Literature Review

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

Article· Literature Review (PDF Available)inThe Journal of Strength and Conditioning Research 24(4):1150-9 · March 2010with 9,214 Reads 
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DOI: 10.1519/JSC.0b013e3181d4d436 · Source: PubMed
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Abstract
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.
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.
24(4)/1150–1159
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Ó2010 National Strength and Conditioning Association
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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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  • ... Dynamic strengthening exercises were prescribed to participants in the conventional intervention. The training protocols were designed to ensure that participants in NST and CST performed similar amounts of work, as volume has been reported as one of the main variables to influence muscular adaptations [44], with previous studies demonstrating a dose-response relationship between volume and hypertrophy [44][45][46]. Therefore, the frequency of training and the total number of contractions (i.e., sets × repetitions) in each session were similar between both the novel intervention (e.g., 82 KE contractions as described above) and conventional intervention (e.g., 80 KE contractions; performed as 5 different exercises of 2 sets × 8 repetitions). ...
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    PurposeTreatment of head and neck cancer (HNC) results in severe weight loss, mainly due to the loss of lean body mass. Consequently, decreases in muscular strength and health-related quality of life (HRQL) occur. This study investigated the feasibility of a 12-week novel strength training (NST) and conventional strength training (CST) intervention delivered after HNC treatment.Methods Participants were randomized to a NST group (n = 11) involving eccentric overloaded strength training and neuromuscular electrical stimulation (NMES), or a CST group (n = 11) involving dynamic resistance exercises matched for training volume. Feasibility outcomes included recruitment, completion, adherence, and evidence of progression. A neuromuscular assessment involving maximal isometric voluntary contractions (MIVCs) in the knee extensors was evaluated prior to and during incremental cycling to volitional exhaustion at baseline and after the interventions. Anthropometrics and patient-reported outcomes (PROs) were also assessed.ResultsAlthough recruitment was challenging, completion was 100% in NST and 82% in CST. Adherence was 92% in NST and 81% in CST. Overall, MIVC increased by 19 ± 23%, muscle cross-sectional area improved 18 ± 22%, cycling exercise time improved by 18 ± 13%, and improvements in HRQL and fatigue were clinically relevant.Conclusions Both interventions were found to be feasible for HNC patients after treatment. Strength training significantly improved maximal muscle strength, muscle cross-sectional area, and PROs after HNC treatment. Future research should include fully powered trials and consider the use of eccentric overloading and NMES during HNC treatment.Implications for Cancer SurvivorsEccentric- and NMES-emphasized strength training may be useful alternatives to conventional strength training after HNC treatment.
  • ... In many studies examining varying rest period lengths, individuals assigned short rest periods performed less total volume than individuals assigned long rest periods due to the fact that with short rest periods there is not enough time for lactate accumulation to be cleared and phosphocreatine to be completely replenished (Baechle & Earle, 2008;McArdle, Katch & Katch, 2008). Theoretically, longer rest periods should lead to more total volume resulting in greater gains in muscle hypertrophy (Krieger, 2010;Lawton, 2006). ...
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    The purpose of this study was to compare the effects of 1-minute versus 3-minute, between-set rest intervals on muscular hypertrophy adaptations in 12 recreationally, resistance-trained males, 18-25 years of age over a 6-week period. Week 1 consisted of dual-energy X-ray absorptiometry (DEXA) scans, and 1-repetition maximum (1RM) testing in the back squat and bench press. Weeks 2 through 5 consisted of four resistance training sessions per week, split between two lower-body and two upper-body workouts. The resistance training protocol started at 3 sets of 10 repetitions at 67% of 1RM and progressed from there. Week 6 consisted of follow-up body composition assessment, using DEXA scan, and 1RM testing of back squat and bench press. The findings of the study revealed no significant differences between the effects of 1-minute and 3-minute rest periods on muscle hypertrophy in recreationally strength-trained males during a 4-week training program.
