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Sports Medicine (2024) 54:303–321
https://doi.org/10.1007/s40279-023-01937-7
REVIEW ARTICLE
Maximal Number ofRepetitions atPercentages oftheOne Repetition
Maximum: AMeta‑Regression andModerator Analysis ofSex, Age,
Training Status, andExercise
JamesL.Nuzzo1 · MatheusD.Pinto1 · KazunoriNosaka1 · JamesSteele2
Accepted: 10 September 2023 / Published online: 4 October 2023
© The Author(s) 2023
Abstract
The maximal number of repetitions that can be completed at various percentages of the one repetition maximum (1RM)
[REPS ~ %1RM relationship] is foundational knowledge in resistance exercise programming. The current REPS ~ %1RM
relationship is based on few studies and has not incorporated uncertainty into estimations or accounted for between-individ-
uals variation. Therefore, we conducted a meta-regression to estimate the mean and between-individuals standard deviation
of the number of repetitions that can be completed at various percentages of 1RM. We also explored if the REPS ~ %1RM
relationship is moderated by sex, age, training status, and/or exercise. A total of 952 repetitions-to-failure tests, completed
by 7289 individuals in 452 groups from 269 studies, were identified. Study groups were predominantly male (66%), healthy
(97%), < 59years of age (92%), and resistance trained (60%). The bench press (42%) and leg press (14%) were the most
commonly studied exercises. The REPS ~ %1RM relationship for mean repetitions and standard deviation of repetitions were
best described using natural cubic splines and a linear model, respectively, with mean and standard deviation for repetitions
decreasing with increasing %1RM. More repetitions were evident in the leg press than bench press across the loading spec-
trum, thus separate REPS ~ %1RM tables were developed for these two exercises. Analysis of moderators suggested little
influences of sex, age, or training status on the REPS ~ %1RM relationship, thus the general main model REPS ~ %1RM
table can be applied to all individuals and to all exercises other than the bench press and leg press. More data are needed to
develop REPS ~ %1RM tables for other exercises.
1 Introduction
The number of repetitions that individuals can be expected
to perform to volitional failure at various percentages of the
one repetition maximum (1RM) [i.e., the REPS ~ %1RM
relationship] is foundational knowledge in resistance exer-
cise programming. Investigations related to this topic were
first conducted in the 1950s and 1960s [1–4] and were even-
tually followed by two influential studies by Hoeger etal. in
1987 [5] and 1990 [6].
For many years, a table of the REPS ~ %1RM relationship
has been published in a commonly assigned strength training
textbook (Table1) [7]. This table has been presented as a
general guideline based on a small number of studies [e.g.,
5, 6]. To the best of our knowledge, no attempt has been
made to reaffirm the table, update it, or consider whether it
should be made exercise or population specific. The current
REPS ~ %1RM table provides only point estimates for the
number of repetitions that individuals might be expected to
complete at a given relative load. The table does not incor-
porate the uncertainty of such estimates, nor does it indicate
the expected variation between individuals.
Muscle endurance or “strength endurance,” the attribute
evaluated by a repetitions-to-failure test at a submaximal
loads, may be impacted by sex [8–11], age [12–14], or
muscle group [15]. Thus, potential moderating influences
of sex, age, and muscle group should be considered when
examining the REPS ~ %1RM relationship. Moreover, the
current REPS ~ %1RM table is specific to the concentric
1RM and concentric repetitions-to-failure tests at submaxi-
mal loads. This has occurred because resistance exercise
equipment such as free weights and weight stack machines
involves lifting the same load in the concentric and eccen-
tric phases, and concentric phase strength is ~ 40% less than
eccentric phase strength [16]. Some evidence suggests that
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304 J.L.Nuzzo et al.
Key Points
We applied meta-regression to data from approxi-
mately7000 individuals to update the table of the
maximal number of repetitions completed at various
percentages of one repetition maximum (REPS ~ %1RM
relationship).
Sex, age, and training status did not clearly moderate
the REPS ~ %1RM relationship; thus, estimates of mean
repetitions and between-individuals variation in the main
model table can be applied to most individuals and most
exercises.
