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Single vs. Multiple Sets of Resistance Exercise for Muscle Hypertrophy: A Meta-Analysis

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Previous meta-analyses have compared the effects of single to multiple sets on strength, but analyses on muscle hypertrophy are lacking. The purpose of this study was to use multilevel meta-regression to compare the effects of single and multiple sets per exercise on muscle hypertrophy. The analysis comprised 55 effect sizes (ESs), nested within 19 treatment groups and 8 studies. Multiple sets were associated with a larger ES than a single set (difference = 0.10 +/- 0.04; confidence interval [CI]: 0.02, 0.19; p = 0.016). In a dose-response model, there was a trend for 2-3 sets per exercise to be associated with a greater ES than 1 set (difference = 0.09 +/- 0.05; CI: -0.02, 0.20; p = 0.09), and a trend for 4-6 sets per exercise to be associated with a greater ES than 1 set (difference = 0.20 +/- 0.11; CI: -0.04, 0.43; p = 0.096). Both of these trends were significant when considering permutation test p values (p < 0.01). There was no significant difference between 2-3 sets per exercise and 4-6 sets per exercise (difference = 0.10 +/- 0.10; CI: -0.09, 0.30; p = 0.29). There was a tendency for increasing ESs for an increasing number of sets (0.24 for 1 set, 0.34 for 2-3 sets, and 0.44 for 4-6 sets). Sensitivity analysis revealed no highly influential studies that affected the magnitude of the observed differences, but one study did slightly influence the level of significance and CI width. No evidence of publication bias was observed. In conclusion, multiple sets are associated with 40% greater hypertrophy-related ESs than 1 set, in both trained and untrained subjects.
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SINGLE VS.MULTIPLE SETS OF RESISTANCE
EXERCISE FOR MUSCLE HYPERTROPHY:A
META-ANALYSIS
JAMES W. KRIEGER
Journal of Pure Power, Colorado Springs, CO
ABSTRACT
Krieger, JW. Single vs. multiple sets of resistance exercise for
muscle hypertrophy: a meta-analysis. J Strength Cond Res
24(4): 1150–1159, 2010—Previous meta-analyses have com-
pared the effects of single to multiple sets on strength, but
analyses on muscle hypertrophy are lacking. The purpose of this
study was to use multilevel meta-regression to compare the
effects of single and multiple sets per exercise on muscle
hypertrophy. The analysis comprised 55 effect sizes (ESs),
nested within 19 treatment groups and 8 studies. Multiple sets
were associated with a larger ES than a single set (difference =
0.10 60.04; confidence interval [CI]: 0.02, 0.19; p= 0.016).
In a dose–response model, there was a trend for 2–3 sets
per exercise to be associated with a greater ES than 1 set
(difference = 0.09 60.05; CI: 20.02, 0.20; p= 0.09), and
a trend for 4–6 sets per exercise to be associated with a greater
ES than 1 set (difference = 0.20 60.11; CI: 20.04, 0.43; p=
0.096). Both of these trends were significant when considering
permutation test pvalues (p,0.01). There was no significant
difference between 2–3 sets per exercise and 4–6 sets per
exercise (difference = 0.10 60.10; CI: 20.09, 0.30; p= 0.29).
There was a tendency for increasing ESs for an increasing
number of sets (0.24 for 1 set, 0.34 for 2–3 sets, and 0.44 for
4–6 sets). Sensitivity analysis revealed no highly influential
studies that affected the magnitude of the observed differ-
ences, but one study did slightly influence the level of
significance and CI width. No evidence of publication bias
was observed. In conclusion, multiple sets are associated with
40% greater hypertrophy-related ESs than 1 set, in both trained
and untrained subjects.
KEY WORDS metaregression, effect size, lean body mass,
volume
INTRODUCTION
Resistance training improves musculoskeletal
strength, muscle mass, bone mass, and connective
tissue thickness (22,41). The design of a resistance
training program requires appropriate manipula-
tion of numerous variables, including the frequency, intensity,
and volume of the program (12). For general fitness purposes,
the American College of Sports Medicine has recommended
a program of 1 set of 8–10 exercises covering all major
muscle groups (1). Multiple sets are recommended for
athletic populations (21). In the past, some authors have
argued that a single set per exercise is all that is necessary
for all populations and that further gains are not achieved
by successive sets (8). However, a large number of studies
performed over the past decade have demonstrated greater
strength gains with multiple sets per exercise (6,11,16,18–
20,26–27,30,32,35–36). Also, a recent meta-analysis clearly
showed multiple sets to be associated with 46% greater
strength gains in both trained and untrained subjects (23).
The reason for the greater strength gains with multiple sets
is not well established. Strength training is associated with
both neural and structural adaptations that enhance force
production (22). It is not clear whether the greater strength
gains observed with multiple sets are because of greater
neural adaptations, greater hypertrophy, or both. Some
studies have shown greater hypertrophy with multiple sets
(26,35), whereas others have not (11,27,30–32,40). Measures
of muscle hypertrophy are highly variable and insensitive.
Changes in muscle size are smaller and slower than changes
in strength (28). Many resistance training studies are short in
duration, and subject numbers tend to be small. Because of all
these reasons, the risk of a Type II error is high. For example,
McBride et al. (27) reported greater strength gains in a group
performing 6 sets per exercise compared with a group
performing 1 set per exercise. There were no significant
differences in changes in lean mass between the groups.
However, the study only lasted 12 weeks, and there were
only 9 subjects per group. The mean change in leg lean mass
was nonsignificantly greater in the multiple-set group
compared with the single-set group (0.86 kg vs. 20.05 kg,
respectively). A difference of 0.9 kg in 12 weeks is a
meaningful difference in leg lean mass. Given the sample size
BRIEF REVIEW
Address correspondence to James W. Krieger, jim@jopp.us.
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and the reported SDs, the estimated statistical power to
detect this difference, using a 2-tailed test and an aof 0.05, is
only 12%. Thus, only 12 of 100 studies would detect a
significant difference, if each study only had 9 subjects per
group. If this 0.9-kg difference represents a true difference
between populations, then a Type II error has occurred.
