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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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... Subsequently, 25 duplicate records were removed, and 13 meta-analyses were excluded based on their titles and/or abstracts. Nineteen metaanalyses were read in more detail (i.e., full-text) and 14 metaanalyses were included in the umbrella review (Roig et al., 2009;Krieger, 2010;Schoenfeld et al., 2015Schoenfeld et al., , 2016aSchoenfeld et al., , 2017aSchoenfeld et al., ,b,c, 2019aSlysz et al., 2016;Grgic et al., 2017Grgic et al., , 2019Lixandrão et al., 2018;Grgic, 2020;Nunes et al., 2020). ...
... These studies were published between 2009 and 2020 and comprised 178 primary studies corresponding to 4,704 participants. The 14 selected meta-analyses were classified attending to the analyzed variable, differentiating between volume (Krieger, 2010;Schoenfeld et al., 2017a), frequency (Schoenfeld et al., 2019a), intensity (Schoenfeld et al., 2016a(Schoenfeld et al., , 2017cGrgic, 2020), contraction type (Roig et al., 2009;Schoenfeld et al., 2017b), repetition duration (Schoenfeld et al., 2015), exercises order (Nunes et al., 2020), time of day (Grgic et al., 2019), periodization followed and blood-flow restriction (Slysz et al., 2016;Lixandrão et al., 2018). ...
... The methodological quality of the 14 included meta-analyses is presented in Table 2. Nine meta-analyses were categorized as high quality, presenting values of 81 and 88% (i.e., 13 items satisfied) (Schoenfeld et al., 2015(Schoenfeld et al., , 2017a(Schoenfeld et al., ,b, 2019aGrgic et al., 2017Grgic et al., , 2019Lixandrão et al., 2018;Nunes et al., 2020). The remaining meta-analyses were rated as moderate quality, with values between 63 and 75% (i.e., from 10 to 12 items satisfied) (Roig et al., 2009;Krieger, 2010;Schoenfeld et al., 2016aSchoenfeld et al., , 2017cSlysz et al., 2016;Grgic, 2020). According to GRADE, 8 metaanalyses were based on high-quality primary studies (i.e., high GRADE) (Roig et al., 2009;Slysz et al., 2016;Grgic et al., 2017Grgic et al., , 2019Schoenfeld et al., 2017c;Lixandrão et al., 2018;Grgic, 2020;Nunes et al., 2020) while the other 7 meta-analyses did not presented information regarding to quality (Krieger, 2010;Schoenfeld et al., 2015Schoenfeld et al., , 2016aSchoenfeld et al., , 2017aSchoenfeld et al., ,b, 2019a. ...
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This umbrella review aimed to analyze the different variables of resistance training and their effect on hypertrophy, and to provide practical recommendations for the prescription of resistance training programs to maximize hypertrophy responses. A systematic research was conducted through of PubMed/MEDLINE, SPORTDiscus and Web of Science following the preferred reporting items for systematic reviews and meta-analyses statement guidelines. A total of 52 meta-analyses were found, of which 14 met the inclusion criteria. These studies were published between 2009 and 2020 and comprised 178 primary studies corresponding to 4784 participants. Following a methodological quality analysis, nine meta-analyses were categorized as high quality, presenting values of 81-88%. The remaining meta-analyses were rated as moderate quality, with values between 63-75%. Based on this umbrella review, we can state that at least 10 sets per week per muscle group is optimal, that eccentric contractions seem important, very slow repetitions (≥10s) should be avoided, and that blood flow restriction might be beneficial for some individuals. In addition, other variables as, exercise order, time of the day and type of periodization appear not to directly influence the magnitude of muscle mass gains. These findings provide valuable information for the design and configuration of the resistance training program with the aim of optimizing muscle hypertrophy.
... The total training intensity is represented as the product of the total number of sets and repetitions performed in one session multiplied by the load used in each repetition [12,13] and directly depends on the rest interval adopted between the sets. ...