Numbers of repetitions completed across the loading
spectrum were higher in the leg press than bench press;
thus, separate REPS ~ %1RM tables were created for
these two exercises.
more eccentric-only than concentric-only repetitions can be
completed at equal relative loads [17]. Thus, when coupled
with the rise in popularity of eccentric resistance exercise
and the emergence of eccentric exercisetechnologies that
permit eccentric-only repetitions [11, 18, 19], the possibility
that the REPS ~ %1RM relationships might differ between
concentric and eccentric muscle actions should be consid-
ered. Examination of the above issues seems possible using
meta-analytic methods given that numerous papers over the
past several decades have included data on repetitions-to-
failure tests at various percentages of the 1RM.
Therefore, the purpose of the current study was to perform
a meta-regression to estimate the maximal number of rep-
etitions that can be performed at various percentages of the
1RM and the variance between individuals in repetitions com-
pleted. More specifically, we aimed to provide an updated and
more comprehensive table of the REPS ~ %1RM relationship
byincorporating uncertainty of estimates from all available
data. A secondary aim was to explore if the REPS ~ %1RM
relationship is moderated by exercise, sex, age, training status,
and muscle action type. Such information might have impli-
cations for resistance exercise prescriptions. For example, it
might provide practitioners with a more accurate expectation
of how many repetitions individuals can be expected to com-
plete at given relative loads. Exploration of moderators might
reveal factors that impact the REPS ~ %1RM relationship, as
measured by repetitions-to-failure tests.
2 Methods
2.1 Literature Search
Our literature search was thorough, but not necessarily sys-
tematic or exhaustive. We used a mixed approach similar to
that described by Greenhalgh and Peacock [20] and imple-
menteded in our previous work [16]. The approach relied on
the investigators’ personal knowledge [21, 22], checking of
personal digital files, relevant keyword searches in PubMed
and Google Scholar, and “snowballing” strategies (i.e., ref-
erence and citation tracking). Example keyword searches
included: “repetitions to failure,” “repetitions to fatigue,”
“repetitions to exhaustion,” “number of repetitions,” “maxi-
mal number of repetitions,” “muscular endurance,” “strength
endurance,” “relative muscle endurance,” “local muscular
endurance,” and “task failure.” Searches were performed
in January and February of 2023 but were otherwise not
limited by publication date. A flow diagram of the search
strategy is presented in Fig.1.
2.2 Eligibility andData Extraction
A study was eligible for inclusion into the meta-analysis if
the following conditions were met: (a) published in English;
(b) published in a paper in a journal; (c) human data; (d) the
1RM was tested rather than estimated; (e) a repetitions-to-
failure testwas performed (i.e., maximal number of repeti-
tions at % 1RM); (f) the test was performed in a non-fatigued
state and without concurrent experimental intervention
Table 1 From a commonly assigned strength training textbook [7],
the maximal number of repetitions that individuals have historically
been thought to complete at various percentages of the one repetition
maximum (1RM) [REPS ~ %1RM relationship]
%1RM Maximal number of
repetitions that can be
completed
100 1
95 2
93 3
90 4
87 5
85 6
83 7
80 8
77 9
75 10
70 11
67 12
65 15
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305
Repetitions~%1RM Relationship
(e.g., blood flow restriction, acute caffeine supplementa-
tion, static stretching); and (g) repetitions were reported as
unadjusted group means with an accompanying estimate of
variance. Both cross-sectional and exercise training studies
were eligible for inclusion. With exercise training studies,
the extracted data were from baseline/pre-intervention tests.
With acute intervention studies, the extracted data were
from either pre-intervention tests or from placebo condi-
tions, depending on the study’s design. In studies in which
participants performed multiple repetitions-to-failure sets
at a given relative load, only data from the first set were
extracted, subsequent sets would have been impacted by
muscle fatigue. Of note, the data reported by Hoeger etal.
in 1987 [5] were later reported in a more extensive paper
in1990 [6]. Thus, only the paper from 1990 was included
in the final list of relevant studies.
Extracted dataincluded sample size, number of study
groups tested, study type (e.g., training study), sex, age,
body mass, resistance training status and years, exercise,
equipment type, 1RM, relative load tested (% 1RM), test
pace method (e.g., metronome, self-paced, maximal veloc-
ity), repetition duration for the eccentric and concentric
phases, and the number of repetitions completed. For age,
body mass, resistance training years, 1RM, and number of
repetitions completed, the means and standard deviations
(SDs) were extracted. The minimums and maximums were
also extracted for the number of repetitions completed. Vari-
ances reported as standard errors were converted to SDs. For
papers in which data were presented in figures, the data were
extracted using a graph digitzer (WebPlotDigitizer, https://
autom eris. io). Finally, some researchers did not report age
or body mass for all study groups, but instead reported
suchdata for the entire study sample. In such instances, if
the various study groups were all from the same general
demographic (i.e., sex and age group), then the values rep-
resenting the entire study sample were used to represent each
study group.
