Give it a Rest: A systematic review with Bayesian meta-analysis on the effect of inter-set rest interval duration on muscle hypertrophy

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Give it a Rest: A
systematic review with
Bayesian meta-analysis
on the effect of inter-set
rest interval duration on
muscle hypertrophy
For correspondence:
brad.schoenfeld@lehman.cuny.edu
Alec Singer1, Milo Wolf1, Leonardo Generoso1, Elizabeth Arias1, Kenneth Delcastillo1, Edwin
Echevarria1, Amaris Martinez1, Patroklos Androulakis Korakakis1, Martin Refalo2, Paul A.
Swinton3, *Brad J. Schoenfeld1
1. Department of Exercise Science and Recreation, Applied Muscle Development Lab,
CUNY Lehman College, Bronx, NY
2. Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition
Sciences, Deakin University, Geelong, Australia
3. Department of Sport and Exercise, School of Health Sciences, Robert Gordon
University, Aberdeen, United Kingdom
Please cite as:. Singer, A., Wolf, M., Generoso, L., Arias, E., Delcastillo, K., Echevarria, E., Martinez,
A., Korakakis, P.A., Refalo, M.C., Swinton, P.A., Schoenfeld, B.J. (2024). Give it a rest: A systematic
review with meta-analysis on the effect of inter-set rest interval duration on muscle
hypertrophy. SportRχiv.

1
ABSTRACT
We systematically searched the literature for studies with a randomized design that
compared different inter-set rest interval durations for estimates of pre-/post-study
changes in lean/muscle mass in healthy adults while controlling all other training
variables. Meta-analyses on non-controlled effect sizes using hierarchical models of all
19 measurements (thigh: 10; arm: 6; whole body: 3) from 9 studies meeting inclusion
criteria analyses showed substantial overlap of standardized mean differences across
the different inter-set rest periods (binary: short: 0.48 [95%CrI: 0.19 to 0.81], longer:
0.56 [95%CrI: 0.24 to 0.86]; Four categories: short: 0.47 [95%CrI: 0.19 to 0.80],
intermediate: 0.65 [95%CrI: 0.18 to 1.1], long: 0.55 [95%CrI: 0.15 to 0.90], very long:
0.50 [95%CrI: 0.14 to 0.89]), with substantial heterogeneity in results. Univariate and
multivariate meta-analyses of controlled effect sizes showed similar results for the arm
and thigh with central estimates favoring longer rest periods (arm: 0.13 [95%CrI: -0.27
to 0.51]; thigh: 0.17 [95%CrI: -0.13 to 0.43]). In contrast, central estimates closer to
zero but favoring shorter rest periods were estimated for the whole body (whole body:
-0.08 [95%CrI: -0.45 to 0.29]). Subanalysis of set end-point data indicated that training
to failure or stopping short of failure did not meaningfully influence the interaction
between rest interval duration and muscle hypertrophy. In conclusion, results suggest
a small hypertrophic benefit to employing rest interval durations >60 seconds with
unclear effects as to durations >90 seconds.
KEYWORDS: rest period; recovery interval; muscle growth; muscle development;
muscle thickness; muscle cross-sectional area

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INTRODUCTION
It has been proposed that the manipulation of resistance training (RT) program
variables can help to optimize skeletal muscle hypertrophy (2). However, because of the
onerous time commitment involved in conducting directly supervised longitudinal RT protocols,
most research on the effects of manipulation of program variables have recruited relatively
small sample sizes. Thus, meta-analytic techniques that pool and explore the results of all
relevant studies on a given topic can provide additional insights on the topic by quantifying the
magnitude of effects, which may help to guide prescription. To date, relatively recent meta-
analyses have investigated the effect of manipulating a variety of RT program variables on
muscle hypertrophy outcomes including load (23), volume (36), frequency (38), and proximity
to failure (32), furthering our understanding of their practical implications.
The rest interval, operationally defined herein as the duration between sets during RT,
is thought to be an important variable for promoting skeletal muscle hypertrophy. The National
Strength and Conditioning Association recommends relatively short rest periods (30 to 90
seconds) to optimize muscle hypertrophy (15). This is largely based on acute research showing
that short rest periods enhance the post-exercise hormonal response to RT, which has been
theorized to promote muscular adaptations (20). However, emerging research suggests that
transient post-exercise hormonal elevations may not play an important role in eliciting
hypertrophy (27) (28), which calls into question the benefit of short rest intervals for optimizing
muscle development. Indeed, McKendry et al. (24) reported that short rest intervals (1 min)
blunted the myofibrillar protein synthetic response to RT compared to longer rest intervals (5
min) despite higher acute testosterone elevations in the short-rest condition.
Longitudinal research investigating the influence of rest intervals on muscle
hypertrophy has been largely equivocal. A systematic review by Grgic et al. (14) concluded that
both short and long inter-set rest periods are viable options for untrained individuals seeking
to optimize hypertrophy, but that longer durations may be advantageous for those with