... To respond to the objective of the present study and considering a large amount of information available regarding the prescription of strength training, it was decided to adopt the training standard described by scientific studies with international recognition for each strength training variable, as described in chart II. [12]; Krieger [13]; Bird et al. [15]; Schoenfeld et al. [16]; Perterson et al. [21]; Schoenfeld et al. [23]; Kraemer and Ratamess [24]; Wernbom et al. [26]; Grgic et al. [27]; Bottaro et al. [28]; Hill-Haas et al. [29] Procedures All participants were instructed to answer all questions in the questionnaire, indicating only one of the options and, in case of doubt, when not solved, choose the option "Prefer not to answer (P.N.A). The collection was carried out in predetermined dates and times, during the intervals of the specialization classes and under the supervision of the researcher responsible for the study. ...
... The scientific literature advises that training aimed at hypertrophy should be programmed with loads between 60 to 80% of maximum capacity, with a number of sets greater than 3 and repetitions ranging from 5 to 12, with rest intervals above 60 seconds.Three of the four questions, percentage of load, number of sets and repetitions related to MH were in accordance with the standard above 70% [1,9,11,13,26], with only the question regarding the agreement rest interval being presented below 50%. This high agreement observed between professionals' prescriptions and scientific recommendations can be justified by the fact that it is a training modality widely used among professionals both for health promotion and sports performance. ...
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How to cite: Cantieri FP, Arruda GA, Coledam DHC, Gomes AC, Aranha ACM, Barros MVG, et al. Strength training: the agreement between methodological standards and prescription by fitness professionals. Rev Bras Fisiol Exerc 2022;21(1):15-25. https://doi. ABSTRACT Introduction: The scientific advances have resulted in proposed methodologic standards to assist the prescription of physical exercise, but it is not clear whether there is a practical application of these standards by fitness professionals. Objective: To analyze the agreement between the methodologic standard for strength training and the methodology used by the fitness professionals. Methods: 461 professionals (men = 68.1%), aged 31.3 (± 6.8) years old, from the city of Londrina/PR and São Paulo/SP participated in the study, who filled out a questionaire containing 16 objective questions about strength training methodology. The Binomial test (cutoffs: 50% and 70%) was used for statistical analysis (p < 0.05). Results: Agreement significantly greater than 70% was obtained for 37.5% of the questions when considering agreement greater than 50%, plus 12.5% of the questtions were added. Agreements significantly less than 50% were identified for the number of repetitions for local muscle endurance (33.5%), load percentage for muscle power (39.5%), as well as for the rest interval for local muscle endurance (19.3%), hypertrophy (33.8%) and muscle power (20.3%). Conclusion: In general, the prescriptions indicated by fitness professionals had low agreement with the analyzed methodologic standards. RESUMO Introdução: Avanços científicos resultaram em padrões metodológicos propostos para auxiliar na prescri-ção do exercício físico, porém ainda não está claro se há aplicação prática de tais padrões por profissionais do fitness. Objetivo: Analisar a concordância entre padrões metodológicos para treinamento de força muscular e a metodologia utilizada por profissionais que atuam na área do fitness. Métodos: Participa-ram do estudo 461 profissionais (homens = 68,1%) com média de 31,3 (± 6,8) anos da cidade de Londrina/ PR e São Paulo/SP, que preencheram um questionário contendo 16 questões objetivas sobre metodologia do treinamento de força. O teste Binomial (cutoffs: 50% e 70%) foi utilizado para as análises estatísticas (p < 0,05). Resultados: Concordância significativamente maior que 70% foi obtida para 37,5% das questões. Ao considerar concordância maior que 50% mais 12,5% das questões foram adicionadas. Concordâncias significativamente inferiores a 50% foram identificadas para o número de repetições para a resistência muscular localizada (33,5%), percentual de carga para potência (39,5%), bem como para o intervalo de re-cuperação para resistência muscular localizada (19,3%), hipertrofia (33,8%) e potência (20,3%). Conclusão: A prescrição apontada pelos profissionais que atuam com fitness em geral apresentou baixa concordância com os padrões metodológicos analisados. Palavras-chave: treinamento resistido; exercício; diretrizes práticas; aptidão física; saúde.