Fig. 1 Flow diagram of search
strategy. RM repetition maxi-
mum
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306 J.L.Nuzzo et al.
2.3 Statistical Analyses
All extracted data and the analysis code utilized to ana-
lyze the data are available at the Open Science Framework
(https:// osf. io/ s94gf/). Given the aim of this research was
descriptive, we opted to take a model-based [23] and esti-
mation-based approach [24]. For all analyses, effect esti-
mates and their precision, along with conclusions based
upon them, were interpreted continuously and probabilisti-
cally, considering data quality, and all within the context
of each outcome [25]. Effect size calculation and main
modeling was performed using the ‘metafor’ package
[26], ‘emmeans’ [27] used for moderator contrasts, and
‘performance’ [28] and ‘bayestestR’ [29] used for model
comparison. All analyses were performed in R (version
4.2.2; R Core Team, https:// www.r- proje ct. or g/) and RStu-
dio (version 2023.03.0 + 492, Posit Software, https:// posit.
co/). All data visualizations were made using ‘ggplot2’
[30] and ‘patchwork’ [31]. Tables were produced using
‘gt’ [32], ‘gtsummary’ [33], and ‘kableExtra’ [34].
We were interested in modeling the functional form of
the relationship between the relative load (i.e., %1RM,
predictor variable) and the mean number of repetitions
performed and the between-individuals SD in repetitions
performed (response variables). As the included studies
often had multiple groups and reported multiple rep-
etitions to failure tests at different relative loads within
these, the data had a nested structure. Therefore, multi-
level mixed-effects meta-analyses were performed with
random intercepts for study level, group level, and effect
level included in all models. In each model, we allowed for
random linear slopes within study and group levels. Effects
were weighted by inverse sampling variance. Our initial
approach was to examine a selection of different models
and compare their fit and performance.
We began with comparing models for both the raw mean
repetitions as well as the log-transformed mean repetitions
with the predictor taking linear, log-transformed, or quad-
ratic functional forms, and also each model was compared
with either the intercept being estimated or with the pre-
dictor recentered to force the intercept to take on a value
of 1RM at 100% of the 1RM (see visual comparison of
these models here: https:// osf. io/ 83c62). It was immedi-
ately obvious that the raw means would not be suitable as
they permitted the models to predict impossible values (i.e.,
repetitions < 0). However, the mean repetitions followed
a log-normal distribution (see https:// osf. io/ p8ryh), so we
opted to only consider the models of log mean repetitions as
candidates. From visual comparison of the log mean models,
the linear model appeared to fit the data well. However, the
estimated response values at large predictor values of %1RM
appeared larger than expected (e.g., ~ 5 repetitions at 95%
1RM). Yet, the recentered models that forced the estimates
to take on a value of one repetition at 100% 1RM did not
appear to fit the rest of the data well. As such, we examined
a final model employing natural cubic splines with knots at
60% and 80% of 1RM (where most data were available; see
https:// osf. io/ qa5gb) and boundary knots at 0% and 100%
of 1RM, hoping this model would allow for a good fit to the
data available and flexibility to estimate reasonable values
at higher values of %1RM. We then compared fit statistics
for all log mean models (see https:// osf. io/ 4v32n) and also
compared the models using Bayes factors calculated with
approximate Bayesian information criterion (see https:// osf.
io/ 432gn [35]). Fit statistics favored the natural cubic spline
model and Bayes factors indicated that there was strong evi-
dence favoring the natural cubic spline model as being a
more probable description of the data generating process
compared with all other models. Thus, for log mean repeti-
tions we opted to take the natural cubic spline model forward
(diagnostics for this model can be seen here: https:// osf. io/
e6rqf).
We followed a similar process for comparing models
for the variances between people in repetitions performed.