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previous RT experience. It should be noted that this review was published in 2017 and
additional research has been conducted on the topic since that time. Moreover, no study to
date has endeavored to quantify the magnitude of effect between different rest interval
conditions to determine if differences may be practically meaningful for RT prescription.
Therefore, the purpose of this study was to systematically review the literature and perform a
Bayesian meta-analysis of the existing data on the effects of rest interval duration during
resistance training on measures of muscle hypertrophy.
METHOD
We conducted this review in accordance with the guidelines of the “Preferred Reporting
Items for Systematic Reviews and Meta-Analyses” (PRISMA) . The study was preregistered on
the Open Science Framework (https://osf.io/ywevc).
Search strategy
To identify relevant studies for the topic, we conducted a comprehensive search of the
PubMed/MEDLINE, Scopus, and Web of Science databases using the following Boolean search
syntax: ("rest interval" OR “inter-set rest” OR "interset rest" OR "rest period*" OR "rest between
sets" OR "resting interval" OR "resting period" OR “recovery interval”) AND ("resistance training"
OR "resistance exercise" OR "weight lifting" OR "weightlifting" OR "strength exercise" OR
"strength training" OR "strengthening" OR "resistive exercise" OR "resistive training") AND
("muscle hypertrophy" OR "muscular hypertrophy" OR "muscle mass" OR "lean body mass" OR
"fat-free mass" OR "fat free mass" OR "muscle fiber" OR "muscle size" OR "muscle fibre" OR
"muscle thickness" OR "cross-sectional area" OR "computed tomography" OR "magnetic
resonance imaging" OR “ultrasound” OR “DXA” OR “DEXA” OR “bioelectrical impedance
analysis”). As previously described (30), we also screened the reference lists of articles retrieved
and applicable review papers, as well as tapped into the authors’ personal knowledge of the
topic, to uncover any additional studies that might meet inclusion criteria (13). Moreover, we

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performed secondary “forward” and “backward” searches for citations of included studies in
Google Scholar.
As previously described, the search process was conducted separately by 3 researchers
(LG, AS and MR). Initially, we screened all titles and abstracts to uncover studies that might
meet inclusion/exclusion criteria using online software (https://www.rayyan.ai/). If a paper was
deemed potentially relevant, we scrutinized the full text to determine whether it warranted
inclusion. Any disputes that could not be resolved by the search team were settled by a fourth
researcher (BJS). The search was finalized in March 2024.
Inclusion criteria
We included studies that satisfied the following criteria: (a) had a randomized design
(either within- or between-group design) and compared different inter-set rest interval
durations for estimates of pre-/post-study changes in lean/muscle mass using a validated
measure (dual-energy X-ray absorptiometry [DXA], bioelectrical impedance analysis, magnetic
resonance imaging [MRI], computerized tomography [CT], ultrasound, muscle biopsy or limb
circumference measurement) in healthy adults (≥18 years of age) of any RT experience while
controlling all other training variables (in the case of volume, this represented either sets per
muscle per session or volume load per session [i.e., sets x repetitions x load]
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; (b) involved at
least 2 RT sessions per week for a duration of at least 4 weeks; (c) published in a peer-reviewed
English language journal or on a preprint server. We excluded studies that (a) included
participants with co-morbidities that might impair the hypertrophic response to RT
(musculoskeletal disease/injury/cardiovascular impairments); (b) employed unequal dietary
supplement provision (i.e., one group received a given supplement and the other received an
alternative supplement/placebo).
Data extraction
1
In cases where studies equated sets between conditions, fewer repetitions may have been performed
in the shorter rest conditions over multiple sets of a given exercise.