... In this sense, it is known that more fatigue produced by resistance training will not always induce more strength gains (Pareja-Blanco et al. 2017, which means that the same stimulus to get stronger was provoked when less fatigue occurred. About hypertrophy measurements, although higher training volume is associated with higher muscle gains (Krieger 2010;Schoenfeld et al. 2017aSchoenfeld et al. , 2019a, it has been shown that there is a volume threshold from which more training volume does not mean more muscle gains, or even less muscle gains (Amirthalingam et al. 2017;Heaselgrave et al. 2019). Knowing that more training volume provoke more fatigue (Bartolomei et al. 2017), these results demonstrate that more fatigue does not always cause more muscle growth. ...
... Note that central fatigue reduces muscle force and attenuates peripheral fatigue by decreasing the neural drive to the muscles ). Related to this, it has been shown that mental fatigue induced by demanding cognitive tasks impairs the volume load performed in lower (de Queiros et al. 2021;Gantois et al. 2021) and upper body (Dorris et al. 2012;Graham et al. 2017) resistance training, being volume load related to hypertrophy gains (Krieger 2010;Schoenfeld et al. 2017aSchoenfeld et al. , 2019a. These effects of mental fatigue on physical performance have been reported regardless the duration of the cognitive task (Giboin and Wolff 2019) and even when the cognitive task was using social networks on a smartphone (Gantois et al. 2021). ...
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Resistance training is frequently performed with the goal of stimulating muscle hypertrophy. Due to the key roles motor unit recruitment and mechanical tension play to induce muscle growth, when programming, the manipulation of the training variables is oriented to provoke the correct stimulus. Although it is known that the nervous system is responsible for the control of motor units and active muscle force, muscle hypertrophy researchers and trainers tend to only focus on the adaptations of the musculotendinous unit and not in the nervous system behaviour. To better guide resistance exercise prescription for muscle hypertrophy and aiming to delve into the mechanisms that maximize this goal, this review provides evidence-based considerations for possible effects of neural behaviour on muscle growth when programming resistance training, and future neurophysiological measurement that should be tested when training to increase muscle mass. Combined information from the neural and muscular structures will allow to understand the exact adaptations of the muscle in response to a given input (neural drive to the muscle). Changes at different levels of the nervous system will affect the control of motor units and mechanical forces during resistance training, thus impacting the potential hypertrophic adaptations. Additionally, this article addresses how neural adaptations and fatigue accumulation that occur when resistance training may influence the hypertrophic response and propose neurophysiological assessments that may improve our understanding of resistance training variables that impact on muscular adaptations.
... 2 As for training volume, there seems to be a dose-response relationship, with an upper threshold for the improvements in muscular strength and hypertrophy. [3][4][5] Traditionally, prescribed resistance training is based on set configurations where the repetitions are performed continuously followed by an inter-set recovery period (e.g. 1-5 minutes rest). ...
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... Schoenfeld et al. (2017) found that a volume over nine sets per week had a larger effect on muscle mass gains. Krieger (2010) compared the number of sets per exercise with its effects on hypertrophy. In the present systematic review with meta-analysis, we made a comparison between 12-20 weekly sets (MV) and over 20 weekly sets (HV) to verify whether this doseresponse relationship exists. ...
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The main goal of this study was to compare responses to moderate and high training volumes aimed at inducing muscle hypertrophy. A literature search on 3 databases (Pubmed, Scopus and Chocrane Library) was conducted in January 2021. After analyzing 2083 resultant articles, studies were included if they met the following inclusion criteria: a) studies were randomized controlled trials (with the number of sets explicitly reported), b) interventions lasted at least six weeks, c) participants had a minimum of one year of resistance training experience, d) participants' age ranged from 18 to 35 years, e) studies reported direct measurements of muscle thickness and/or the cross-sectional area, and f) studies were published in peer-review journals. Seven studies met the inclusion criteria and were included in the qualitative analysis, whereas just six were included in the quantitative analysis. All participants were divided into three groups: "low" (<12 weekly sets), "moderate" (12-20 weekly sets) and "high" volume (>20 weekly sets). According to the results of this meta-analysis, there were no differences between moderate and high training volume responses for the quadriceps (p = 0.19) and the biceps brachii (p = 0.59). However, it appears that a high training volume is better to induce muscle mass gains in the triceps brachii (p = 0.01). According to the results of this review, a range of 12-20 weekly sets per muscle group may be an optimum standard recommendation for increasing muscle hypertrophy in young, trained men.
