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META-ANALYSIS OF POSTACTIVATION POTENTIATION
AND
POWER:EFFECTS OF CONDITIONING ACTIVITY,
V
OLUME,GENDER,REST PERIODS, AND TRAINING
STATUS
JACOB M. WILSON,
1
NEVINE M. DUNCAN,
1
PEDRO J. MARIN,
2,3
LEE E. BROWN,
4
JEREMY P. LOENNEKE,
5
STEPHANIE M.C. WILSON,
6
EDWARD JO,
7
RYAN P. LOWERY,
1
AND
CARLOS UGRINOWITSCH
8
1
Department of Health Sciences and Human Performance, The University of Tampa, Tampa, Florida;
2
Laboratory of
Physiology, European University Miguel de Cervantes, Valladolid, Spain;
3
Research Center on Physical Disability, ASPAYM
Castilla y Leo
´
n, Valladolid, Spain;
4
Human Performance Laboratory, Department of Kinesiology, California State University,
Fullerton, Californi a;
5
Department of Health and Exercise Science, The University of Oklahoma, Norman, Oklahoma;
6
Department of Nutrition, IMG Performance Institute, IMG Academy, Bradenton, Florida;
7
Department of Nutrition, Food
and Exercise Sciences, The Florida State University, Tallahassee, Florida; and
8
School of Physical Education and Sport,
University of Sa˜o Paulo, Brazil
A
BSTRACT
Wilson, JM, Duncan, NM, Marin, PJ, Brown, LE, Loenneke, JP,
Wilson, SMC, Jo, E, Lowery, RP, and Ugrinowitsch, C. Meta-
analysis of postactivation potentiation and power: Effects of
conditioning activity, volume, gender, rest periods, and training
status. J Strength Cond Res 27(3): 854–859, 2013—There is
no clear agreement regarding the ideal combination of factors
needed to optimize postactivation potentiation (PAP) after
a conditioning activity. Therefore, a meta-analysis was con-
ducted to evaluate the effects of training status, volume, rest
period length, conditioning activity, and gender on power aug-
mentation due to PAP. A total of 141 effect sizes (ESs) for
muscular power were obtained from a total of 32 primary stud-
ies, which met our criteria of investigating the effects of a heavy
preconditioning activity on power in randomized human trials.
The mean overall ES for muscle power was 0.38 after a condi-
tioning activity (p , 0.05). Significant differences were found
between moderate intensity (60–84%) 1.06 and heavy inten-
sity (.85%) 0.31 (p , 0.05). There were overall significant
differences found between single sets 0.24 and multiple sets
0.66 (p , 0.05). Rest periods of 7–10 minutes (0.7) after
a conditioning activity resulted in greater ES than 3–7 minutes
(0.54), which was greater than rest periods of .10 minutes
(0.02) (p , 0.05). Significant differences were found between
untrained 0.14 and athletes 0.81 and between trained 0.29
and athletes. The primary findings of this study were that a con-
ditioning activity augmented power output, and these effects
increased with training experience, but did not differ signifi-
cantly between genders. Moreover, potentiation was optimal
after multiple (vs. single) sets, performed at moderate intensi-
ties, and using moderate rest periods lengths (7–10 minutes).
KEY WORDS warm up, training status, regulatory light chains,
motor unit recruitment
INTRODUCTION
T
he capacity to maximize muscular power is critical
to successful outcomes in a number of athletic
events, such as the long jump and the high jump.
Several authors have demonstrated that muscle
postactivation potentiation (PAP) is a phenomenon that can
acutely increase muscular power and, consequently, perfor-
mance (6). Short-term gains in power after heavy muscle pre-
loading are thought to result from phosphorylation of myosin
regulatory light chains and increased recruitment of higher
order motor units (52). Accordingly, a great deal of research
has attempted to identify methods to elicit PAP through a va-
riety of conditioning activities during warm-up routines
(1,3,4,6,9,11–14,16,21–24,26–34,37–39,42,43,46,49,53–55).