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Three researchers (KD, EA and MW) independently extracted and coded the following
data for each included study: Author name(s), title and year of publication, sample size,
participant characteristics (i.e. sex, training status, age), description of the training intervention
(duration, volume, frequency, modality), nutrition controlled (yes/no), method for lean/muscle
mass assessment (i.e. DXA, MRI, CT, ultrasound, biopsy, circumference measurement), and
mean pre- and post-study values for lean/muscle mass with corresponding standard
deviations. In cases where rest periods fluctuated over time, we averaged values to report a
mean. In cases where measures of changes in lean/muscle mass were not reported, we
attempted to contact the corresponding author(s) to obtain the data as previously described
(30). If unattainable, we extracted the data from graphs (when available) via online software
(https://automeris.io/WebPlotDigitizer/). To account for the possibility of coder drift, a third
researcher (AS) recoded 30% of the studies, which were randomly selected for assessment (5).
Per case agreement was determined by dividing the number of variables coded the same by
the total number of variables. Acceptance required a mean agreement of 0.90. Any
discrepancies in the extracted data were resolved through discussion and mutual consensus
of the coders.
Methodological quality
The methodological quality of the included studies was assessed using the “Standards
Method for Assessment of Resistance Training in Longitudinal Designs” (SMART-LD) scale (30).
The SMART-LD tool consists of 20 questions that address a combination of study bias and
reporting quality as follows: general (items 1-2); participants (items 3–7), training program
(items 8–11), outcomes (items 12–16), and statistical analyses (17–20). Each item in the
checklist is given 1 point if the criterion is sufficiently displayed or 0 points if the criterion is
insufficiently displayed. The values of all questions are summed, with the final total used to
classify studies as follows: “good quality” (16–20 points); “fair quality” (12–15 points); or “poor

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quality” (≤ 11). Three reviewers (EE, AM and PAK) independently rated each study using the
SMART-LD tool; any disputes were resolved by majority consensus.
Statistical analyses
All meta-analyses were conducted within a Bayesian framework enabling the results to
be interpreted more intuitively compared to a standard frequentist approach through use of
posterior probabilities (21). A Bayesian framework avoids dichotomous interpretations of
meta-analytic results regarding the presence or absence of an effect (e.g., with p values), and
instead places greater emphasis on describing the most likely values for the average effect (21)
while addressing practical questions such as which inter-set rest interval duration is likely to
create the greatest muscle hypertrophy. To facilitate comparisons across the inter-set rest
interval spectrum, durations were categorized using two sets of cut-points. The first was a
binary categorization of shorter (duration ≤ 60 s) and longer (duration > 60 s), and the second
comprised four categories (short: duration ≤ 60 s; intermediate: 60 s < duration < 120 s; long:
120 s ≤ duration < 180 s; and very long: duration ≥ 180 s). Due to the use of different
measurement technologies, effect sizes were quantified by using standardized mean
differences (SMDs). To account for the small sample sizes generally used in strength and
conditioning, a bias correction was applied (25). The primary measure for this meta-analysis
was controlled magnitude-based SMDs obtained by subtracting the baseline change of one
inter-set rest interval category from another and dividing by the pre-intervention pooled
standard deviation (25). To assess the overall effectiveness of the interventions included, initial
analyses were conducted using non-controlled SMDs (26). Interpretation of the magnitude of
effect sizes was facilitated by comparison to small, medium, and large thresholds developed
for strength and conditioning outcomes (43).
Three-level hierarchical models were used with inter-set rest interval included as a
categorical variable to summarize the results using non-controlled SMDs. Pairwise (direct
comparisons only) and network (direct and indirect comparisons) meta-analysis approaches

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were then used with controlled SMDs to compare across the binary and four category
representations, respectively. Univariate analyses separated by measurement site (whole body,
thigh, or arm) were also conducted. For the direct comparison, multivariate analysis was also
conducted allowing for correlations between measurement sites. Network meta-analyses are
becoming increasingly common in evidence synthesis and are most used to compare
qualitatively different treatments where individual studies are unlikely to directly compare all
levels (12). The technique calculates pairwise effect sizes from studies comparing two levels
(direct evidence) and generates indirect evidence comparing other levels through a common
comparator (12). To summarize potential differences in hypertrophy across all inter-set rest
interval categories in a network, the Surface Under the Cumulative Ranking curve (SUCRA; (35)
was used. For each category a SUCRA value expressed as a percentage was calculated
representing the likelihood that muscle hypertrophy was highest or among the highest relative
to other categories. Where applicable, we reported probabilities as p-values representing the
proportion of the distribution that exceeded zero.
Informative priors were used for all models. For the hierarchical meta-regressions, the
mean pre to post intervention change included an informative prior obtained from a large
meta-analysis of strength and conditioning outcomes expressed in terms of SMDs (ref). For
controlled effect sizes, similar research in strength and conditioning conducted with
comparative effect sizes was used. For the between-studies standard deviation, informative
priors were based on an analysis of the predictive distributions generated from a large number
of previous meta-analyses (33). It is a common limitation in meta-analyses using SMDs from
intervention change scores to use a fixed value for the pre- to post-study correlation (e.g. a
value of 0.7) not based on any empirical data (6). To account for this limitation, the sampling
error for each study was estimated using an informative uniform prior with lower bound based
on the value calculated with a correlation of 0.9 and the upper bound based on the value