... Training with velocity loss thresholds > 25% resulted in significantly greater muscle hypertrophy than velocity loss thresholds ≤ 25%. This finding is consistent with the literature corroborating that there is a malleable inverted U-curve of optimal training volume for strength [90] and hypertrophy [91][92][93] that is individual-specific [94]. It is important to recognize that although the velocity loss threshold groups within each study were equated for set volume, velocity loss thresholds > 25% were associated with substantially greater total relative volume [30][31][32]. ...
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Background Autoregulation has emerged as a potentially beneficial resistance training paradigm to individualize and optimize programming; however, compared to standardized prescription, the effects of autoregulated load and volume prescription on muscular strength and hypertrophy adaptations are unclear. Our objective was to compare the effect of autoregulated load prescription (repetitions in reserve-based rating of perceived exertion and velocity-based training) to standardized load prescription (percentage-based training) on chronic one-repetition maximum (1RM) strength and cross-sectional area (CSA) hypertrophy adaptations in resistance-trained individuals. We also aimed to investigate the effect of volume autoregulation with velocity loss thresholds ≤ 25% compared to > 25% on 1RM strength and CSA hypertrophy. Methods This review was performed in accordance with the PRISMA guidelines. A systematic search of MEDLINE, Embase, Scopus, and SPORTDiscus was conducted. Mean differences (MD), 95% confidence intervals (CI), and standardized mean differences (SMD) were calculated. Sub-analyses were performed as applicable. Results Fifteen studies were included in the meta-analysis: six studies on load autoregulation and nine studies on volume autoregulation. No significant differences between autoregulated and standardized load prescription were demonstrated for 1RM strength (MD = 2.07, 95% CI – 0.32 to 4.46 kg, p = 0.09, SMD = 0.21). Velocity loss thresholds ≤ 25% demonstrated significantly greater 1RM strength (MD = 2.32, 95% CI 0.33 to 4.31 kg, p = 0.02, SMD = 0.23) and significantly lower CSA hypertrophy (MD = 0.61, 95% CI 0.05 to 1.16 cm ² , p = 0.03, SMD = 0.28) than velocity loss thresholds > 25%. No significant differences between velocity loss thresholds > 25% and 20–25% were demonstrated for hypertrophy (MD = 0.36, 95% CI – 0.29 to 1.00 cm ² , p = 0.28, SMD = 0.13); however, velocity loss thresholds > 25% demonstrated significantly greater hypertrophy compared to thresholds ≤ 20% (MD = 0.64, 95% CI 0.07 to 1.20 cm ² , p = 0.03, SMD = 0.34). Conclusions Collectively, autoregulated and standardized load prescription produced similar improvements in strength. When sets and relative intensity were equated, velocity loss thresholds ≤ 25% were superior for promoting strength possibly by minimizing acute neuromuscular fatigue while maximizing chronic neuromuscular adaptations, whereas velocity loss thresholds > 20–25% were superior for promoting hypertrophy by accumulating greater relative volume. Protocol Registration The original protocol was prospectively registered (CRD42021240506) with the PROSPERO (International Prospective Register of Systematic Reviews).
... To estimate and compare RE protocols, the term exercise volume load was established, reflecting lifted load × number of sets × number of repetitions. Krieger (2010) compared the effects of single to multiple sets on hypertrophy using a multilevel meta-regression, including eight studies with 322 individuals. The effect size of multiple sets was found to be 44% greater performing multiple sets (ES: 0.35 ± 0.03) in comparison to the performance of a single set (ES: 0.25 ± 0.03) (P < 0.05). ...