In all models, we used the log-transformed SDs for repeti-
tions again with the predictor taking linear, log-transformed,
quadratic, or the natural cubic spline functional forms as
initially examined for the mean repetitions (see visual com-
parison of these models here: https:// osf. io/ wgmrj). Visu-
ally, the differences between these models were negligible,
which was also confirmed when we compared fit statistics
(see https:// osf. io/ q9brs). Examining the Bayes factors for
model comparisons suggested that both the linear and natu-
ral cubic spline models had higher probabilities than log-
transformed or quadratic; but evidence favoring the natural
cubic spline model over the simpler linear model was only
marginally positive (see https:// osf. io/ d87th). As such, for
the log SD of repetitions, we opted to take the simpler linear
model forward (diagnostics for this model can be seen here:
https:// osf. io/ 9kmzg).
A main model including all effects for both log mean
repetitions and log SD of repetitions was produced for all
groups in each study. From this, we exponentiated the model
estimates back to the raw repetition scale to aid interpretabil-
ity and present meta-analytic scatterplots showing the rela-
tionship of both mean repetitions and the SD of repetitions
with %1RM with both 95% confidence intervals (CIs) and
95% prediction intervals. We also tabulated the estimated
values and CIs for levels of %1RM that range from 15 to
95% (i.e., the range of the data).
As a secondary aim, we conducted exploratory interac-
tion models for both log mean and log SD of repetitions
to explore the moderating effects of sex, age, training sta-
tus, and exercise performed. We also intended to explore
a potential moderating effect for the muscle action type
performed in testing (e.g., eccentric-only repetition,
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307
Repetitions~%1RM Relationship
traditional eccentric-concentric repetition), but this was
not possible given that only a small number of studies
examined eccentric-only repetitions. For sex, we limited
this to studies where groups were reported as male or
female only (i.e., excluded mixed samples). We examined
the mean age of the samples as a continuous predictor,
but for ease of interpretation we present predicted val-
ues from this interaction model for 30, 50, and 70years
of age. For training status, we limited this to comparing
those with and without prior resistance training experi-
ence as there were limited data for other populations (e.g.,
endurance trained) and for specific durations of prior
resistance training experience. Last, we limited our exam-
ination of exercises to the bench press, chest press, squat,
and leg press given that for these exercises we had more
data available over a wider range of %1RM values, allow-
ing comparison between upper- and lower-body exercise
and between exercises involving similar muscle groups
but different equipment (i.e., machines vs free weights).
Results from the barbell squat were combined with results
from the Smith machine squat, and results from the bar-
bell bench press were combined with results from the
Smith machine bench press. These data were combined
because many papers did not include information on the
equipment used, and of those papers that included such
information, insufficient data were available to warrant
exploration of separate REPS ~ %1RM relationships for
Smith machine and barbell exercises. In each moderator
interaction comparison, we calculated pairwise contrasts
using ratios with 95% CIs given the use of log means and
log SDs.
Giventhe potential practical utility of the REPS ~ %1RM
relationship, the statistical terminology used herein also
warrants brief explanation to facilitate interpretation of
the results. The number of repetitions performed at a given
%1RM could be described by two parameters: a mean and an
SD. The mean refers to the central tendency for repetitions
performed by individuals, and the SD refers to the disper-
sion of repetitions performed. The point estimate for a given
parameter refers to the best estimate of the parameter value
in the population from which the sample was drawn, given
the assumptions of the statistical model employed as an esti-
mator and the sample data (in this case, the summary data
from studies included in the meta-analysis described). Thus,
when referring to the point estimate for either the mean rep-
etitions or SDs in repetitions, we are referring to our best
estimate of each of these parameters. However, we also
present the uncertainty in our estimates for each of these,
both mean and SD, by providing CIs from our estimator for
each parameter. These are interpreted as being wide enough
that a certain percentage of the time (95% in the present
case), if we took samples (individual studies in this case)
and employed a particular statistical model (meta-analysis
in this case), we would expect them to include the true value
of the parameter, given that the assumptions of the statistical
model are met.
3 Results
A total of 269 eligible studies were identified [1, 4, 6, 17,
36–84] [85–123] [124–216] [162, 217–267] [268–300].
These studies included 452 groups that contributed data
from 952 repetitions-to-failure tests completed by 7289 indi-
viduals. The earliest study was published in 1961 and the lat-
est in 2023. The descriptive characteristics of the groups in
the identified studies are reported in the Electronic Supple-
mentary Material (ESM) [see https:// osf. io/ r2xs7]. Results
from 77 studies were extracted using WebPlotDigitizer.