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calculated with a correlation of 0.5. All analyses were performed in R, using the R2OpenBUGS
package (41) for Bayesian sampling.
Results
We initially screened 359 studies and identified 11 that potentially met inclusion
criteria. After reviewing the full texts of these studies, 2 studies were excluded: one because
neither set volume nor volume load was equated between conditions (1) and the other
because the loading range was not equated in the initial set of the given exercise(s) (10). Figure
1 provides a flow chart of the search process.

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Figure 1: PRISMA 2020 flow diagram for new systematic reviews which included searches of
databases and registers only.
Study Characteristics
Eight studies employed young participants (18-35 years of age) (29) (3) (11) (22) (16) (8)
(40) (37) and 1 employed older participants (>65 years of age) (45). Six studies employed
untrained participants (29) (3) (11) (16) (22) (45) and 3 studies employed resistance-trained

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participants (8) (40) (37). Six studies employed male participants (29) (3) (8) (40) (37) (45), 1
study employed female participants (16), 1 study employed both male and female participants
(22), and 1 study did not specify the sex of participants (11). Three studies assessed total body
measures of hypertrophy (29) (3) (45), 5 studies assessed upper body measures of
hypertrophy (biceps brachii and triceps brachii) (3) (11) (8) (40) (37), and 7 studies assessed
lower body measures of hypertrophy (quadriceps femoris and total thigh) (3) (11) (22) (16) (8)
(40) (37). The duration of the included studies ranged from 5 to 10 weeks. Table 1 provides a
descriptive overview of each study’s methodological design.
Table 1. Summary of the methods of included studies.
Study
Sample
Design
RT Protocol
Hypertrophy
Measure
Duration
Buresh et al.
(2009)
12 young,
untrained
men
Parallel group random
assignment to 1 of 2
groups: (1) 60 sec RI;
(2) 150 sec RI
TB protocol
performed 2 d/wk
consisting of 2–3 sets
of 10 repetitions per
exercise
- Hydrodensitometry:
FFM
- Skinfold and CIR:
CSA of arm and thigh
10 wks
de Souza et
al. (2010)
20 young,
resistance-
trained
men
Parallel group random
assignment to 1 of 2
groups: (1) 120 sec RI;
(2) RI decreasing from
120 sec to 30 sec
(mean RI = ~80 sec)
TB protocol
performed 6 d/wk
consisting of 3-4 sets
of 8-12 repetitions
per exercise
- MRI: CSA of arm
and thigh
8 wks
Fink et al.
(2016)
21 young,
untrained
individuals
Parallel group random
assignment to 1 of 2
groups: (1) 30 sec RI;
(2) 150 sec RI
4 sets of squats and
bench performed 2
d/wk at 40% 1RM
- MRI: CSA of triceps
brachii and thigh
8 wks
Hill-Haas et
al. (2007)
18 young,
untrained
women
Parallel group random
assignment to 1 of 2
groups: (1) 20 sec RI;
(2) 80 sec RI
TB protocol
performed 3 d/wk
consisting of 2–5 sets
of 15-20 repetitions
per exercise
- CIR: thigh
5 wks
Longo et al.
(2022)
28 young,
untrained
men and
women
Within-participant
random assignment of
legs to 1 of 4
conditions: (1) 60 sec
RI; (2) 180 sec RI; (3) 60
3 sets of leg press
performed 2 d/wk at
80% 1RM
- MRI: CSA of
quadriceps femoris
10 wks