In fact, using an estimated SD of the difference, the study
would need 75–175 subjects per group to detect this 0.9-kg
difference with 80% power. Therefore, underpowered
resistance training studies can potentially lead to incorrect
conclusions regarding the effects of set volume on muscle
hypertrophy, and these erroneous conclusions are only rein-
forced with the publication of more underpowered studies.
Unfortunately, many resistance training studies do not report
power analyses.
Another problem with determining the effects of set
volume on hypertrophy is the many ways in which
hypertrophy can be measured. Studies have used whole-
body lean mass (11,26), regional lean mass (27,35), muscle
thickness (31,40), muscle cross-sectional area (31,35), or
muscle circumference (30–32) to measure hypertrophy.
Different regions of a particular muscle may also be measured
(40). Thus, comparisons across studies can be difficult. The
calculation of a standardized effect size (ES) can aid in the
comparison across studies (3). A meta-analysis of these ESs
can allow for the identification of trends among conflicting
and/or underpowered studies (45). Meta-analyses regarding
the effects of set volume on strength have been published
(23,33–34,44), but none of these analyses looked at measures
of hypertrophy.
The purpose of this paper was to use meta-analysis to
compare the effects of single and multiple sets per exercise on
muscle hypertrophy. A second purpose was to establish
a dose–response effect of set volume on hypertrophy. The
hypothesis was that multiple sets would be associated with
greater hypertrophy compared with single sets.
METHODS
Experimental Approach to the Problem
Studies comparing single with multiple sets per exercise, with
all other variables being equivalent, were eligible for inclusion.
This helped eliminate confounding effects of other training
variables that may affect hypertrophy. To account for
nonindependent ESs and the variation between between
studies, between treatment groups, and between ESs within
each treatment group, multilevel statistical models were used
for the analysis. The dependent variable was the pre to
posttraining change in muscle size. The primary independent
variable was the number of sets per exercise.
Procedures
Study Selection. Searches were performed of PubMed, SPORT
Discus, and CINAHL for English-language studies published
between January 1, 1960, and October 15, 2009. A sample of
keywords and phrases used in searches included ‘‘resistance
training,’’ ‘‘strength training,’’ ‘‘resistance exercise,’’ ‘‘sets,’’ ‘‘single,’’
‘‘multiple,’’ and ‘‘volume’’; Boolean operators such as AND,
OR, and NOT were used to help narrow searches. Hand
searching and crossreferencing were performed from the
bibliographies of previously retrieved studies and from
review articles. Studies were selected if they met the
following criteria: (a) resistance exercise program lasting
a minimum of 4 weeks; (b) training on at least one exercise
for at least one major muscle group; major muscle groups
included the quadriceps, hamstrings, pectoralis major,
latissimus dorsi, biceps, triceps, and deltoids; (c) adults
$19 years; (d) comparison of single to multiple sets per
exercise, with all other training variables being equivalent; (e)
subjects free from orthopedic limitations that could affect
progress on a resistance exercise program; (f ) pre and
posttraining determination of at least one measure of muscle
hypertrophy; these measures included lean body mass,
regional lean mass, muscle cross-sectional area, muscle
circumference, and muscle thickness; (g) sufficient data to
determine sets per exercise and to calculate ESs; and (h)
published studies in English-language journals.
Data Abstraction. Data were tabulated onto a spreadsheet
using Microsoft Excel (Microsoft Corp., Redmond, WA).
Each row represented a specific ES for a treatment group. If
there were multiple ESs for a particular treatment group (i.e.,
a treatment group was subjected to multiple measures of
hypertrophy), then each ES was coded in a separate row.
Variables abstracted from each study were the following:
authors, year, research design (randomized trial, nonrandom-
ized trial, or randomized crossover), n, quality score, sex
(male, female, or mixed), age (19–44 or $45 years), baseline
body mass (kg), resistance exercise experience (,6or$6
months), training program duration (weeks), average repe-
titions per set, training frequency (dwk
21
), sets per exercise,
supervised training (yes/unspecified/no), pre and posttest
means for hypertrophy measures, and pre and posttest SD for
those measures. The study quality score was the sum of
2 scores used in previous reviews to rate the quality of
resistance training studies: a 0–10 scale-based score used by
Ba
˚genhammar and Hansson (2) and a 0–10 scale-based score
used by Durall et al. (10). For the average repetitions per set, if
a range of repetitions was reported (e.g., 8–12 repetitions),
the midpoint of the range was used (e.g., 10 repetitions).
For each measure of hypertrophy in each treatment group,
an ES was calculated as the pretest–posttest change, divided
by the pretest SD (29). Becker (3) recommended the ES for
the control group be subtracted from the experimental group
ES; however, numerous studies in this analysis did not
include a control group. Because it is important to define the
ES in a standard way across all studies (29), the control ES
was assumed to be 0 in all studies and was not subtracted
from the experimental ES. To test this assumption, the mean
control ES was calculated among all studies that had
a control group; the mean ES was 0.0 60.03, which was not
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TABLE 1. Studies included in the analysis.