The efficacy by which a conditioning activity can
stimulate PAP mechanisms and acutely enhance muscular
performance ultimately depends on the balance between
fatigue and potentiation (52). This balance is affected by
numerous factors including, but not limited to, training
experience (27), rest period length (28), and the intensity
of the conditioning activity performed (45). Chiu et al. (6)
reported a 1–3% increase in vertical and drop jump heights
Address correspondence to Dr. Jacob M. Wilson, jmwilson@ut.edu.
27(3)/854–859
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Ó 2013 National Strength and Conditioning Association
854
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5 minutes after 5 sets of 1 repetition back squat, performed at
90 % of their concentric 1 repetition maximum (1RM) in
trained subjects. In contrast, recreationally trained individu-
als exhibited a 1–4% decline in performance postcondition-
ing activity. In addition to training experience, absolute
individual strength appears to influence PAP because there
is a moderately positive correlation (r = 0.63) between 1RM
values and countermovement jump potentiation, after
a high-intensity activity (27). These findings can be
explained by greater fatigue resistance in trained than in un-
trained subjects after a conditioning activity, which is typi-
cally performed at high intensities (e.g., 75–95% of 1RM). In
general, although authors have identified several factors that
may affect the occurrence of PAP, there is no clear agree-
ment regarding the ideal combination of these factors to
optimize performance after a conditioning activity (52).
A robust and quantitative approach to define the factors
that contribute the most in eliciting PAP can be provided by
a meta-analysis of the data presented in the current body of
literature. This technique minimizes subjectivity by standard-
izing treatment effects of relevant studies into effect sizes
(ESs), pooling the data, and then analyzing it to draw
conclusions. Thus, the primary objective of this investigation
was to quantitatively iden-
tify which components of
conditioning activities op-
timize power output.
METHODS
Experimental Approach
to the Problem
A meta-analytic statistical
analysis was conducted to
evaluate the effects of
training status, volume, rest
period length, condition-
ing activity, and gender
on power augmentation
because of PAP. Relevant
studies were combined
and analyzed statistically
to provide an overview of
the body of research on
this topic. Conclusions
were based on the results
of the statistical analysis
with suggestions for appli-
cations and future research
for strength and condition-
ing professionals.
Subjects
Subject characteristics
can be found in table 1.0.
Of the studies included
there were 141 male and female subjects with an average
age of 20 +/2 5 years of age.
Literature Search
Searches were performed for published studies with 4
specific criteria.
First, the primary focus of the study was to investigate the
effects of a conditioning activity on a specific criterion power
task (force 3 velocity). Second, the conditioning activity had
to be performed at a greater load than the criterion task (e.g.,
a free weight squat performed before a vertical jump). Third,
the study could not use any outside electrically elicited stim-
uli during the conditioning activity. Fourth, all studies were
limited to human controlled randomized trials. Scientific
articles were retrieved based on an extensive search of
the following data bases completed in February 2011:
MEDLINE (1966–2011), EMBASE (1974–2011), Cochrane
Database of Systematic Reviews (1993–2011), Lilacs (1982–
2011), Scielo (1997––2011), and Google Scholar (1980–
2011). Computer search engines used the following key
words combinations: ‘postactivation potentiation,’ ‘power,’
‘countermovement jump,’ ‘warm-up,’ and ‘Wingate.’ Exclu-
sion of studies with irrelevant content and doublets were
TABLE 1. Effect size for muscle power.
Overall
Mean (95% CI) N
0.38 (0.21, 0.55) 141
Moderators
Gender
Male 0.42 (0.23, 0.61) 113 p . 0.05
Female 0.20 (20.31, 0.71) 16
Male and female 0.21 (20.38, 0.79) 12
Age
,25 y 0.38 (0.21, 0.55) 141
Training status
Untrained 0.14 (20.27, 0.57) 25 p , 0.05
Trained 0.29 (0.03, 0.55) 68 p , 0.05
Athletes 0.81 (0.44, 1.19) 32
Conditioning activity
Dynamic low body 0.42 (0.22, 0.61) 107 p . 0.05
Static lower body 0.35 (20.19, 0.89) 14
Dynamic upper body 0.17 (20.28, 0.63) 20
Intensity (%1RM)
Moderate (60–84%) 1.06 (0.54, 1.57) 15 p , 0.05
Heavy (85–100%) 0.31 (0.13, 0.49) 121
Volume
Single 0.24 (0.37 0.44) 95 p , 0.05
Multiple 0.66 (0.36, 0.95) 46
Rest periods (min)
,2:00 0.17 (20.23, 0.58) 24
3–7 0.54 (0.31, 0.77) 75 p , 0.05
7–10 0.70 (0.10, 1.30) 11
.10 0.02 (20.33, 0.38) 31
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carried out in 3 steps. First, the titles of the articles were read.