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sec RI with VL equated
to long RI; (4) 180 sec
RI with VL equated to
short RI
Piirainen et
al. (2011)
21 young,
untrained
men
Parallel group random
assignment to 1 of 2
groups: (1) 55 secs RI;
(2) 120 sec RI
TB protocol
performed 3 d/wk
consisting of 3 sets of
10-20 repetitions per
exercise
- BIA: FFM
7 wks
Schoenfeld
et al. (2016)
21 young,
resistance-
trained
men
Parallel group random
assignment to 1 of 2
groups: (1) 60 secs RI;
(2) 180 sec RI
TB protocol
performed 3 d/wk
consisting of 3 sets of
8-12 repetitions per
exercise
- US: MT of biceps
brachii, triceps
brachii, quadriceps
femoris
8 wks
Souza-Junior
et al. (2011)
22 young,
resistance-
trained
men
Parallel group random
assignment to 1 of 2
groups: (1) 120 sec RI;
(2) RI decreasing from
120 sec to 30 sec
(mean RI = ~80 sec)
TB protocol
performed 6 d/wk
consisting of 3-4 sets
of 8-12 repetitions
per exercise
- MRI: CSA of upper
arm and thigh
8 wks
Villanueva et
al. (2014)
22 older,
untrained
men
Parallel group random
assignment to 1 of 2
groups: (1) 60 secs RI;
(2) 240 sec RI
TB protocol
performed 3 d/wk
consisting of 2-3 sets
of 4-6 repetitions per
exercise
- DXA: FFM
8 wks
RI = rest interval; TB = total body; VL = volume load; FFM = fat-free mass; MT = muscle thickness; CIR =
circumference; US = ultrasound; VM = vastus medialis; DXA: dual-energy x-ray absorptiometry; MRI =
magnetic resonance imaging; BIA = bioelectrical impedance analysis
Meta-analysis of non-controlled effect sizes
Meta-analyses on non-controlled effect sizes using hierarchical models of all 19
measurements (thigh: 10; arm: 6; whole body: 3) from nine studies are presented in figures 2
and 3. Both meta-analyses showed substantial overlap of SMDs across the different inter-set
rest periods (Binary: short: 0.48 [95%CrI: 0.19 to 0.81], longer: 0.56 [95%CrI: 0.24 to 0.86]; Four
categories: short: 0.47 [95%CrI: 0.19 to 0.80], intermediate: 0.65 [95%CrI: 0.18 to 1.1], long:
0.55 [95%CrI: 0.15 to 0.90], very long: 0.50 [95%CrI: 0.14 to 0.89]), with substantial
heterogeneity in results. Central estimates suggested that improvements across the

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interventions were most likely to be between medium and large, highlighting that interventions
included in this review were generally effective irrespective of rest interval duration.
Figure 2: Meta-analysis of non-controlled effect sizes separated by binary categorization of inter-
set rest period.
Plots illustrate shrunken posterior distribution of effect sizes following application of meta-analytic model. Circle:
Median, error bars represent 75 and 95% credible intervals. Small, medium, and large effect size thresholds are
presented according to previous research in strength and conditioning (43).

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Figure 3: Meta-analysis of non-controlled effect sizes separated by short to very long
categorization of inter-set rest period.
Plots illustrate shrunken posterior distribution of effect sizes following application of meta-analytic model. Circle:
Median, error bars represent 75 and 95% credible intervals. Small, medium, and large effect size thresholds are
presented according to previous research in strength and conditioning (43)..
Meta-analysis of controlled effect sizes
Univariate and multivariate meta-analyses of controlled effect sizes were conducted for
outcomes separated by body region (arm, thigh, whole body; figures 4-6). Direct pairwise
comparisons with binary categorization showed similar results for the arm and thigh with
central estimates slightly favoring longer rest periods (arm: 0.13 [95%CrI: -0.27 to 0.51]; 𝜏: 0.10
[95%CrI: 0.02 to 0.31], Figure 4; thigh: 0.17 [95%CrI: -0.13 to 0.43]; tau: 0.17 [95%CrI: 0.02 to
0.22], Figure 5). In contrast, central estimates closer to zero but slightly favoring shorter rest
periods were estimated for the whole body (whole body: -0.08 [95%CrI: -0.45 to 0.29]; tau: 0.08
[95%CrI: 0.02 to 0.27], Figure 6). Application of the multivariate meta-analysis model resulted in

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slight reductions in uncertainty with smaller central estimates all modestly favoring longer rest
periods (arm: 0.11 [95%CrI: -0.26 to 0.48]; thigh: 0.16 [95%CrI: -0.13 to 0.41]; whole body: 0.03
[95%CrI: -0.28 to 0.36]).
Figure 4: Meta-analysis of controlled effect sizes of muscular hypertrophy of the upper arm with
direct comparisons of binary categorization of inter-set rest period.
Plots illustrate shrunken posterior distribution of effect sizes following application of meta-analytic model. Circle:
Median, error bars represent 75 and 95% credible intervals. Small, medium, and large effect size thresholds are
presented according to previous research in strength and conditioning (42). Probability of effect size greater than 0
favoring longer rest period = 0.74; Probability of effect size greater than small favoring longer rest period = 0.45;