The main descriptive results indicated that the samples (k)
were predominantly male (k = 292; 66%), healthy (k = 433;
97%), < 59years of age (k = 410; 92%; median of the mean
age for samples 23years), and resistance trained (k = 247;
60%). Barbells (k = 172; 47%), weight stack and plate-loaded
machines (k = 145; 39%), and Smith machines (k = 33; 9%)
were the most commonly used devices for testing. The most
common exercises tested were the bench press (k = 189;
42%), leg press (k = 65; 14%), squat (k = 52; 12%), knee
extension (k = 48; 11%), and chest press (k = 42; 9%). Test-
ing was predominantly bilateral (k = 394; 89%) with repeti-
tion duration1 controlled using a metronome (k = 94; 68%) in
those studies reporting it (though the majority did not report
this; k = 311). Most studies involved tests using traditional
concentric-eccentric repetitions (k = 439; 98%).
Not all identified repetitions-to-failure tests were included
in the meta-analyses because effect sizes could not be calcu-
lated when variances were not reported. Further, we opted
to only examine tests that had performed traditional concen-
tric-eccentric repetitions as there was limited data for either
concentric-only (1.1%) or eccentric-only tests (1.3%). It was
possible therefore to include the results from 425 groups and
898 tests from 6970 individuals in our analyses. The median
sample size for any included group was 13 participants with
a range from 3 to 112 participants.
1 As repetition duration might impact the REPS ~ %1RM relation-
ship, we included an exploratory analysis of this in studies where
the repetition duration was reported. The range for reported total
repetitions durations (i.e., both concentric and eccentric phases) was
1.4–6.0s coming from only 122 of the included studies (46%). Whilst
there was a tendency for fewer repetitions to be performed when
using longer repetition durations, almost all interval estimates on con-
trast ratios included 1 and thus it is uncertain what the exact extent of
moderating effects for this variable is over this range upon mean rep-
etitions or SDs of repetitions (see ESM for figure https:// osf. io/ e9y7h,
estimates table https:// osf. io/ yjrwz, and contrasts table https:// osf. io/
yje3k).
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
308 J.L.Nuzzo et al.
3.1 Main Models
Both of the main models exploring the relationships between
%1RM and both mean repetitions and SD of repetitions indi-
cated a negative trend in estimates with increasing %1RM.
The mean number of repetitions decreases with increasing
%1RM, as does the between-individuals SD in repetitions
performed. Figure2 presents the meta-analytic scatter plots
for both the mean repetitions (natural cubic spline model of
log means) and the SDs of repetitions (linear model of log
SDs) with 95% CIs and 95% prediction intervals, alongside
an updated REPS ~ %1RM table that ranged from 15 to 95%
of 1RM in 5% intervals. The precision of estimates for both
means and SDs are tight up to 65% 1RM range to ~ 1 repeti-
tion. Estimates from the models are less precise for lower
%1RM values due to limiteddata at these loads.
3.2 Moderators
The impact of most of the moderators was uncertain based
on the precision of estimates for the contrasts. Whilst there
were slight differences when comparing moderators such
as sex (sex plot https:// osf. io/ xcesk, sex table https:// osf. io/
zmd8f), age (age plot https:// osf. io/ 3tfxd, age table https://
osf. io/ mt7cs), training status (training status plot https://
osf. io/ kupbq, training status table https:// osf. io/ 7964a), and
exercise (exercise plot https:// osf. io/ kx6gp, exercise table
https:// osf. io/ bxjh9) in point estimates for both mean and
SD of repetitions, almost all interval estimates on contrast
ratios included 1. Thus, it is uncertain if there are moderat-
ing effects for these variables in mean repetitions or SDs of
repetitions (see contrast ratio tables for sex https:// osf. io/
jub3h, age https:// osf. io/ gavmc, training status https:// osf. io/
9f5ke, and exercise https:// osf. io/ kfbuh). The only exception
was for contrasts between the bench press (Fig.3)and leg
press exercise(Fig.4), where up to ~ 50% 1RM, fewer mean
repetitions were possible in the bench press, and up to ~ 35%
1RM, there was also lower between-individuals SDs in num-
ber of repetitions possible for the bench press.
