to clearly define the combination of 5
variables which would bring optimal adaptations for different outcomes is undermined 6
by the large number of studies involving RT, the conflicting findings reported by many 7
of them and the lack of methodological clarity and consistency in previous studies’ 8
protocols. As such, meta-analyses emerge as an attractive approach since they allow the 9
combination of multiple studies in an attempt to estimate the effect size of a single 10
variable, surpassing possible inadequacies of statistical power within individual studies. 11
With this aggregation of information, a more robust estimation of the effects is possible. 12
13
However, Field (2015) has noted a pertinent philosophical objection to these types 14
of analyses that might apply to RT studies; in essence we have a replication crisis. 15
Researchers often attempt to perform replications of the findings from earlier studies, yet 16
frequently they do not adequately replicate the conditions of the original study. For 17
example, one study may examine the effects of low or high set volume whilst participants 18
train at a frequency of twice a week using repetition ranges of 8-12 and perform sets to 19
momentary failure. Another may examine the effects of low or high set volume whilst 20
participants train at a frequency of five times a week using 10 repetitions per set and not 21
having participants perform sets to momentary failure. Though the two studies might 22
appear to be examining whether low or high set volumes produce greater adaptations, 23
they are in fact examining these within the context of different manipulations of other RT 24
variables. There is likely a reason for this lack of proper replication, as was noted by 25
Richard Feynman1. Indeed, we would argue that the currently heterogeneous body of 26
literature on the effects of the manipulation of different RT variables is evidence of this 27
replication crisis being alive and well in our field. 28
29
1“…under certain circumstances, X, rats did something, A. She was curious as to
whether, if she changed the circumstances to Y, they would still do, A. So her
proposal was to do the experiment under circumstances Y and see if they still did
A.
I explained to her that it was necessary first to repeat in her laboratory the
experiment of the other person—to do it under condition X to see if she could also
get result A—and then change to Y and see if A changed. Then she would know
that the real difference was the thing she thought she had under control.
She was very delighted with this new idea, and went to her professor. And his
reply was, no, you cannot do that, because the experiment has already been done
and you would be wasting time. This was in about 1935 or so, and it seems to
have been the general policy then to not try to repeat psychological experiments,
but only to change the conditions and see what
happens.”(http://calteches.library.caltech.edu/51/2/CargoCult.htm)
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In this current opinion article we explain specifically why it might be unwise to 30
conduct meta-analyses with such heterogeneous RT studies noting the effects of different 31
confounding RT variables, and also suggest that it might be irresponsible to make general 32
estimates of RT effects and propose recommendations. 33
34
Impact of resistance training variables 35
36
The first aspect to consider is the confusion borne from the definition and 37
reporting of training ‘intensity’. RT studies have previously defined and thus controlled 38
exercise ‘intensity’ as a percentage of the load equivalent to the one repetition maximum 39
(1RM). Nevertheless, previous studies reported that the number of repetitions performed 40
at a given percentage of 1RM largely varies when different individuals are performing 41
the same exercise and when the same individual performs different exercises (Hoeger et 42
al. 1990). Hoeger et al. (1990) reported that, when using 80% of 1RM, resistance trained 43
women were able to perform 22 repetitions in the leg press exercise and only 9 in the 44
knee extension. Therefore, if a study prescribes 12 repetitions at 80% of 1RM for the leg 45
press and another prescribes the same repetitions and load for the knee extension, they 46
would be performing the exercise with a similar intensity of load but different intensity 47
of effort. Additionally, Hoeger et al. (1990) reported the average number of repetitions 48
performed with 80% of 1RM was 22 for trained and 12 for untrained women. As a result, 49
the prescription of the same number of repetitions at the same relative load would result 50
in different intensity of effort. For this reason, several authors have proposed that using 51
1RM percentages may be inadequate for controlling RT intensity (Fisher and Smith 2012, 52
Fisher et al. 2013, Steele 2014) and suggested that controlling effort (i.e. exercising to the 53
point of momentary failure) would be a better strategy for this purpose. 54
55
The need for controlling effort is further supported by a previous study reporting 56
that training to failure results in greater gains in muscle strength and more positive 57
changes in body composition than not training to failure (Giessing et al. 2014). 58
Considering this, the use of percentages of 1RM in many of the previous studies might 59
have resulted in unmatched intensities of effort, which likely influenced training 60
outcomes. We acknowledge that this is a contentious issue, and other studies exist 61
suggesting that, where volume matched, equivocal results can be obtained whether 62
training to muscular failure or not (Fisher, Blossom, et al. 2016) However, other authors 63
have suggested that training to muscular failure is necessary for standardizing resistance 64
training interventions (Dankel, Jessee, et al. 2016). With this in mind, we believe that the 65
inadequate control of intensity of effort is a problem in many previous studies, since there 66
is often no clear definition of the set end point used in training which was performed to 67
either volitional fatigue, or a self-determined repetition maximum or to momentary 68