References Age (y) Sex Status* Length (wks) Type of measure Sets ESSVStudy ES§
Galva
˜o and Taaffe (11) $45 M, F U 20 0.03
n= 16 Lean body mass 1 0.05 0.021
n=16 3 0.08 0.012
Marzolini et al. (26) $45 M, F U 24 0.06
n= 19 Lean body mass 1 0.11 0.004
n=18 3 0.18 0.004
McBride et al. (27) 19–44 M, F U 12 0.12
n= 9 Leg LM
k
120.01 0.040
n=9 3 0.23 0.033
Arm LM 1 0.00 0.040
320.01 0.040
Munn et al. (30) 19–44 M, F U 6 0.02
n= 23 Arm circumference 1 0.24 0.006
n=23 1 0.09 0.006
n=23 3 0.31 0.006
n=23 3 0.07 0.005
Ostrowski et al. (31) 19–44 M T 10 0.20
n= 9 Tricep thickness 1 0.25 0.029
n=9 2 0.40 0.034
n=9 4 0.50 0.029
Rectus Femoris CSA{1 0.30 0.037
2 0.29 0.021
4 0.78 0.027
Rectus Femoris
Thickness 1 0.27 0.040
2 0.14 0.035
4 0.73 0.027
Rhea et al. (32) 19–44 M T 12 0.44
n= 8 Chest circumference 1 0.50 0.015
n=8 3 0.62 0.012
Leg circumference 1 20.13 0.043
3 0.64 0.059
Rønnestad et al. (35) 19–44 M U 11 0.24
n= 10 Quadriceps CSA 1 0.56 0.032
n=11 3 0.67 0.026
Hamstring CSA 1 0.27 0.032
3 0.56 0.026
n= 5 Lower body LM 1 0.14 0.189
n=5 3 0.36 0.189
n= 5 Upper body LM 1 0.25 0.189
n=5 3 0.45 0.189
n= 11 Trapezius CSA 1 0.28 0.026
n=10 3 0.68 0.032
Starkey et al. (40) 19–44 M, F U 14 0.07
Thigh thickness
n= 18 20% Anterior 1 0.05 0.008
n=20 3 0.15 0.008
40% Anterior 1 0.14 0.009
3 0.13 0.007
60% Anterior 1 0.14 0.009
3 0.15 0.008
Medialis 1 0.15 0.010
3 0.14 0.007
Lateralis 1 20.07 0.006
320.08 0.008
20% Lateral 1 0.03 0.009
3 0.37 0.003
(Continued on next page)
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Single vs. Multiple Sets for Muscle Hypertrophy
significantly different from 0 (p= 0.94) when compared using
a one-sample t-test. The sampling variance for each ES was
estimated according to Morris and DeShon (29). Calculation
of the sampling variance required an estimate of the
population ES and the pretest–posttest correlation for each
individual ES. The population ES was estimated by
calculating the mean ES across all studies and treatment
groups (29). The pretest–posttest correlation was calculated
using the following formula (29):
r¼ðs2
1þs2
2s2
DÞ=ð2s1s2Þ;
where s
1
and s
2
are the SDs for the pre and posttest means,
respectively, and s
D
is the SD of the difference scores. Where
s
2
was not reported, s
1
was used in its place. Where s
D
was not
reported, it was estimated using the following formula:
sD¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ððs2
1=nÞþðs2
2=nÞÞ:
q
Statistical Analyses
Meta-analyses were performed using multilevel linear mixed
models, modeling the variation between studies as a random
effect, the variation between treatment groups as a random
effect nested within studies, the variation between ESs as
a random effect nested within treatment groups, and group-
level predictors as fixed effects (15). The within-group
variances were assumed known. Observations were weighted
40% Lateral 1 0.10 0.009
3 0.21 0.007
60% Lateral 1 0.25 0.009
3 0.22 0.008
40% Posterior 1 0.30 0.009
3 0.43 0.007
60% Posterior 1 0.29 0.010
3 0.34 0.008
*U = Untrained (,6 mo resistance exercise experience); T = Trained ($6 mo experience).
Effect size.
Sampling variance.
§Mean study-level effect size (mean multiple-set ES – mean single-set ES).
k
Lean mass.
{Cross-sectional area.
TABLE 2. Full model with all covariates.
Predictor Coefficient 6SE* 95% CI pValue
Multiple sets per exercise
No 0
Yes 0.11 60.04 (0.02, 0.19) 0.016
Intercept0.45 60.10 (0.26, 0.64) ,0.0001
Sex
M0
M, F 20.31 60.08 (20.53, 20.09) 0.017
Training duration (wk) 0.00 60.01 (20.02, 0.01) 0.70
Training experience
,6 mos 0
$6 mos 20.06 60.09 (20.31, 0.19) 0.54
*Positive values for coefficients represent an increase in overall effect size (ES). Negative values represent a decrease in overall ES.
Coefficients of 0 represent the default categories in the model. Coefficients for other categories within the same variable represent the
difference from the default category.
Intercept of the model produced by hierarchical regression.
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by the inverse of the sampling variance (29). An intercept-
only model was created, estimating the weighted mean ES
across all studies and treatment groups. A full statistical
model was then generated. Because of the small number of
studies identified for this analysis (Table 1), the number of
predictors that could be included in the full statistical model
was small. A binary variable (multiple or single sets) was
included as a predictor in the model. Other predictors chosen
for the full model were based on predictors observed to show
weak relationships (p,0.30) to strength in a previous
metaregression that used an identical statistical model (23).
The predictors selected were sex, training duration, and
training experience. Although age showed a weak effect (p=
0.24) in the previous metaregression (23), it was not chosen
for this model as 6 of the 8
studies in this analysis involved
subjects ,44 years of age. The
full model was then reduced by
removing one predictor at
a time, starting with the most
insignificant predictor (7). The
final model represented the
reduced model with the lowest
Bayesian information criterion
(BIC) (37) and that was not
significantly different (p.0.05)
from the full model when
compared with a likelihood ra-
tio test (LRT). Model parame-
ters were estimated by the
method of restricted maximum
likelihood (REML) (43); an
exception was during the
model reduction process, in
which parameters were esti-
mated by the method of
maximum likelihood (ML), as LRTs cannot be used to
compare nested models with REML estimates. Denominator
df for statistical tests and confidence intervals (CIs) were
calculated according to Berkey et al. (5) The multiple-sets
predictor was not removed during the model reduction
process. Because metaregression can result in inflated false-
positive rates when heterogeneity is present and/or when
there are few studies (13), a permutation test described by
Higgins and Thompson (13) was used to verify the
significance of the predictors in the final model; 1,000
permutations were generated. To examine the relationship
between set volume and treatment effect, a dose–response
model was created by replacing the multiple-sets predictor
with a categorical predictor representing the number of sets
TABLE 3. Reduced model.
Predictor Coefficient 6SE* 95% CI pValue Permutation pvalue
Multiple sets per exercise
No 0
Yes 0.10 60.04 (0.02, 0.19) 0.016 0.0
Intercept0.39 60.05 (0.29, 0.49) ,0.0001 N/A
Sex
M0
M, F 20.28 60.05 (20.40, 20.16) 0.0012 0.0
*Positive values for coefficients represent an increase in overall effect size (ES). Negative values represent a decrease in overall ES.
Coefficients of 0 represent the default categories in the model. Coefficients for other categories within the same variable represent the
difference from the default category.
Intercept of the model produced by hierarchical regression.