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0102030405060708090 100
Load (%1RM)
Mean Repetitions (n)
Natural Cubic Spline Model (Log Means)
0
10
20
30
40
50
60
70
80
90
100
0102030405060708090 100
Load (%1RM)
Standard Deviation of Repetitions (n)
Linear Model (Log SDs)
Fig. 2 Meta-analytic scatterplots from main models for both the natu-
ral cubic spline model used to model log mean repetitions (top left
panel) and the linear model used to model standard deviation (SD) of
repetitions (bottom left panel). Estimates from both models have been
exponentiated back to the raw repetitions scale. For the mean repeti-
tions plot, the dashed horizontal reference line is at one repetition.
For the SD of the repetitions plot, the dashed horizontal reference line
is at zero. The grey band shows the 95% confidence interval (CI) and
the dashed lines show the 95% prediction interval. A table showing
the exact point estimates and 95% CIs for both mean repetitions, and
SDs of repetitions, is presented that ranges from 15 to 95% 1 repeti-
tion maximum (RM) at 5% 1RM intervals (right panel)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
309
Repetitions~%1RM Relationship
4 Discussion
The purposes of this study were to use meta-regression to
estimate the number of repetitions that individuals can be
expected to complete at various percentages of the 1RM
and to explore if the REPS ~ %1RM relationship is moder-
ated by sex, age, training status, and exercise. From data
collected on approximately 7000 individuals, we generated
an updated main model table of the REPS ~ %1RM rela-
tionship (Fig.2). Because sex, age, and training status did
not clearly moderate the REPS ~ %1RM, the main model
table can be used when prescribing resistance exercise to
all individuals and for most exercises. However, differences
in the REPS ~ %1RM relationship were observed for the leg
press and bench press and thus separate tables were created
for these two exercises. We were unable to explore mus-
cle action type as a moderator owing to the lack of data
available for repetitions-to-failure tests with eccentric-only
muscle actions.
Our results update the REPS ~ %1RM table that has been
presented in a commonly assigned strength and conditioning
textbook for many years (Table1) [7]. Table1 provides only
point estimates for the number of repetitions that an indi-
vidual might be expected to complete at various percentages
of the 1RM. Our updated table provides both mean repeti-
tion estimates, and estimates for between-individuals varia-
tion, and incorporates the uncertainty of these estimates by
reporting their corresponding 95% CIs (Fig.2).
As expected, we found that estimates for the mean num-
ber of repetitions decreased with increasing %1RM. Com-
pared with Table1, estimates in Fig.2 are most different at
lighter loads, whereas estimates at higher loads are more
similar between Table1 and Fig.2. For example, in Table1,
estimates at 90% and 70% 1RM are 4 and 11 repetitions,
respectively. In Fig.2, estimates at 90% and 70% 1RM
are ~ 5 and ~ 15 repetitions, respectively. For the bench press,
estimates in Fig.2 are generally similar with Table1. For
example, at 90, 80, and 70% 1RM, the estimates in Table1
are 4, 8, and 11 repetitions, respectively. In Fig.2, the esti-
mates at these same relative loads are ~ 4, ~ 9, and ~ 14 repeti-
tions, respectively. However, estimates for the leg press are
notably higher in Fig.2 than Table1. At 90, 80, and 70%
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0102030405060708090 100
Load (%1RM)
Mean Repetitions (n)
Natural Cubic Spline Model (Log Means)
0
10
20
30
40
50
60
70
80
90
100
0102030405060708090 100
Load (%1RM)
Standard Deviation of Repetitions (n)
Linear Model (Log SDs)
Bench Press
Fig. 3 Meta-analytic scatterplots for the bench press for both the nat-
ural cubic spline model used to model log mean repetitions (top left
panel) and the linear model used to model standard deviation (SD)
of repetitions (bottom left panel). Estimates from both models have
been exponentiated back to the raw repetitions scale. For the mean
repetitions plot, the dashed horizontal reference line is at one repeti-
tion. For the SD of repetitions plot, the dashed horizontal reference
line is at zero. The grey band shows the 95% confidence interval (CI).
A table showing the exact point estimates and 95% CIs for both mean
repetitions, and SD of repetitions, is presented that ranges from 15 to
95% 1 repetition maximum (RM) at 5% 1RM intervals (right panel)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
310 J.L.Nuzzo et al.
1RM, point estimates in Fig.2 are ~ 9, ~ 13, and ~ 19 repeti-
tions, respectively.