failure. 69
70
Supervision ratio might be another aspect to influence the results of RT. In 71
previous studies by Gentil & Bottaro (2010), Mazzetti et al. (2000) and Coutts et al. 72
(2004), untrained men, trained men and young athletes had higher gains in muscle 73
strength and performance when training with a more favorable supervision ratio (e.g. 74
fewer participants to trainers). It is important to note that in these studies all participants 75
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performed the same protocol, with the same exercises, number of sets, repetitions, etc. 76
The differences in the results were due to the supervision ratio. It is likely that a more 77
favourable supervision ratio resulted in participants exercising to a greater intensity of 78
effort. More recent studies which have used a one to one (client: trainer) ratio have failed 79
to identify any benefits from performing advanced resistance training methods (Fisher, 80
Carlson, et al. 2016a, Fisher, Carlson, et al. 2016b, Fisher et al. 2014) likely because all 81
participants, irrespective of group, exercised to true momentary failure due to the 82
supervision. It is our opinion that whilst, supervision ratio is not usually considered in RT 83
meta-analyses [e.g. Krieger (2010), Schoenfeld et al.(2015), Rhea et al (2003), Peterson 84
et al. (2004), Werbom et al. (2007), Scheonfeld (2016a, 2016b)] possibly since it is often 85
unreported in RT studies; it is an important factor which needs future consideration in 86
both empirical trials and review articles including meta-analyses. 87
88
Other aspects usually forgotten are movement velocity and the type of muscle 89
action predominantly performed. Previous studies suggest that different muscle actions 90
influence the acute responses to exercise. For example, when using the same load, it is 91
possible to perform more repetitions when only eccentric actions are performed in 92
comparison to combined concentric and eccentric, which, in turn, permits greater 93
repetitions than concentric only muscle actions (Flanagan et al. 2014). Moreover, 94
eccentric actions have been shown to induce a higher degree of muscle damage than 95
concentric, even when using the same load and performing the same number of repetitions 96
(Gibala et al. 1995). Consequently, two persons performing the same number of sets and 97
repetitions for the same exercise, at the same relative load may have different 98
physiological responses if using different movement velocities in the concentric or 99
eccentric phase. In addition, previous studies reported differences in anabolic signaling 100
(Burd et al. 2012), fitness (Bottaro et al. 2007) and gains in muscle size and strength 101
(Nogueira et al. 2009) when the same exercise protocol was performed with different 102
velocities. Again, this is often an overlooked variable which we believe is important when 103
considering studies to include in a meta-analysis. 104
105
Another possible source of confusion might be considering only specific muscles 106
(e.g., the supposed prime movers) when counting the number of sets performed. This 107
aspect is especially relevant for upper body muscles (i.e., not considering the biceps 108
brachii work during pulldowns), since previous research has shown that upper-body multi 109
joint exercises produce equivalent gains in muscle size and strength as single joint 110
exercises specifically targeted for the elbow flexors (Gentil et al. 2016, Gentil et al. 2015). 111
Therefore, one may not disregard the involvement of a muscle in the multi joint 112
movement (i.e. the involvement of triceps during bench press, biceps during pulldowns, 113
etc.) as it will lead to an inadequate estimation of training volume. For example, Krieger 114
(2010) considers hypertrophy in response to set volume, however within this meta-115
analysis he reports sets of exercise rather than sets per muscle group. Notably, one of the 116
key studies included in this meta-analysis reports high-, moderate- and low-set volume 117
per exercise (1, 2 or 4 sets) as well as set volume per muscle group (3, 6, or 9 sets; 118
(Ostrowski et al. 1997) concluding no differences between groups. Furthermore, when 119
this study was included in a later meta-analysis by Schoenfeld, et al. (2016a) considering 120
dose-response between weekly volume per muscle group and hypertrophy it was not 121
adequately identified that whilst the data showed a ‘trend’ for higher volumes producing 122
greater hypertrophic adaptations in the triceps brachii (4.8%, 4.7%, and 2.3%), the same 123
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trend was not evident in the rectus femoris (12.3%, 4.6% and 6.3%) for high-, moderate- 124
and low-volume groups. In fact inspection of the original article might lead us to believe 125
that the set volume per muscle group per week reported is inaccurate. Participants 126
performed 3 pull-down and row exercises and 3 biceps curl variations in addition to an 127
upright row exercise, as well as 3 bench press variations, a shoulder press exercise, and 3 128
triceps specific exercises. Ultimately we could argue that participants’ biceps and triceps 129
brachii muscles received a minimum of 7 sets per week in the low volume group, and up 130
to 28 sets in the high volume group. Meanwhile since participants only trained lower-131
body exercises one day per week the reported set volume per exercise and per muscle 132
group appear more accurate. It is important to recognize that this is not discussed with 133
sufficient clarity within the aforementioned meta-analyses (Krieger 2010) but rather only 134
from reading the empirical research studies. Notably Schoenfeld, et al., (2016a) does 135
identify these differences (table 1, pp4). However. Moreover, including studies that 136
measured legs and arm muscles in the same analyses could be misleading, Considering 137
that , since previous data did not present the same trend of greater adaptations with 138
increasing sets per muscle group across both upper- and lower-body muscles (Bottaro et 139