  • ... However, a recent meta-analysis has shown that when RT is performed to concentric muscle failure, the increases in muscle strength and mass are similar if training is performed with RD ranging from 0.5 to 8 s (Schoenfeld, Ogborn & Krieger, 2015;Davies et al., 2017), which comprises the TUT range of both protocols (i.e., SELF and CON). On the other hand, meta-analyzes showed that higher volume (i.e., number of sets, which directly influences the number of repetitions in the session and VL) produced greater increases in muscle strength (Ralston et al., 2017) and hypertrophy (Krieger, 2010;Schoenfeld, Ogborn & Krieger, 2017). Thus, it is reasonable to speculate that SELF may promote greater muscle strength gains and hypertrophy than CON, which may not happen when the VL (which is considered the most appropriate method for volume quantification when comparing protocols (Schoenfeld & Grgic, 2018)) of SELF is equalized to CON. ...
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    The aim of this study was to compare the effect of self-selected repetition duration (SELF), with and without volume load (VL) equalized with controlled repetition duration (CON) on muscle strength and hypertrophy in untrained males. We used a within-subjects design in which 20 volunteers (age: 24.7 ± 2.9 years) had one leg randomly assigned to CON (i.e., 2 s concentric, 2 s eccentric) and the other to SELF or to self-selected repetition duration with equalized volume load (SELF-EV). One repetition maximum (1-RM) and muscle cross-sectional area (CSA) were measured at baseline (Pre) and after (Post) resistance training (RT; 2×/wk for 8 weeks). For the main study variables (1-RM and muscle CSA), a mixed-model analysis was performed, assuming repetition duration (SELF, SELF-EV and CON), and time (Pre and Post) as fixed factors and the subjects as random factor for each dependent variable (1-RM and CSA). All RT protocols showed significant increases in values of 1-RM from Pre (CON: 73.7 ± 17.6 kg; SELF: 75.9 ± 17.7 kg; and SELF-EV: 72.6 ± 16.9 kg) to Post (CON: 83.4 ± 19.9 kg, effect size (ES): 0.47; SELF: 84 ± 19.1 kg, ES: 0.43; and SELF-EV: 83.2 ± 19.9 kg, ES: 0.57, P < 0.0001). Muscle CSA values increased for all protocols from Pre (CON: 12.09 ± 3.14 cm ² ; SELF: 11.91 ± 3.71 cm ² ; and SELF-EV: 11.93 ± 2.32 cm ² ) to Post (CON: 13.03 ± 3.25 cm ² , ES: 0.29; SELF: 13.2 ± 4.16 cm ² , ES: 0.32; and SELF-EV: 13.2 ± 2.35 cm ² , ES: 0.53, P < 0.0001). No significant differences between protocols were found for both 1-RM and CSA ( P > 0.05). Performing RT with SELF, regardless of VL, was equally effective in inducing increases in muscle strength and hypertrophy compared to CON in untrained men.
  • ... It has been suggested that proper manipulation of RT variables (e.g., load, volume, rest, and frequency) can optimize muscle hypertrophic response (20,28,29,31). Training volume (number of sets or repetitions performed) is one of the variables that has received considerable attention, particularly regarding the number of sets performed per muscle group, because it seems to be associated with the hypertrophic response (15,16,36). In addition, increasing RT volume could ensure a progressive overload, likely preventing the stagnation in gains over time (i.e., overload principle) (1,14). ...
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    Scarpelli, MC, Nóbrega, SR, Santanielo, N, Alvarez, IF, Otoboni, GB, Ugrinowitsch, C, and Libardi, CA. Muscle hypertrophy response is affected by previous resistance training volume in trained individuals. J Strength Cond Res XX(X): 000-000, 2020-The purpose of this study was to compare gains in muscle mass of trained individuals after a resistance training (RT) protocol with standardized (i.e., nonindividualized) volume (N-IND), with an RT protocol using individualized volume (IND). In a within-subject approach, 16 subjects had one leg randomly assigned to N-IND (22 sets·wk, based on the number of weekly sets prescribed in studies) and IND (1.2 × sets·wk recorded in training logs) protocols. Muscle cross-sectional area (CSA) was assessed by ultrasound imaging at baseline (Pre) and after 8 weeks (Post) of RT, and the significance level was set at p < 0.05. Changes in the vastus lateralis CSA (difference from Pre to Post) were significantly higher for the IND protocol (p = 0.042; mean difference: 1.08 cm; confidence interval [CI]: 0.04-2.11). The inferential analysis was confirmed by the CI of the effect size (0.75; CI: 0.03-1.47). Also, the IND protocol had a higher proportion of individuals with greater muscle hypertrophy than the typical error of the measurement (chi-square, p = 0.0035; estimated difference = 0.5, CI: 0.212-0.787). In conclusion, individualizing the weekly training volume of research protocols provides greater gains in muscle CSA than prescribing a group standard RT volume.