Second, the abstracts were read. Third, the entire article was
read. The reference lists of relevant articles were, in turn,
scanned for additional articles (published or unpublished)
that met the inclusion criteria. Attempts were made to con-
tact authors requesting any unpublished work. Conference
abstracts and proceedings were excluded. Relevant studies
were selected and searched for data necessary to compute
ES and descriptive information regarding the PAP protocol.
As a result, 44 articles (1,3,4,6,8,9,11–24,26–35,37–40,42–
44,46,49,51–55) related to postactivation potentiation and
power in response to exercise were considered, all of which
were full-text articles and published in the English language.
After using selection criteria previously described, a total of 32
studies were selected to be used for this study (1,3,4,6,9,11–
14,16,19,21–24,26–34,37–40,42–44, 46,49,53–55).
Coding of Studies
Each study was read and coded by the primary investigator
for descriptive information including gender and training
experience. The primary outcome variable was power,
which was coded as power output and performance on
a criterion power task. Conditioning activity protocol was
coded for mean intensity (low #60 % 1RM, moderate = 60–
84% 1RM, and heavy $85% 1RM), sets (single vs. multiple),
mode (isometric vs. dynamic), and rest period between the
end of the conditioning activity and performance of the cri-
terion task. Rest periods were coded as immediate
(,2 minutes), short (3–7 minutes), moderate (7–10 minutes),
and long (.10 minutes). Training status was coded as rec-
reationally trained (active but not currently resistance train-
ing), trained (at least 1 year of resistance training experience),
and athlete (criteria included either .3 years resistance
training experience, National Collegiate Athletic Association
college or pro level athlete, or competitive power or weight
lifter). Gender was coded as male, female, or a combination
(both men and women). Despite attempts, age could not be
statistically compared, given all individuals in the studies in-
cluded were young adults (18–35 years of age).
Calculation and Analysis of Effect Size
Pre-ES and post-ES were calculated with the following
formula: ([Posttest mean 2 pretest mean]/pretest SD). The
ES were then adjusted for sample size bias. This adjustment
consisted of applying a correction factor to adjust for a pos-
itive bias in smaller sample sizes. Descriptive statistics were
calculated and univariate analysis of variance by groups was
used to identify differences between training status, gender,
conditioning activity, intensity, volume, and rest periods
with level of significance set at p , 0.05. When a significant
F-value was achieved, pairwise comparisons were performed
using a Bonferroni corrected alpha. All calculations were
made with SPSS statistical software package v.19.0 (SPSS
Inc., Chicago, IL, USA). The scale proposed by Rhea et al.
(41) was used for interpretation of ES magnitude. Coder drift
was assessed by randomly selecting 10 studies for recoding
by a second investigator. Per case agreement was determined
by dividing the variables coded the same by the total number
of variables. A mean agreement of 0.90 was required for
acceptance. The mean agreement for this analysis was 0.97.
RESULTS
Overall ES and moderating variables are presented in Table 1.
There were a total of 141 ESs for muscular power obtained
from a total of 32 primary studies, which met our criteria
(1,3,4,6,9,11–14,16,21–24,26–34,38,39,42,43,46,49,53–55). The
mean overall E S for muscle power was 0.38 when a condition-
ing activity was performed before a criterion power task (95%
confidence interval [C I]: 0.21, 0.55).
Moderating Variables
No significant differences were found between gender
groups; the mean overall ES for male was 0.42 (95% CI:
Figure 1. Effects of a single vs. multiple sets conditioning mode
protocol on power in untrained, trained, and athletic populations.
Figure 2. Power after immediate (,2 minutes), short (3–7 minutes),
moderate (7–10 minutes), and long (.10 minutes) rest period lengths in
untrained, trained, and athletic populations.