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Probability of effect size greater than medium favoring longer rest period = 0.18; Probability of effect size greater than
large favoring longer rest period = 0.03.
Figure 5: Meta-analysis of controlled effect sizes of muscular hypertrophy of the thigh with direct
comparisons of binary categorization of inter-set rest period.
Plots illustrate shrunken posterior distribution of effect sizes following application of meta-analytic model. Circle:
Median, error bars represent 75 and 95% credible intervals. Small, medium, and large effect size thresholds are
presented according to previous research in strength and conditioning (42). Probability of effect size greater than 0
favoring longer rest period = 0.88; Probability of effect size greater than small favoring longer rest period = 0.54;
Probability of effect size greater than medium favoring longer rest period = 0.15; Probability of effect size greater than
large favoring longer rest period = 0.01.

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Figure 6: Meta-analysis of controlled effect sizes of muscular hypertrophy of the whole body with
direct comparisons of binary categorization of inter-set rest period.
Plots illustrate shrunken posterior distribution of effect sizes following application of meta-analytic model. Circle:
Median, error bars represent 75 and 95% credible intervals. Small, medium, and large effect size thresholds are
presented according to previous research in strength and conditioning (42). Probability of effect size greater than 0
favoring short rest period = 0.69; Probability of effect size greater than small favoring short rest period = 0.36;
Probability of effect size greater than medium favoring short rest period = 0.12; Probability of effect size greater than
large favoring short rest period = 0.01.
Controlled effect sizes for the four categories of inter-set rest period were analyzed
with network meta-analyses. Sufficient data were available for univariate analysis of the arm
and thigh. Network structures are presented in Supplemental Figure 1S, with effect size

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estimates combining direct and indirect estimates, and SUCRA values presented in Table 2. In
general, effect size estimates and SUCRA values for both regions of the body indicated greater
effectiveness for rest periods beyond the short categorization.
Table 2: Univariate network meta-analyses combining direct and indirect pairwise comparisons
for hypertrophy at the thigh and arm for the four inter-set rest period categories.
Region
Category
Comparative eect
size (95%CrI)
SUCRA
Arm
Short
-
0.40
Intermediate
0.22 (-0.31 to 0.74)
0.49
Long
-0.02 (-0.43 to 0.37)
0.52
Very long
0.18 (-0.36 to 0.70)
0.60
Thigh
Short
-
0.18
Intermediate
0.13 (-0.31 to 0.58)
0.54
Long
0.01 (-0.39 to 0.41)
0.63
Very long
0.32 (-0.10 to 0.68)
0.64
Comparative effect sizes are expressed relative to the short inter-set rest category. CrI: Credible interval. SUCRA:
Surface Under the Cumulative Ranking curve
Subanalyses
Subanalyses were performed on direct comparisons separating studies based on set
end-point (i.e., training to momentary muscular failure or non-failure) and training status
(specific to designs that included untrained participants). A multivariate analysis comprised of
data from three studies that incorporated training to momentary muscular failure was
conducted for hypertrophy of the thigh (0.31 [95%CrI: -0.03 to 0.61]) and arm (0.04 [95%CrI: -
0.37 to 0.44]). Similarly, a multivariate analysis comprised of data from three studies that
incorporated non-failure RT was conducted for hypertrophy of the thigh (0.27 [95%CrI: -0.02 to
0.51]) arm (0.04 [95%CrI: -0.37 to 0.44]), and whole body (-0.06 [-0.40 to 0.27). Finally, sufficient
data were available to perform a multivariate analysis comprised of data from six studies that
included untrained participants and was conducted for hypertrophy of the thigh (0.17 [95%CrI:

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-0.15 to 0.47]) arm (0.02 [95%CrI: -0.41 to 0.46]), and whole body (-0.05 [-0.43 to 0.26).
Insufficient data were available to subanalyze results in trained individuals.
Methodological qualitative assessment
Qualitative assessment of included studies via the SMART-LD tool showed a mean
score of 15 out of a possible 20 points (range: 12 to 17 points). Four studies were judged to be
of good quality (8) (11) (40) (45), 4 studies were judged to be of fair quality (16) (22) (29) (37),
and 1 study was judged to be of poor quality (3). See table S1 in the supplementary files.
Discussion
Our meta-analysis quantified data from studies that directly compared the effects of
different rest interval lengths on measures of muscle hypertrophy. While the initial meta-
regressions with non-controlled effect sizes highlighted substantial heterogeneity across
studies (figures 2 and 3), they also demonstrated that most interventions were effective in
eliciting hypertrophic adaptations regardless of rest interval duration, with SMDs that could be
considered medium to large in magnitude. Binary categorization comparing shorter (≤60 secs)
with longer (>60 s) rest intervals returned slightly greater central estimates favoring the longer
rest condition (SMD = 0.56 vs 0.48, respectively; figure 2). When further stratifying data, results
showed slight differences between short (SMD = 0.47), intermediate (SMD = 0.65), long (SMD =
0.55) and very long (SMD = 0.50) rest periods (figure 3). These results suggest no clear benefit
to altering rest interval length for the purpose of promoting muscle hypertrophy. However,
given substantial heterogeneity, meta-regressions with small numbers of studies provide
limited ability to draw strong inferences as any differences observed can be the result of
chance imbalances in the distribution of studies. Therefore, the primary inference from this
study was focused on meta-analyses that comprised controlled effect sizes with either direct
pairwise comparisons only (bivariate categorization), or both direct and indirect pairwise
comparisons (four categories) through network models.

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When subanalyzing the effects of rest interval length on hypertrophy of the limbs, the
results suggest a small benefit for rest intervals >60 seconds. For the binary categorization, the
pooled effect size for the arms slightly favored a hypertrophic benefit for longer vs shorter rest
durations (SMD = 0.13). The probability of the effect being greater than zero was 0.74, with
only a 0.45 probability that the difference in effect was greater than small. Similarly, the pooled
effect size for quadriceps femoris modestly favored longer vs shorter durations (SMD = 0.17).
There was a strong probability that this effect was greater than zero (p=0.88), but only a 0.54
probability that the difference in effect was greater than small. Both upper and lower limb
analyses showed a very low probability that differences would be greater than a medium effect
(SMD = 0.18 and 0.15, respectively). Conversely, measures of whole-body hypertrophy showed
slightly greater effects favoring shorter vs longer rest durations (SMD = -0.08, p(>0)=0.69,
p(>small)=0.36); however, with substantial uncertainty due to only three studies providing
whole body data.
Potential discrepancies between findings of hypertrophy of the extremities vs the whole
body may be related to the different methods of assessment. Whole-body measures of muscle
growth were based on estimates of fat-free mass (FFM) via DXA, BIA and hydrodensitometry,
which are often used as a proxy for muscle hypertrophy (4). However, FFM encompasses all
bodily tissues other than fat mass; while alterations in skeletal muscle comprise the majority of
FFM changes that occur during resistance training, other components such as water and
mineral can influence results as well (31). Alternatively, the majority of assessments for the
extremities employed direct measurements of changes in muscle mass via MRI and
ultrasonography. Given that direct assessment methods have been shown to be more
sensitive to detecting resistance training-induced hypertrophy than indirect assessments (9)
(44), the results of our whole-body analysis should be interpreted with caution.
Potential beneficial effects of rest periods ≤ 60 s on muscle hypertrophy may be
attributable to preservation of volume load during a training session. Research indicates that

21
very short rest periods (≤ 60 seconds) appreciably reduce the number of repetitions
performed across multiple sets compared to longer rest durations (19) (39) (34), which could
have a detrimental effect on long-term muscular adaptations. This hypothesis is supported by
Longo et al (22), who reported appreciably greater increases in quadriceps femoris cross-
sectional area when training with 180 vs 60 inter-set rest periods over a 10-week intervention
(13.1% vs 6.8%, respectively); of note, volume load was reduced to a significantly greater extent
in the shorter vs longer rest condition. However, similar hypertrophy was observed with the
performance of additional sets to equate volume load between conditions.
Alternatively, evidence suggests that differences in volume load tend to level off when
comparing rest intervals of 120 vs 180 seconds (34) (19). When compared to very short rest
intervals (≤ 60 s), our univariate network meta-analysis suggested that very long rest intervals
(≥ 180 seconds) provided a modest advantage versus intermediate (61-119 seconds) and long
(120-179 seconds) durations with respect to quadriceps femoris hypertrophy. However, these
data showed a high degree of uncertainty and the U-shaped response between conditions
casts further doubt on the veracity of the finding. Analyses of hypertrophy of the arms did not
show an appreciable effect of rest interval durations beyond intermediate (>60 second)
durations. Future research should explore this topic in greater detail to better determine
whether graded increases in rest interval duration alter muscular adaptations as well as the
extent to which volume load may play a role in the process.
Subanalysis of set end-point data indicated that training to failure or non-failure did not
meaningfully influence the interaction between rest interval duration and muscle hypertrophy.
Central estimates from both analyses suggested a hypertrophic benefit for longer rest periods
in the quadriceps femoris, irrespective of the proximity-to-failure reached during RT. However,
the magnitude of effect was relatively small (SMD = 0.27 and 0.31 for non-failure and failure
conditions, respectively). Alternatively, negligible differences were observed for the influence of
rest interval length in the arms (SMD = 0.04) regardless of set end-point. The findings are