In addition to the estimates for mean repetitions, Fig.2
provides estimates for SDs for repetitions between indi-
viduals. This advances Table1, which does not account
for between-individual variability in test performance. The
estimates for SDs also increases as %1RM decreases. For
example, at 80% 1RM, the estimate for the SD about the
point estimate is 2.51 repetitions, whereas at 60% 1RM the
estimate is 4.36 repetitions. These results reveal greater
between-individual heterogeneity in repetitions completed
at lighter than heavier relative loads. Why between-indi-
vidual heterogeneity in repetitions completed is greater at
lighter loads is not entirely clear. This result may reflect
the commonly observed mean–variance relationship (i.e., as
means increase so do their corresponding SDs) that has been
reported for other exercise outcomes such as muscle strength
[301]. Our exploratory meta-regression model confirmed the
presence of such a mean–variance relationship (see https://
osf. io/ sknyr). This variance could also be influenced by het-
eroskedasticity in measurement error whereby it also scales
with measured repetitions. Thus, although large SDs could
be due to between-individual heterogeneity in repetitions
completed, a mathematical phenomenon, or heteroskedas-
tic measurement errors, this information is still practically
useful because it illustrates the amount of variance that can
be expected.
We thought that sex and age might moderate the
REPS ~ %1RM relationship because of evidence suggesting
that sex [8–11] and age [12–14] impact muscle fatigabil-
ity. However, the REPS ~ %1RM relationship was largely
similar between men and women, and the relationship was
also similar between younger and older adults, potentially
questioning the magnitude of the impact of these factors on
fatigability. Consequently, we did not generate sex- or age-
specific REPS ~ %1RM tables.
We also examined exercise as a potential moderator of the
REPS ~ %1RM relationship. We observed a difference in the
REPS ~ %1RM relationship between the leg press and bench
press, with greater mean repetitions completed in the leg
press than bench press across the spectrum of relative loads.
For example, at 80% and 70% 1RM, the estimated number of
repetitions in the leg press were 13.1 [95% CI 9.8–17.5] and
19.0 [95% CI 14.2–25.5], respectively, whereas for the bench
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
0102030405060708090 100
Load (%1RM)
Mean Repetitions (n)
Natural Cubic Spline Model (Log Means)
0
10
20
30
40
50
60
70
80
90
100
0102030405060708090 100
Load (%1RM)
Standard Deviation of Repetitions (n)
Linear Model (Log SDs)
Leg Press
Fig. 4 Meta-analytic scatterplots for the leg press for both the natu-
ral cubic spline model used to model log mean repetitions (top left
panel) and the linear model used to model standard deviation (SD)
of repetitions (bottom left panel). Estimates for both models have
been exponentiated back to the raw repetitions scale. For the mean
repetitions plot, the dashed horizontal reference line is at one repeti-
tion. For the SD of repetitions plot, the dashed horizontal reference
line is at zero. The grey band shows the 95% confidence interval (CI).
A table showing the exact point estimates and 95% CIs for both mean
repetitions, and SD of repetitions, is presented that ranges from 15 to
95% 1 repetition maximum (RM) at 5% 1RM intervals (right panel)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
311
Repetitions~%1RM Relationship
press, the estimated number of repetitions were 8.8 [95% CI
7.7–10.1] and 14.1 [95% CI 12.4–16.1], respectively. Con-
sequently, we generated separate REPS ~ %1RM tables for
the bench press (Fig.3) and leg press (Fig.4). For all other
exercises, the main model table is most applicable (Fig.2).
We also intended to explore if the REPS-%1RM relation-
ship differs between concentric and eccentric muscle actions.
However,only 1% of all data were from eccentric-only test-
ing. Consequently, we could not determine whether differ-
ent REPS ~ %1RM tables should exist for eccentric-only and
traditional repetitions. Results from a small number of stud-
ies suggest that at equal relative loads, more eccentric-only
than concentric-only repetitions can be completed at certain
relative loads for some exercises [17, 169, 302]. If these
results are replicated in future research, a REPS ~ %1RM
table specific to eccentric muscle actions will beneeded,
particularly as eccentric resistance exercise is growing in
popularity and new technologies are making its prescription
more feasible [11, 18, 19].
4.1 Limitations andFuture Research
The current study is not without limitations. Our search
strategy did not follow standard guidelines for meta-analy-
ses. The disadvantage of this strategy is that it makes future
attempts to replicate the strategy challenging or impossible.