  • ... Bien que le renforcement classique en charge améliore les paramètres de force, de vitesse et les qualités pliométriques [15][16][17], les résultats de cette recherche montrent une efficacité supérieure du principe iso-inertiel. Il apparaît judicieux de prendre en compte ce type de renforcement aussi bien sur le plan des performances que sur le plan adaptatif et fonctionnel des structures musculaires et tissulaires. ...
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    Le travail excentrique est très rarement isolé, une multitude de cycles allongement-raccourcissement étant à l'origine du mouvement. Il semblerait que dès la première répétition, le travail iso-inertiel permette de développer une stimulation musculaire proche du maximum, contrairement au renforcement musculaire en charge qui ne semble solliciter que très tardivement la stimulation musculaire maximale.
  • ... This finding is in line with those by Schoenfeld et al. [130,132] showing that when equaling volume, greater hypertrophy was observed in those groups that distributed the sets with a higher weekly frequency (2−3 days) per muscle group. Contrary to these results (ours and previous) there is a meta-analysis supporting the efficacy of greater exercise volume [142], concluding that individuals interested in achieving maximal hypertrophy should perform a minimum of 2-3 sets per exercise, and that possibly 4-6 sets could give even a greater response; yet still, considerable heterogeneity was present in the analysis [143], and the only significant difference was observed when comparing one set with three sets. Nevertheless, even if a minimum number of series seems to be necessary to maximise muscle hypertrophy [144] there also appears to be a threshold by which the increase in the sets of exercise performed per muscle group within a given training session does not necessarily lead to greater muscle growth [144,145]. ...
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    We performed a systematic review and meta-analysis to study all published clinical trial interventions, determined the magnitude of whole-body hypertrophy in humans (healthy males) and observed the individual responsibility of each variable in muscle growth after resistance training (RT). Searches were conducted in PubMed, Web of Science and the Cochrane Library from database inception until 10 May 2018 for original articles assessing the effects of RT on muscle size after interventions of more than 2 weeks of duration. Specifically, we obtain the variables fat-free mass (FMM), lean muscle mass (LMM) and skeletal muscle mass (SMM). The effects on outcomes were expressed as mean differences (MD) and a random-effects meta-analysis and meta-regressions determined covariates (age, weight, height, durations in weeks…) to explore the moderate effect related to the participants and characteristics of training. One hundred and eleven studies (158 groups, 1927 participants) reported on the effects of RT for muscle mass. RT significantly increased muscle mass (FFM+LMM+SMM; Δ1.53 kg; 95% CI [1.30, 1.76], p < 0.001; I2 = 0%, p = 1.00). Considering the overall effects of the meta-regression, and taking into account the participants' characteristics, none of the studied covariates explained any effect on changes in muscle mass. Regarding the training characteristics, the only significant variable that explained the variance of the hypertrophy was the sets per workout, showing a significant negative interaction (MD; estimate: 1.85, 95% CI [1.45, 2.25], p < 0.001; moderator: -0.03 95% CI [−0.05, −0.001] p = 0.04). In conclusion, RT has a significant effect on the improvement of hypertrophy (~1.5 kg). The excessive sets per workout affects negatively the muscle mass gain.
  • ... That is, there seem to be differences in morphofunctional and metabolic responses to different training volumes. The number of sets of an RT program has an important effect on the physiological adaptations occurring because of RT performance, with several studies showing a dose-response relationship between the number of sets and muscular adaptations (5,15,26,31). ...