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Skeletal muscle is one of the most important tissues of the human body. It comprises up to 40% of the body mass and is crucial to survival. Hence, the maintenance of skeletal muscle mass and strength is pivotal. It is well-established that resistance exercise provides a potent anabolic stimulus to increase muscle mass and strength in men and women of all ages. Resistance exercise consists of mechano-biological descriptors, such as load, muscle action, number of repetitions, repetition duration, number of sets, rest interval between sets, frequency, volitional muscular failure, and range of motion, which can be manipulated. Herein, we discuss the evidence-based contribution of these mechano-biological descriptors to muscle mass and strength.
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Strength training is a kind of practice intended to enhance muscle strength and hypertrophy. To maximize these training adaptations, the appropriate modulation of resistance training variables is needed. Objective: To measure the effect of resistance training on muscle strength and hypertrophy between two groups, group A (05 sets) vs. group B (10 sets) over a period of 14 weeks of training. Methods: The data was collected from gym goers at the University of Lahore, aged between 18-25 years, and had experience of resistance training at a recreational level for one year. Twenty trained athletes were incorporated and purposive sampling was used in this study. The training intervention was conducted over three sessions in a week with one day of transition period for proper recovery from fatigue due to high intensity training. Session one consisted of chest and upper back exercises, session two consisted of leg exercises, and session three contained arms and shoulder exercises. The baseline characteristics of both groups were assessed at the initial stage, including age, height, and total body mass, and after training, the paired-samples t-test was used to assess the mean difference between both groups. Results: The mean difference for paired-samples t-test for anterior thigh muscle thickness of group A was -1.900 while group B had -4.900. Similarly, in strength, the mean difference for paired-samples test for1RM leg press of group A was -39.600 whereas group B had -29.800. The results showed that group A gained significant muscle hypertrophy and strength as compared to group B. Conclusion: It was found that 5 sets on each muscle group in a week with three workout sessions showed better results in order to enhance muscle hypertrophy and strength
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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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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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ABSTRACT: Purpose: This systematic review of randomized controlled trials (RCTs) comparing strength gains following single-set (SS) and multiple-set (MS) resistance-training protocols was performed to formulate therapeutic exercise recommendations. Methods: Systematic search of Cumulative Nursing and Allied Health Index (CINAHL) and MEDLINE databases for RCTs published between January 1980 and January 2003. Main Outcome Measures: Methodological quality scores based on 10 predetermined criteria, with 100% (10/10) indicating highest quality. Results: Quality scores of the 12 reviewed studies ranged from 50% (moderate quality) to 100% (high quality). Seven studies suggest that MS protocols are superior (MS > SS), and 5 suggest there is no difference in strength gains between SS and MS designs (MS = SS). Mean quality scores were 80.0% (high quality) for the pooled MS > SS studies and 70.0% (high quality) for the pooled MS = SS studies. Conclusions: The higher number of MS > SS studies and slightly higher quality of these pooled studies suggests that MS protocols are more effective for healthy individuals. None of these studies involved patients undergoing rehabilitation; thus, it remains to be seen if the inherently greater time, cost, and presumed injury risk with MS protocols is justified for therapeutic exercise.
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Perhaps the most controversial element of any strength training programme is the number of sets required to increase muscular strength and hypertrophy. There is a prevalent belief that at least 3 sets of each exercise are required to elicit optimal increases in strength and hypertrophy. However, most of the studies that reported the results of training with single versus multiple sets do not substantiate this tenet. In fact, the preponderance of evidence suggests that for training durations of 4 to 25 weeks there is no significant difference in the increase in strength or hypertrophy as a result of training with single versus multiple sets. Because of the design limitations of these studies, conclusions concerning the efficacy of multiple sets should be tentative. However, there is little scientific evidence, and no theoretical physiological basis, to suggest that a greater volume of exercise elicits greater increases in strength or hypertrophy. This information may represent an important practical application of time-efficient, low-volume exercise.