N/A = Not available; permutation pvalues were only calculated for covariates.
Figure 1. Mean hypertrophy effect size for single vs. multiple sets per exercise. Data are presented as means 6SE.
*Significant difference from 1 set per exercise (p,0.05).
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Single vs. Multiple Sets for Muscle Hypertrophy
performed per exercise: 1 set,
2–3 sets, and 4–6 sets. Adjust-
ment for post hoc multiple
comparisons among set cate-
gories were performed with
a Hochberg correction (14).
Histograms of residuals were
examined to identify major
departures from normality; no
departures from normality
were found. Publication bias
was assessed via a funnel plot
regression method described
by Macaskill et al. (25).
To identify the presence of
highly influential studies that
may have biased the analysis,
a sensitivity analysis was carried
out by removing one study at
a time and then examining the
multiple-sets predictor. Studies
were identified as influential if
their removal resulted in
a change of .1SE in the multiple-sets coefficient. All
analyses were performed using S-PLUS version 8.0 (Insight-
ful, Seattle, WA). Effects were considered significant at p#
0.05. Trends were declared at p#0.10. Data are reported as
means (6SEs) and 95% confidence intervals (CIs).
RESULTS
Study Characteristics
The analysis comprised 55 ESs, nested within 19 treatment
groups and 8 studies (Table 1). The weighted mean ES across
all studies and treatment groups was 0.25 60.06 (CI: 0.13, 0.37).
Full Model
Results for the full model with all predictors are shown in
Table 2. There was a significant effect of sets per exercise
while controlling for all other covariates, with multiple
sets being associated with a larger ES than a single set
(difference = 0.11 60.04; CI: 0.02, 0.19; p= 0.016).
Reduced Model
Results for the reduced model are shown in Table 3. After the
model reduction procedure, only the sex (male or mixed) of
the treatment groups remained as a covariate. The BIC
decreased from 8.9 in the full model to 29.7 in the reduced
model. The reduced model was not significantly different
from the full model (p= 0.73). In the reduced model, multiple
sets were associated with a larger ES than a single set
(difference = 0.10 60.04; CI: 0.02, 0.19; p= 0.016; Table 3).
The mean ES for a single set was 0.25 60.03 (CI: 0.18, 0.32;
Figure 1). The mean ES for multiple sets was 0.35 60.03
(CI: 0.29, 0.41; Figure 1).
Figure 2. Dose–response effect of set volume on hypertrophy. Data are presented as means 6SE. ES = effect
size. *Trend toward difference from 1 set per exercise according to Hochberg-adjusted standard pvalue (p,0.10).
Significantly different from 1 set per exercise according to Hochberg-adjusted permutation pvalue (p,0.01).
TABLE 4. Sensitivity analysis.
Study removed Coefficient* 95% CI pValue Permutation pvalue
None 0.10 60.04 (0.02, 0.19) 0.016 0.0
Galva
˜o and Taaffe (11) 0.11 60.04 (0.02, 0.19) 0.014 0.0
Marzolini et al. (26) 0.11 60.04 (0.02, 0.20) 0.019 0.0
McBride et al. (27) 0.10 60.04 (0.02, 0.19) 0.020 0.001
Munn et al. (30) 0.12 60.05 (0.02, 0.22) 0.016 0.001
Ostrowski et al. (31) 0.10 60.04 (0.01, 0.18) 0.028 0.0
Rhea et al. (32) 0.09 60.04 (0.01, 0.18) 0.033 0.001
Rønnestad et al. (35) 0.09 60.04 (20.01, 0.19) 0.062 0.001
Starkey et al. (40) 0.12 60.06 (0.00, 0.25) 0.043 0.001
*Coefficient 6SE. Value represents difference in effect size (ES) between single and multiple sets per exercise.
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Dose–Response Model
In the dose–response model, there was a trend for 2–3 sets per
exercise to be associated with a greater ES than 1 set per
exercise (difference = 0.09 60.05; CI: 20.02, 0.20; p= 0.09).
The difference was significant when considering the
Hochberg-adjusted permutation test pvalue (p= 0.009).
There was also a trend for 4–6 sets per exercise to be
associated with a greater ES compared with 1 set per exercise
(difference = 0.20 60.11; CI:
20.04, 0.43; p= 0.096). The
difference was significant when
considering the Hochberg-ad-
justed permutation test pvalue
(p= 0.008). There was no
significant difference between
2–3 sets per exercise and 4–6
sets per exercise (difference =
0.10 60.10; CI: 20.09, 0.30;
p= 0.29). There was a tendency
for increasing ESs for an in-
creasing number of sets. The
mean ES for 1-set per exercise
was 0.24 60.03 (CI: 0.18, 0.31;
Figure 2). The mean ES for 2–3
sets per exercise was 0.34 6
0.03 (CI: 0.27, 0.41; Figure 2).
The mean ES for 4–6 sets per
exercise was 0.44 60.09 (CI:
0.26, 0.62; Figure 2).
Sensitivity Analysis
Results for the sensitivity anal-
ysis are reported in Table 4.
The difference in ES between
single and multiple sets was not
affected by more than 1SE for
any study removed. However,
the removal of the study by
Rønnestad et al. (35) changed
the pvalue from 0.016 to 0.06.
The CI was widened to (20.01,
0.19). The pvalue from the
permutation test remained sig-
nificant (p= 0.001).
Publication Bias
There was no significant re-
lationship between treatment
effect and sample size (slope
of line = 20.002 60.002; p=
0.32), indicating no evidence of
publication bias.
DISCUSSION
The purpose of this meta-anal-
ysis was to determine whether
multiple sets per exercise are associated with greater muscle
hypertrophy than a single set per exercise in a resistance
training program. Multiple sets per exercise were associated
with significantly greater ESs in both the full and reduced
statistical models. The mean ES for a single set per exercise
was 0.25, whereas the mean ES for multiple sets was 0.35.
Thus, multiple sets were associated with 40% greater
hypertrophy-related ESs than a single set. According to
Figure 3. Mean strength effect size for single vs. multiple sets per exercise from Krieger (23). Note similarity to
hypertrophy response in Figure 1. Data are presented as means 6SE. *Significant difference from 1 set per
exercise (p,0.05).