Postactivation Potentiation
856
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0.23, 0.61; n: 123), for female 0.20 (95% CI: 20.31, 0.71;
n: 16), and for the combined group 0.21 (95% CI: 20.38,
0.79; n: 12) (p . 0.05). No significant differences existed
between dynamic 0.42 (95% CI: 0.22, 0.61; n: 107) and static
conditioning activities 0.35 (95% CI: 20.19, 0.89; n: 14).
Significant differences were found between untrained 0.14
(95% CI: 20.27, 0.57; n: 25) and athletes 0.81 (95% CI:
0.44, 1.19; n: 32) (p , 0.05), and between trained 0.29
(95% CI: 0.03, 0.55; n: 68) and athletes 0.81 (95% CI: 0.44,
1.19; n: 32) (p , 0.05) (Table 1).
Significant differences were found between moderate
intensity 1.06 (95% CI: 0.54, 1.57; n: 15) and heavy intensity
0.31 (95% CI: 0.13, 0.49; n: 121) (p , 0.05). There were
overall significant differences found between single sets
0.24 (95% CI: 0.37, 0.44; n: 95) and multiple sets 0.66 (95%
CI: 0.36, 0.95; n: 46) (p , 0.05). There were overall signifi-
cant differences found between rest periods of 3–7 minutes
0.54 (95% CI: 0.31, 0.77; n: 75) and .10 minutes 0.02 (95%
CI: 20.33, 0.38; n: 31) (p , 0.05), and between rest periods
of 7–10 minutes 0.70 (95% CI: 0.10, 1.30; n : 11) and
.10 minutes 0.02 (95% CI: 20.33, 0.38; n: 31) (p , 0.05).
DISCUSSION
After a conditioning activity protocol, mechanisms of
muscular fatigue and potentiation coexist; however, sub-
sequent power output and performance depend on the
balance between these 2 factors. Although numerous studies
have been conducted, to date, no precise consensus has been
formed regarding the optimal acute conditioning mode
protocol in recreationally trained, trained, and athletic
populations. Therefore, we conducted a meta-analysis of
32 studies to quantitatively identify which components of
the conditioning protocol (activity, intensity, rest period
length) optimized power output and how these variables
were affected by training status and gender. The primary
findings of this study were that a conditioning activity
augmented power output (ES = 0.38), and these effects in-
creased with training experience but did not differ signifi-
cantly between genders. Moreover, potentiation was
optimal following multiple (vs. single) sets, performed at
moderate intensities (60–84%), and using moderate rest peri-
ods lengths (7–10 minutes).
Our results indicated trivial (ES = 0.14), small (E S = 0.29),
and moderate (ES = 0.89) augmentation of power after
a conditioning activity in untrained, trained, and athletic
populations, respectively (Table 1, Figure 1). Moreover, ath-
letes with .3 years of resistance training experience appear
to respond optimally to conditioning activities. These results
agreed with the results of Chiu et al. (6) who found 1–3%
increases in countermovement jump and drop jump heights
after a heavy conditioning activity in resistance trained indi-
viduals, whereas those who were recreationally trained
experienced a 1–4% decline in performance. Additionally,
past research indicates a moderate correlation (r = 0.63)
between 1RM strength values and countermovement jump
potentiation, after a high-intensity activity (27). It is likely
that the balance between fatigue and potentiation is more
favorable with increased training experience. It should
also be noted that trained individuals demonstrate elevated
regulatory myosin light chain phosphorylation activity (45)
relative to those untrained, suggesting that increased power
output may be bidirectionally mediated with increased train-
ing experience (greater PAP and lower fatigue).
Banister et al. (2) provided a 2-factor mathematical theory
on human performance. Their theory suggested that an ath-
lete should be viewed as a system that receives input in the
form of a training impulse and produces output in the form
of performance. For a specific conditioning activity, the im-
pulse is calculated by the intensity (percent of 1RM) multi-
plied by the volume performed. Banister et al. (2) suggested
that the training impulse leads to the build-up of fitness and
fatigue in the athlete and that performance is a result of the
difference between those 2 variables. After a heavy condi-
tioning exercise, fatigue may be elicited in the form of de-
pletion of substrate (10), a build-up of hydrogen ions (50), or
mechanical disruption of the myofibrillar architecture (7).