    Article
    Full-text available
    The purpose of the present study was to investigate the effects of resistance training (RT) performed with a higher versus lower training volume on oxidative stress (OS) biomarkers in older women. Thirty-eight older women (≥ 60 years) were randomly assigned to one of two groups: a group that performed one set per exercise (LV, n = 18), or 3 sets per exercise (HV, n = 20). The whole-body RT consisted of a 12-week RT program involving 8 exercises performed with sets of 10-15 repetitions maximum, 3 days/week. Advanced oxidation protein products (AOPP), total radical-trapping antioxidant potential (TRAP) and ferrous oxidation-xylenol orange (FOX) were used as OS biomarkers. The composite Z-score of the percentage changes from pre- to post-training of OS biomarkers according to groups was calculated. A significant main effect of time (p < 0.05) was found for AOPP (LV = -7.3% vs. HV = -12.2%) and TRAP (LV = +1.5% vs. HV = +15.5%) concentrations, without a statistical difference between groups (p > 0.05). A significant group vs. time interaction (p < 0.001) was revealed for FOX (LV = +6.4% vs. HV = -8.9%). The overall analysis indicated higher positive changes for HV than LV (Z-score: HV = 0.41 ± 1.22 vs. LV = -0.37 ± 1.03, p < 0.05). Our results suggest that a greater volume of RT seems to promote superior improvements on OS biomarkers in older women.
  • Article
    A quantitative evaluation of athletic training is one of the most important issues in the sports science, and its optimization problem between fitness and fatigue has to be considered. Although fitness is the gain of athletic training and athletes can acquire extensive fitness by hard training, they cannot continue such training for a long time because of fatigue. After the hard training which exceeds the physical capacity of athletes, a delayed onset muscle soreness (DOMS) appears and they cannot continue the same training hereafter. Because DOMS impedes the progress of training, it must be avoided when the training plan is made. By continuing training for a long period, the performance capacity of athlete body has enhanced and they can endure hard and long training with which it could not tolerate before, and such performance capacity enhancement is called supercompensation. In a planning athletic training, its optimization problem between fitness and fatigue should be discussed considering, both DOMS and supercompensation by forming probabilistic models of fitness and fatigue, optimal training plans which maximize the total fitness during training periods and minimize the occurrence of DOMS are discussed analytically and numerically.
  • Article
    In meta-analysis, study participants are nested within studies, leading to a multilevel data structure. The traditional random effects model can be considered as a model with a random study effect, but additional random effects can be added in order to account for dependent effects sizes within or across studies. The goal of this systematic review is three-fold. First, we will describe how multilevel models with multiple random effects (i.e., hierarchical three-, four-, five-level models and cross-classified random effects models) are applied in meta-analysis. Second, we will illustrate how in some specific three-level meta-analyses, a more sophisticated model could have been used to deal with additional dependencies in the data. Third and last, we will describe the distribution of the characteristics of multilevel meta-analyses (e.g., distribution of the number of outcomes across studies or which dependencies are typically modeled) so that future simulation studies can simulate more realistic conditions. Results showed that four- or five-level or cross-classified random effects models are not often used although they might account better for the meta-analytic data structure of the analyzed datasets. Also, we found that the simulation studies done on multilevel meta-analysis with multiple random factors could have used more realistic simulation factor conditions. The implications of these results are discussed, and further suggestions are given.
Literature Review
  • Article
    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.
  • Article
    Purpose: Our goal was to determine the effects resistance training on circulating IGF-I and on two of its major binding proteins, IGFBP-1 and IGFBP-3. Additional goals were to compare the time course of hormonal changes with the time course of strength changes and to determine the effect of training volume on the extent of hormonal changes, Methods: Thirty-one men and women (mean age = 37 +/- 7 yr) completed a 25-wk, 3 d . wk(-1) program in which they performed single-set resistance training (I-SET, N = 11), multiple-set resistance training (3-SET, N = 11), or no exercise (Control, N = 9). Before training, and after 13 and 25 wk of training, blood hormones were analyzed and strength was assessed as the sum of one-repetition maximum (I-RM) far leg extension and chest press exercises. Results: During the first 13 wk of resistance training, circulating IGF-I increased by approximately 20% in both the I-SET and 3-SET groups (P = 0.041). No further increases occurred between 13 and 25 wk. In the 3-SET group, IGFBP-3 decreased 20% between 13 and 25 wk (P = 0.008). Training did not alter IGFBP-1. Increases in 1-RM strength occurred mainly during the first 13 wk of training and were significantly higher with 3-SET training compared to 1-SET. Conclusions: These findings indicate that increased circulating IGF-I may, at least in part, mediate increases in strength that result from resistance training.
  • 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.
  • Article
    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.