Figure 4. Dose–response effect of set volume on strength from Krieger (23). Note similarity to dose–response
effect for hypertrophy in Figure 2. Data are presented as means 6SE. ES = effect size. *Significantly different from
1 set per exercise (p,0.001).
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Single vs. Multiple Sets for Muscle Hypertrophy
Cohen’s classifications for ESs (,0.41 = small; 0.41–0.70 =
moderate; .0.70 = large) (9), both estimates are consistent
with small treatment effects. In a previous meta-analysis on
strength using an identical statistical model (23), 1 set per
exercise was associated with a moderate treatment effect
(mean ES = 0.54), whereas multiple sets were associated with
a large treatment effect (mean ES = 0.80; Figure 3). The
differences in ES estimates for strength vs. hypertrophy are
consistent with the observation that changes in muscle size
are often smaller and slower than changes in strength (28),
particularly in untrained subjects (6 of the 8 studies in the
current analysis involved untrained subjects). The observed
ES difference for sex (a decrease of 0.28 for mixed groups
compared with male groups) is consistent with the
observation that women experience smaller changes in
muscle size compared with men (17).
In a previous meta-analysis on strength using an identical
statistical model, a 46% greater ES was observed for multiple
sets compared with single sets (23) (Figure 3). A 40% greater
ES was observed in this study. This indicates that the greater
strength gains observed with multiple sets are in part because
of greater muscle hypertrophy. It is known that mechanical
loading stimulates protein synthesis in skeletal muscle (39),
and increasing loads result in greater responses until a plateau
is reached (24). It is likely that protein synthesis responds in
a similar manner to the number of sets (i.e., an increasing
response as the number of sets are increased, until a plateau is
reached), although there is no research examining this. The
results of this study support this hypothesis; there was a trend
for an increasing ES for an increasing number of sets. The
response appeared to start to level off around 4–6 sets, as
the difference between 2–3 sets and 4–6 sets was smaller than
the difference between 1 set and 2–3 sets. Also, the difference
between 1 set and 2–3 sets was nearly significant (and the
permutation test pvalue was significant), whereas the differ-
ence between 2–3 sets and 4–6 sets was not. However,
only 2 studies in this analysis involved 4–6 sets per exercise.
Thus, the statistical power to detect differences is low, and
definitive conclusions cannot be made. These results are
similar to a previous meta-analysis on strength, where there
was an increasing response to an increasing number of sets,
with an apparent plateau around 4–6 sets per exercise (23)
(Figure 4).
It has been proposed that the majority of initial strength
gains in untrained subjects are because of neural adapta-
tions rather than hypertrophy (28). The results of this
analysis suggest that some of the initial strength gains
are because of hypertrophy. Given the insensitivity and
variability of hypertrophy measurements, it is likely that
hypertrophy occurs in untrained subjects but is difficult to
detect. This is supported by research that shows increases
in protein synthesis in response to resistance training
in untrained subjects (24). Recent evidence also shows
measurable hypertrophy after only 3 weeks of resistance
exercise (38).
To examine the effects of potential outliers on the outcome,
a sensitivity analysis was performed. The magnitude of the
difference between single and multiple sets was consistent
regardless of which study was removed. However, the
removal of the study by Rønnestad et al. (35) affected the
width of the CI, and the significant effect of multiple sets
turned into a strong trend. However, this is likely because of
loss of statistical power, given that the magnitude of the
estimate remained similar, the permutation test pvalue
remained significant, and the analysis consisted of only 8
studies.
Publication bias represents the problem where studies
showing statistically significant results are more likely to be
published than studies that fail to show significant results (e.g.,
studies showing a significant difference between 1 set and
multiple sets per exercise may be more likely to be published)
(4). Thus, meta-analyses of published studies may over-
estimate the magnitude of a treatment effect (4). Analyses can
be performed to detect the presence of publication bias; one
analysis involves examining the relationship between sample
size and treatment effect (25). The existence of a significant
relationship suggests that publication bias may be present.
However, no such relationship was observed in the current
study. Two previous meta-analyses on the effects of multiple
vs. single sets on strength also failed to observe any evidence
of publication bias (23,44). Also, only 2 of the 8 studies in this
analysis reported significant differences in hypertrophy-
related measures when comparing single with multiple sets
(26,35). This strongly suggests that publication bias is not
present, because if it were, most of the studies would report
significant differences. In fact, even though only 2 of the 8
studies reported significant differences, the mean study-level
ES favored the multiple-set group in all 8 studies (Table 1).
This indicates that many of these studies are underpowered
to detect differences.
There are a number of strengths to the current study
design. First, strict inclusion criteria were used; only studies
comparing single with multiple sets while holding all other
variables constant were included. Second, the multilevel
model allowed for the simultaneous modeling of the variation
between studies, between treatment groups, and between
ESs within each treatment group. Third, both standard and
permutation test pvalues were used to protect against
spurious findings, a common problem with metaregression
(13). Finally, a sensitivity analysis was performed, and this
indicated the mean difference between single and multiple
sets to be reasonably consistent across the removal of
individual studies.
A primary limitation of this analysis is the small number of
studies. Thus, the statistical power of the analysis is limited.
This was evident as the removal of the study by Rønnestad
et al. (35) affected the pvalue and CIs. This was also evident
by the observed trends that did not quite reach statistical
significance (although they were significant according to
permutation tests). The small number of studies also limited
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the number of predictors that could be included in the
statistical model. Thus, interactions between set volume and
factors such as training experience could not be explored, as
had been done in a previous meta-analysis on strength (23).
Also, the majority of studies in this analysis compared 1 set
with 3 sets per exercise; only 2 studies in this analysis
incorporated $4 sets per exercise. This limits the statistical
power to compare 3 sets with greater set volumes, as the SE
for the 4–6 set category was large. Given that the ES for 4–6
sets (0.44) is considered a moderate effect, whereas the ES for
2–3 sets (0.34) is considered a small effect according to
Cohen’s classifications (9), more research involving $4 sets is
needed to clarify whether this is a chance difference or a true
difference. Another limitation is that meta-regression, like
epidemiological research, can only support observational
associations and cannot demonstrate causation (42). A final
limitation is the availability of data (42). Some studies, despite
meeting the design criteria (comparison of single vs. multiple
sets while keeping other variables constant), were excluded
because hypertrophy was not measured. Because an analysis
can only be undertaken for trials where all information is
available, bias can be introduced in the results (42). However,
most of the excluded studies reported greater strength gains
in the multiple-set groups. Given the relationship between
strength and muscle size, the consistency of the mean
difference during the sensitivity analysis, the fact that the
study-level ES favored the multiple-set group in all 8 studies,
and the lack of evidence of publication bias, it is unlikely that
the addition of more studies would alter the results, other
than improving statistical power.