al. 2011, Paulsen et al. 2003, Ronnestad et al. 2007) we believe that dissociation of these 140
muscle groups, rather than a single recommendation, might have been be beneficial. With 141
the above in mind we believe that when performing meta-analyses authors can 142
inadvertently misrepresent research studies and might exclude essential details which 143
serves to reduce the validity of their conclusions. 144
145
In addition to the above variables which are often left unreported in research 146
publications and uncontrolled in meta-analyses we should also consider the variable of 147
range of motion (ROM). In previous studies, participants whose training differed only in 148
ROM of the exercise performed (e.g. all other variables were controlled) showed between 149
group variation in muscle size, strength, and decreases in skinfold thickness in favor of a 150
larger ROM (Bloomquist et al. 2013, Massey et al. 2005, McMahon et al. 2014, Pinto et 151
al. 2012).Thus, we believe that if ROM is not reported and controlled this may have 152
differed between interventions and depending on the outcome examined may impact any 153
conclusions. 154
155
Finally, the tests used to measure outcomes seem to be of vital importance for 156
estimating effect size. When analyzing strength two of the most popular tests are the one-157
repetition maximum (1RM) and isokinetic or isometric peak torque (PT) (Brown and 158
Weir 2001). Although the two methods are widely used and accepted by the scientific 159
community, they are not equivalent, as the results obtained show large variations and can 160
be even conflicting (Gentil et al. In press). For example, the effect size of an intervention 161
can be large when measuring its results by 1RM, while the effect size of another 162
intervention can be low when evaluating it by isometric or isokinetic dynamometry. 163
Nevertheless, this may not be reflective of the intervention protocols, but rather of the 164
tests performed. Thus, using studies that utilized different assessment methods in the 165
same analysis may produce inaccurate and misleading results. The same is true for in-166
vitro and in-vivo methods of assessing change in hypertrophy as discussed previously 167
(Fisher, Steele, et al. 2016, Steele and Fisher 2014).Furthermore, the use of effect sizes 168
in general has recently been challenged (Dankel, Mouser, et al. 2016), and since meta-169
analyses calculate overall results from this value we urge caution in meta-analyses which 170
provide conclusions contradictory to a body of research. 171
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172
Conclusions 173
174
These observations are only a few examples of the complexity involved with RT 175
prescription, discussed to illustrate how isolated variables may influence RT outcomes. 176
As such we question the possibility of making general estimates of RT adaptation without 177
considering such complexity. The question is, if one single variable can influence the 178
results, how can we control the interaction between them or how can we precisely 179
estimate the impact of one of them when the others are not controlled? 180
181
A common criticism of meta-analyses is that they usually combine studies that 182
have important methodological differences and, consequently, the summary effect can be 183
largely influenced by these differences across studies (Field 2015, Fuhr and Hellmich 184
2015). Although there are many possible strategies to test for heterogeneity across 185
included studies, and methods to account for such heterogeneity and confounding factors 186
upon effect sizes (e.g. treating meta-analysis as a multi-level model), the power to test for 187
these moderators depends partly on the number of studies available and the sample sizes 188
used across these studies. For this reason, an effort for controlling confounding factors 189
would be fruitless if the analyses involve a low number of heterogeneous studies with 190
small sample sizes, as is the case in almost all RT meta-analyses. It is also important to 191
note that a meta-analysis is only as good as the studies included. Thus an initial flaw in 192
screening and selection can lead to inappropriate inclusion and/or analysis of inadequate 193
studies, leading to the phenomenon: “garbage in garbage out” (Charlton, 1996). 194
195
Meta-analyses are highly publishable and have negligible cost and effort when 196
compared to the acquisition of raw data. These might explain the increase in meta-197
analyses production and publication in many areas (Field 2015, Fuhr and Hellmich 2015), 198
including RT. However, it is important to remember that for a meta-analysis to be valid, 199
a large amount of data on homogeneous subgroups under homogenous conditions should 200
accumulate for topics where there is strong consensus about which variables have 201
theoretical importance, and this does not seem to be the case for RT. Therefore, we 202
consider that a greater value can be obtained by designing and conducting studies of larger 203
and homogenous samples that can adequately address the topics considered, or 204
performing more exact replication studies instead of prematurely performing meta-205
analyses on differing RT variables or trying to estimate the effects of RT combining 206
studies that involve an uncontrollable heterogeneity. If it is desirable to obtain an 207
understanding of the conclusions that can be drawn from the body of existing literature, 208
then it would be better to carefully review and interpret studies whilst considering the role 209
of confounding variables and study designs. 210
211
Author Contributions 212
213
PG, AA, DC, JG, AP, JF and JS: Conception, drafting the article, revising it 214
critically, and final approval of the version to be published. 215
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Conflict of Interest Statement 217
218
The authors declare that the research was conducted in the absence of any 219
commercial or financial relationships that could be construed as a potential conflict of 220
interest. 221
222
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