22
somewhat in contrast with data showing that shorter rest periods impair bench press
performance to a greater extent than longer rest periods when training with closer proximities
to failure (18). Further research is needed to better understand the potential discrepancies
between acute and longitudinal outcomes.
Subanalysis of the potential influence of training status on rest interval length showed
that untrained individuals displayed a slight hypertrophic benefit from longer rest periods
when training the quadriceps femoris (SMD = 0.17). However, rest interval length appeared to
have negligible effects on measures of hypertrophy for the arms and whole body in untrained
individuals (SMD = 0.02 and -0.05, respectively). These data are relatively consistent with
findings from a systematic review by Grgic et al. (14) that concluded both shorter and longer
rest durations are equally viable options for promoting hypertrophy in novice trainees. The
systematic review by Grgic et al. (14) also suggested that trained individuals might benefit from
the use of longer rest intervals, conceivably by allowing for a greater volume load across multi-
set protocols. Unfortunately, there was insufficient data to subanalyze results on experienced
lifters, precluding our ability to either confirm or refute this claim. Further research is therefore
needed to better understand how training status may influence the response to rest interval
length.
Conclusion
Pooled analyses of the current body of literature suggest a small benefit to employing
longer versus shorter inter-set rest intervals for muscle hypertrophy. The effect favoring longer
inter-set rest intervals was relatively consistent between the arms and the legs musculature,
and results were not meaningfully influenced by whether RT was performed to failure or non-
failure. These findings are inconsistent with recommendations from the National Strength and
Conditioning Association, which prescribe relatively short rest periods (30 to 90 seconds) for
hypertrophy-related goals (15). Thus, current guidelines regarding rest interval prescription for
achieving muscular hypertrophy warrant reconsideration. It should be noted that while the

23
observed differences in effect are likely to be between zero and small, intervention durations
were relatively short (between 5 to 10 weeks); thus, it is possible that accumulated differences
in muscle mass accretion over longer terms may be more appreciable.
The current evidence remains equivocal as to whether resting more than 90 seconds
between sets further enhances hypertrophic adaptations. Our analysis casts doubt as to any
beneficial effects in this regard. However, given the uncertainty of evidence, additional studies
are needed comparing measures of hypertrophy across a wide spectrum of rest periods to
provide better insights on the topic.
From an applied standpoint, the benefit to employing longer rest periods may be
practically significant for those seeking to optimize hypertrophic adaptations (i.e., bodybuilders,
strength athletes). Although the magnitude of effect between conditions was marginal, even
small alterations in muscular development can potentially make a difference in athletic
outcomes. Alternatively, the results have questionable practical meaningfulness for individuals
seeking to improve overall health and wellbeing. The tradeoff between greater time-efficiency
vs attenuating hypertrophy to a small extent could make shorter rest periods an attractive
option in this population, particularly given the fact that time is often reported as a significant
barrier to exercise participation and adherence (17).
Finally, it is conceivable that autoregulation of rest intervals may be a viable method for
individuals to determine rest interval duration. Preliminary evidence suggests that self-
selecting the time taken between sets can result in similar number of repetitions performed
across multiple sets with greater time-efficiency compared to a fixed 120 second rest interval
(7). This hypothesis warrants further study using longitudinal designs that directly measure
changes in muscle growth.

24
Conflict of interest
BJS serves on the scientific advisory board for Tonal Corporation, a manufacturer of fitness
equipment. All other authors report no competing interests.
Funding information
No funding was received for this manuscript.
Data and Supplementary Material Accessibility
Data and supplementary material are available on the Open Science Framework project page:
https://osf.io/zp6vs/
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