PRACTICAL APPLICATIONS
Multiple sets per exercise were associated with significantly
greater changes in muscle size than a single set per exercise
during a resistance exercise program. Specifically, hyper-
trophy-related ESs were 40% greater with multiple sets
compared with single sets. This was true regardless of subject
training status or training program duration. There was
a trend for an increasing hypertrophic response to an
increasing number of sets. Thus, individuals interested in
achieving maximal hypertrophy should do a minimum of
2–3 sets per exercise. It is possible that 4–6 sets could give
an even greater response, but the small number of studies
incorporating volumes of $4 sets limits the statistical power
and the ability to form any definitive conclusions. If time is
a limiting factor, then single sets can produce hypertrophy,
but improvements may not be optimal. More research is
necessary to compare the effects of 2–3 sets per exercise to
$4 sets. Future research should also focus on the effects of
resistance training volume on protein synthesis and other
cellular and molecular changes that may impact hypertrophy.
Finally, resistance training studies comparing hypertrophic
responses between treatments should include sufficient
numbers of subjects to obtain adequate statistical power to
detect differences; studies should also report power analyses.
ACKNOWLEDGMENTS
The author thanks Dr. Dan Wagman for his help in
obtaining some articles. There were no financial or personal
conflicts of interest and no external funding for this study.
The results of this study do not constitute endorsement by
the National Strength and Conditioning Association.
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... Resistance training is recognised as a safe and effective intervention to counteract age-related declines in the nervous and musculoskeletal systems [9][10][11]. Numerous resistance training programs [12][13][14][15], varying in duration, volume and intensity, have been demonstrated to reduce the loss rate for lean mass, muscle strength and physical function in healthy older adults to some extent. However, the optimal prescription and precise recommendations of resistance training volume (i.e. total amount of work performed), considered by most experts to be a critical training variable [12,14,16], remain inconclusive for healthy older adults. ...
... Numerous resistance training programs [12][13][14][15], varying in duration, volume and intensity, have been demonstrated to reduce the loss rate for lean mass, muscle strength and physical function in healthy older adults to some extent. However, the optimal prescription and precise recommendations of resistance training volume (i.e. total amount of work performed), considered by most experts to be a critical training variable [12,14,16], remain inconclusive for healthy older adults. A higher resistance training volume, compared with a lower resistance training volume, is thought to have a greater potential to induce muscle hypertrophy and improve muscle strength [12,14,16]. ...
... However, the optimal prescription and precise recommendations of resistance training volume (i.e. total amount of work performed), considered by most experts to be a critical training variable [12,14,16], remain inconclusive for healthy older adults. A higher resistance training volume, compared with a lower resistance training volume, is thought to have a greater potential to induce muscle hypertrophy and improve muscle strength [12,14,16]. The rationale behind this is that a higher volume of resistance training, through repetitive cumulative time under tension during each session, can sustain a higher degree of weekly anabolism for muscle hypertrophy [17]. ...
Article
Full-text available
Background The optimal prescription and precise recommendations of resistance training volume for older adults is unclear in the current literature. In addition, the interactions between resistance training volume and program duration as well as physical health status remain to be determined when assessing physical function, muscle size and hypertrophy and muscle strength adaptations in older adults. Objectives This study aimed to determine which resistance training volume is the most effective in improving physical function, lean body mass, lower-limb muscle hypertrophy and strength in older adults. Additionally, we examined whether effects were moderated by intervention duration (i.e. short term, < 20 weeks; medium-to-long term, ≥ 20 weeks) and physical health status (i.e. physically healthy, physically impaired, mixed physically healthy and physically impaired; PROSPERO identifier: CRD42023413209). Methods CINAHL, Embase, LILACS, PubMed, Scielo, SPORTDiscus and Web of Science databases were searched up to April 2023. Eligible randomised trials examined the effects of supervised resistance training in older adults (i.e. ≥ 60 years). Resistance training programs were categorised as low (LVRT), moderate (MVRT) and high volume (HVRT) on the basis of terciles of prescribed weekly resistance training volume (i.e. product of frequency, number of exercises and number of sets) for full- and lower-body training. The primary outcomes for this review were physical function measured by fast walking speed, timed up and go and 6-min walking tests; lean body mass and lower-body muscle hypertrophy; and lower-body muscle strength measured by knee extension and leg press one-repetition maximum (1-RM), isometric muscle strength and isokinetic torque. A random-effects network meta-analysis was undertaken to examine the effects of different resistance training volumes on the outcomes of interest. Results We included a total of 161 articles describing 151 trials (n = 6306). LVRT was the most effective for improving timed up and go [− 1.20 standardised mean difference (SMD), 95% confidence interval (95% CI): − 1.57 to − 0.82], 6-min walk test (1.03 SMD, 95% CI: 0.33–1.73), lean body mass (0.25 SMD, 95% CI: 0.10–0.40) and muscle hypertrophy (0.40 SMD, 95% CI: 0.25–0.54). Both MVRT and HVRT were the most effective for improving lower-limb strength, while only HVRT was effective in increasing fast walking speed (0.40 SMD, 95% CI: − 0.57 to 0.14). Regarding the moderators, our results were independent of program duration and mainly observed for healthy older adults, while evidence was limited for those who were physically impaired. Conclusions A low resistance training volume can substantially improve healthy older adults’ physical function and benefits lean mass and muscle size independently of program duration, while a higher volume seems to be necessary for achieving greater improvements in muscle strength. A low volume of resistance training should be recommended in future exercise guidelines, particularly for physically healthy older adults targeting healthy ageing.