Short-term gains in fitness after heavy muscle preloading
are thought to include phosphorylation of myosin regulatory
light chains and increased recruitment of higher order motor
units (52).
Our results indicate that moderate intensity (60–84%
1RM) (ES = 1.06) exercise is ideal for eliciting PAP when
compared with very high intensities (.85% 1RM) (ES =
0.31), independent of training experience. Muscle damage
appears to occur proportionally to previous contractile
intensity (5). Thus, according to Banister et al.’s (2) model,
it could be postulated that a moderately heavy conditioning
activity elicits PAP (fitness) without as much mechanical
trauma (fatigue) as a heavier activity.
When analyzing volume, we found overall that multiple
sets resulted in a greater augmentation of power than single
sets (Table 1). However, these findings were likely mediated
by training status (Figure 1). Specifically, individuals with
low training experience demonstrated approximately 120%
declines in power when performing multiple as compared to
single sets. In contrast trained individuals and experienced
athletes augmented power approximately 104% and 320%,
respectively, when comparing multiple with single sets.
Chronic resistance training increases fatigue resistance via
increased buffering capacity (25,47) and an overall greater
resistance to skeletal muscle damage (36). These findings
would suggest that the conditioning activity elicits far greater
fatigue than can be overcome by PAP (fitness) when moving
from single to multiple sets in less trained individuals. How-
ever, in trained individuals, it is likely that the increase in
PAP from single to multiple sets outweighs the increase in
fatigue.
Although the length of PAP manifestation remains un-
known, research indicates that the ability to potentiate
performance likely dissipates by 30 minutes after
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a conditioning activity (42). Within that framework, the last
major component to optimizing PAP concerns the ideal in-
terval of time after a given conditioning activity when exam-
ining power output. To date, the limited number of studies
examining postconditioning activity rest periods has yielded
varying and often conflicting results. These studies, collec-
tively, suggest that brief (5 minutes) (48), moderate
(8–12 minutes) (27), and extensive (18.5 minutes) (6) recov-
ery durations, may elicit PAP. Such results highlight the need
for our current statistical synthesis of the literature, which
demonstrated that overall, moderate rest period lengths
(7–10 minutes) appear to optimally augment power output
after a conditioning activity (Table 1). However, as expected,
these findings differed based on training status (Figure 2),
possibly explaining past conflicts in the literature. Less ex-
perienced subjects demonstrated lower ES increases in
power compared with those of a higher training status at
all rest period lengths. Moreover, trained and experienced
athletes peaked in power at moderate (7–10 minutes) and
short (3–7 minutes) rest period lengths, respectively. Our
results were recently supported by Jo et al. (23) who found
that individuals with at least 1 year of weight training expe-
rience demonstrated a negative correlation (r = 20.77)
between optimal recovery length and back squat 1RM.
PRACTICAL APPLICATIONS
The impact of different variables will vary across subjects;
therefore, it essential to always tailor programs specific to the
individual. The benefit of a meta-analysis is that it can provide
the optimal range for each variable and give the practitioner
an idea of where to start and the absolute effect to expect from
each training variable. Therefore, independent of gender, our
analyses suggest that a preconditioning activity can augment
power production capacity in several motor skills (i.e.,
sprinting, jumping, throwing). However, the optimal condi-
tioning activity varies based on training experience. For the
individual with little high-intensity weight training experience,
it is important to emphasize that only a small augmentation of
power can be expected. Therefore, a heavy conditioning
activity may not be ideal until an individual has at least 1 year
of resistance training experience. We suggest that the in-
experienced individuals select a moderate intensity condition-
ing activity (60–85% 1RM loads) and only perform 1 set at
this intensity before performing a criterion power task.
Individuals with at least 1 year of resistance training
experience may realize a low to moderate augmentation of
power after a conditioning activity. However, to optimize
these effects, we suggest the use of multiple sets, moderate
intensity (60–85% 1RM Loads), and rest periods lasting
7–10 minutes in length.
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