Nevertheless, our search identified 269 studies, which is
substantially more studies than the current REPS ~ %1RM
table is based on (Table1) [7]. Moreover, all references,
analyses, and results from the current study have been made
publicly available. Researchers are welcome to use the
publicly available information to further explore the data
or build from it. A second limitation of the current study
is that the amount of data available did not permit formal
analyses that might be of interest to some exercise practition-
ers, for example, whether the REPS ~ %1RM relationship
differs between different types ofathletes [92, 202, 237].
Some results suggest endurance athletes can perform more
repetitions at loads ≤ 75% 1RM than can strength-power
athletes [92, 202, 237]. Moving forward, the solution to
such limitations is tocollectmore data. More data is needed
to provide more precise point estimates of the number of
repetitions that individuals can be expected to complete
across the relative loading spectrum. Most data from the
REPS ~ %1RM relationship have been collected on healthy
individuals who are aged 20–40years. Thus, future research
can examine the REPS ~ %1RM relationship in older adults,
patient groups, and specific athlete groups. Future research
can also explore the REPS ~ %1RM relationship for exer-
cises that are commonly prescribed but for which minimal
data are available(e.g., overhead press, lateral pulldown,
seated row, triceps extension, knee flexion, calf raise). Last,
only a relatively narrow range of repetition durations were
reported with the magnitude of their impact being relatively
small and uncertain. As some resistance training protocols
employ long repetition durations and low repetition numbers
(e.g., 6 repetitions at 10-s concentric and 10-s eccentric)
[303], the REPS ~ %1RM relationship mightdiffer at more
extreme repetition durations, and thus further research can
explore this topic.
5 Conclusions
The REPS ~ %1RM relationship is foundational knowledge
in resistance exercise programming. It gives practition-
ers a sense of the relative loads that can be prescribed to
allow for a certain number of repetitions to be completed.
Though a general table of the REPS ~ %1RM relationship
has been available for many years (Table1), it has not incor-
porated uncertainty into point estimates or accounted for
between-individuals variation in performance. We updated
this table. After using meta-regression to analyze all avail-
able literature on repetitions-to-failure tests, we generated a
main model table of estimates for mean repetitions and SDs
and 95% CIs around the point estimates across the relative
loading spectrum (Fig.2). This table can be used to guide
resistance exercise prescriptions for all individuals and for
most exercises. However, because significantly more repeti-
tions can be completed in the leg press than the bench press,
separate tables should be referenced when prescribing resist-
ance exercise for these two exercises (Figs.3 and 4). Future
research involving hundreds, if not thousands, of participants
will be necessary to establish precise REPS ~ %1RM rela-
tionships for other exercises and specific populations.
Declarations
Funding Open Access funding enabled and organized by CAUL and
its Member Institutions. Matheus D. Pinto received a PhD scholarship
from the Australian Government Research Training Program.
Conflicts of Interest James L. Nuzzo and Matheus D. Pinto were pre-
viously employed at Vitruvian, a company that designs and sells re-
sistance exercise equipment. Kazunori Nosaka and James Steele have
no conflicts of interest that are directly relevant to the content of this
article.
Ethics Approval Not applicable.
Consent to Participate Not applicable.
Consent for Publication Not applicable.
Availability of Data and Material The extracted data, and the code used
to analyze that data, are available as supplementary materials at the
Open Science Framework (https:// osf. io/ s94gf/).
Code Availability Not applicable.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
312 J.L.Nuzzo et al.
Authors’ Contributions JLN conceived of the idea for the manuscript,
conducted the literature search, extracted the data, and wrote the first
draft of the manuscript. JS conducted the statistical analysis and created
the tables and figures. MDP, KN, and JS provided conceptual feedback
throughout the research process. All authors read and revised multiple
drafts of the original manuscript. All authors approved of the final
version of the manuscript.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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321
Repetitions~%1RM Relationship
Authors and Aliations
JamesL.Nuzzo1 · MatheusD.Pinto1 · KazunoriNosaka1 · JamesSteele2
* James L. Nuzzo
j.nuzzo@ecu.edu.au
1 Centre forHuman Performance, School ofMedical
andHealth Sciences, Edith Cowan University, 270
Joondalup Drive, Joondalup, WA6027, Australia
2 School ofSport, Health, andSocial Sciences, Solent
University, Southampton, UK
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