... Weekly RT set volume has been deemed a primary program design variable and therefore received considerable attention (1)(2)(3)(4). Indeed, multiple meta-analyses have reported that the number of RT sets per muscle group per week has a positive dose-response relationship with muscle hypertrophy (1,5,6) and strength gains (7). However, many of these analyses explore a specific range of volumes and have limited conclusions due to a paucity of data at the time of analysis. ...
... The primary meta-regression indicated a positive dose-response relationship whereby higher weekly 'fractional' set volumes resulted in greater muscle hypertrophy, with a 100% posterior probability of the marginal slope exceeding zero. These findings align with previous meta-analytic work (1,5,6,19) (1,4,5,84). Mechanistic data indicates greater-post exercise MPS and intracellular anabolic signaling for higher volume protocols in humans (102,(121)(122)(123)(124)(125); however, a dose-response relationship has been explored using electrically stimulated isometric contractions in male rats, indicating a plateau in MPS but not in p70S6K phosphorylation with additional "sets" (126). ...
Preprint
Full-text available
Background: Weekly set volume and frequency are used to manipulate resistance training (RT) dosage. Previous research has identified higher weekly set volume as enhancing muscle hypertrophy and strength gains, but the nature of the dose-response relationship still needs to be investigated. Mixed evidence exists regarding the effects of higher weekly frequency. Objective: Before meta-analyzing the volume and frequency research, all contributing RT sets were classified as direct or indirect, depending on their specificity to the hypertrophy/strength measurement. Then, weekly set volume/frequency for indirect sets was quantified as 1 for 'total,' 0.5 for 'fractional,' and 0 for 'direct.' A series of multi-level meta-regressions were performed for muscle hypertrophy and strength, utilizing 67 total studies of 2,058 participants. All models were adjusted for the duration of the intervention and training status. Results: The relative evidence for the 'fractional' quantification method was strongest; therefore, this quantification method was used for the primary meta-regression models. The posterior probability of the marginal slope exceeding zero for the effect of volume on both hypertrophy and strength was 100%, indicating that gains in muscle size and strength increase as volume increases. However, both best fit models suggest diminishing returns, with the diminishing returns for strength being considerably more pronounced. The posterior probability of the marginal slope exceeding zero for frequency's effect on hypertrophy was less than 100%, indicating compatibility with negligible effects. In contrast, the posterior probability for strength was 100%, suggesting strength gains increase with increasing frequency, albeit with diminishing returns. Conclusions: Distinguishing between direct and indirect sets appears essential for predicting adaptations to a given RT protocol, such as using the 'fractional' quantification method. This method's dose-response models revealed that volume and frequency have unique dose-response relationships with each hypertrophy and strength gain. The dose-response relationship between volume and hypertrophy appears to differ from that with strength, with the latter exhibiting more pronounced diminishing returns. The dose-response relationship between frequency and hypertrophy appears to differ from that with strength, as only the latter exhibits consistently identifiable effects.
... Among these variables, the number of sets may play an essential role in the RT program since it directly in uences training volume. It is considered important for increasing muscular strength and, mainly, for developing muscle hypertrophy (24,25,26). ...
... There is a consensus in the literature that increases in strength are obtained by increasing the loads lifted in a linear dose-response relationship (2,25,26). However, there is still insu cient evidence regarding muscle hypertrophy and other outcomes. ...
Preprint
Full-text available
BACKGROUND Bioelectrical impedance vector analysis (BIVA) and phase angle (PhA) are important for monitoring hydration, muscle function, and quality of life in older adults. Resistance training (RT) can counteract aging's adverse effects on cellular integrity and function. OBJECTIVE This study compared the effects of RT volume reduction on BIVA and PhA in physically independent older women. Sixty-seven participants (> 60 years) underwent a 20-week standardized whole-body RT program (eight exercises, three sets each, three non-consecutive days per week). They were then randomly assigned to one of three eight-week training conditions: reduced volume to one set (RV1, n = 22), two sets (RV2, n = 24), or maintained volume (MV, n = 21). Bioimpedance spectroscopy measured total body water (TBW), intracellular (ICW), and extracellular (ECW) water, BIVA, and PhA based on resistance (R), impedance (Z), reactance (Xc), and height (H). All groups showed significant increases in TBW, ICW, and ECW during the volume reduction phase (P < 0.05). RESULTS R, R/H, Z, and Z/H decreased across all groups, with significant changes from the pre-conditioning phase in the RV1 and RV2 groups (P < 0.05). Xc and Xc/H increased during the pre-conditioning phase across all groups and returned to baseline during the volume reduction phase (P < 0.05). PhA increased during the pre-conditioning and was maintained during the volume reduction phase (RV1 = + 0.33°, RV2 = + 0.50°, MV = + 0.47°; P < 0.05). CONCLUSION These results suggest that reducing RT volume by up to one-third can still improve PhA, BIVA, and hydration status in older women.
... Just as it is not good to perform all sets of the same exercise, it is not good to perform one set in each exercise. Krieger [92] determined that performing 2-3 sets per exercise provided 40% more hypertrophy than performing only one, while performing 4 to 6 sets already diluted this effect and that although greater hypertrophy was generated by greater volume, this did not have as much increase as in the first sets. Therefore, it is important to perform 2 to 4 sets per exercise. ...
Chapter
Full-text available
The present chapter delves into the topic of muscle hypertrophy in detail, focusing on defining what muscle hypertrophy is, the types of hypertrophy, the mechanisms, and the relationship with resistance training, as well as the variables affecting hypertrophy such as nutrition, rest, exercise selection, training volume, and training frequency, among others. The importance of mechanical tension, metabolic stress, and muscle damage as triggers for muscle hypertrophy is emphasized. Various types of muscle hypertrophy are explored, including connective tissue hypertrophy and sarcoplasmic and myofibrillar hypertrophy. The text also delves into how hypertrophy mechanisms relate to resistance training, highlighting the significance of mechanical tension and metabolic stress as stimuli for muscle hypertrophy. In a practical point of view, the text also discusses factors like nutrition and recovery, highlighting the importance of maintaining a positive energy balance and adequate protein intake to promote muscle growth optimally. Training variables such as exercise selection, exercise order, intensity, volume, frequency, and tempo of execution are discussed in detail, outlining their impact on muscle hypertrophy. The text provides a comprehensive overview of muscle hypertrophy, analyzing various factors that influence the ability to increase muscle mass. It offers detailed information on the biological mechanisms, types of hypertrophy, training strategies, and nutritional and recovery considerations necessary to achieve optimal results in terms of muscle hypertrophy.
... The progression of a weight training program for healthy adults is specific, individualized, and manipulated by variables, such as muscle action, exercises, order, load, volume, interval, speed, and frequency (American College of Sports Medicine, 2009), and must be different between beginners, intermediate, and advanced practitioners. Among training programs, multiple sets have been associated with significant gains in muscle size compared to a single-set program (Krieger, 2010). ...
Article
Full-text available
This study aimed to verify the relationship between changes in thigh muscle‐localized bioelectrical impedance analysis (ML‐BIA) parameters and performance in a multiple‐set exercise. The sample consisted of 30 female university students (22.1 ± 3.2 years). The ML‐BIA parameters, including localized muscle resistance (ML‐R), reactance (ML‐Xc), and phase angle (ML‐AngF), were evaluated using a tetrapolar bioelectric impedance device operating at a frequency of 50 KHz. The multiple sets protocol was performed with an isokinetic dynamometer. For body composition, total and leg lean soft tissue (LST) were evaluated using dual X‐ray absortiometry. Student's t‐test for paired samples was used to compare the ML‐BIA parameters and thigh circumference pre and postexercise. Linear regression analysis was performed to verify the ∆ML‐PhA as a predictor of peak torque for the three sets alone while controlling for total and leg LST. There were differences in the ML‐R (∆ = 0.02 ± 1.45 Ω; p = 0.001; and E.S = 0.19), ML‐Xc (∆ = 2.90 ± 4.12 Ω; p = 0.043; and E.S = 0.36), and thigh circumference (∆ = 0.82 ± 0.60 cm; p < 0.001; and E.S = 0.16) pre‐ and post‐multiple sets. ΔML‐PhA was a predictor of performance in the first set (p = 0.002), regardless of total and leg LST. However, the ΔML‐PhA lost its explanatory power in the other sets (second and third), and the variables that best explained performance were total and leg LST. The ML‐BIA (ML‐R and ML‐Xc) parameters were sensitive and changed after the multiple sets protocol, and the ΔML‐PhA was a predictor of performance in the first set regardless of the total and leg LST.
... In the 8-week resistance training program conducted in this study, free weight exercises involving technical requirements were considered closed kinetic chain exercises, which may have facilitated the development of muscular proprioception, thereby enhancing balance and coordination (Myhre, 2010;Wilson et al., 2018). However, despite the lack of observed differences between set combinations in this study, it can be inferred that compound sets, pyramid sets, and super sets are suitable for increasing 1RM demands, especially for novice individuals lacking exercise experience (Krieger, 2010;Schoenfeld et al., 2014). The analysis of changes in isokinetic muscle function demonstrated significant improvements in shoulder and knee isokinetic muscle function in the pyramid set group compared to the control group. ...
Article
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Introduction This study aimed to evaluate the effects of varied resistance training modalities on physical fitness components, body composition, maximal strength assessed by one-repetition maximum (1RM), isokinetic muscle functions of the shoulder and knee joints, and biomechanical properties of core muscles. Methods Forty participants were randomly assigned to four groups: control group (CG, n = 10), compound set training group (CSG, n = 10), pyramid set training group (PSG, n = 10), and superset training group (SSG, n = 10). Excluding the CG, the other three groups underwent an 8-week resistance training program, three sessions per week, at 60%–80% of 1RM intensity for 60–90 min per session. Assessments included body composition, physical fitness components, 1RM, isokinetic muscle functions, and biomechanical properties (muscle frequency, stiffness, etc.) of the rectus abdominis and external oblique muscles. Results The PSG demonstrated the most significant improvement in relative peak torque during isokinetic testing of the shoulder and knee joints. Compared to the CG, all exercise groups exhibited positive effects on back strength, sprint performance, 1RM, and core muscle biomechanics. Notably, the PSG showed superior enhancement in external oblique stiffness. However, no significant differences were observed among the exercise groups for rectus abdominis biomechanical properties. Discussion Structured resistance training effectively improved maximal strength, functional performance, and core muscle biomechanics. The pyramidal training modality conferred specific benefits for isokinetic muscle functions and external oblique stiffness, suggesting its efficacy in enhancing force production capabilities and core stability.
... Researchers have found that the use of lifting straps directly affects exercise performance, which requires manual grip strength and increases the amount of work performed by the target muscles (Coswig et al., 2015). Additionally, early fatigue of forearm flexor muscles in moderate-to high-load protocols may negatively impact the total volume of the workout (load × repetitions), leading to suboptimal gains in strength and muscle hypertrophy (Krieger 2009;Krieger 2010;Schoenfeld et al., 2017;Schoenfeld et al.,2014). Therefore, owing to the potential negative effects of low grip strength and early forearm fatigue, the use of training equipment such as lifting straps in exercises such as pull-down may be appropriate (Valério etl al., 2021). ...
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Exploring the possible reasons for heterogeneity between studies is an important aspect of conducting a meta-analysis. This paper compares a number of methods which can be used to investigate whether a particular covariate, with a value defined for each study in the meta-analysis, explains any heterogeneity. The main example is from a meta-analysis of randomized trials of serum cholesterol reduction, in which the log-odds ratio for coronary events is related to the average extent of cholesterol reduction achieved in each trial. Different forms of weighted normal errors regression and random effects logistic regression are compared. These analyses quantify the extent to which heterogeneity is explained, as well as the effect of cholesterol reduction on the risk of coronary events. In a second example, the relationship between treatment effect estimates and their precision is examined, in order to assess the evidence for publication bias. We conclude that methods which allow for an additive component of residual heterogeneity should be used. In weighted regression, a restricted maximum likelihood estimator is appropriate, although a number of other estimators are also available. Methods which use the original form of the data explicitly, for example the binomial model for observed proportions rather than assuming normality of the log-odds ratios, are now computationally feasible. Although such methods are preferable in principle, they often give similar results in practice. Copyright © 1999 John Wiley & Sons, Ltd.
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