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Submitted 20 September 2017
Accepted 7 November 2017
Published 30 November 2017
Corresponding author
James Steele,
james.steele@solent.ac.uk
Academic editor
Justin Keogh
Additional Information and
Declarations can be found on
page 9
DOI 10.7717/peerj.4105
Copyright
2017 Steele et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Ability to predict repetitions to
momentary failure is not perfectly
accurate, though improves with
resistance training experience
James Steele1, Andreas Endres2, James Fisher1, Paulo Gentil3and
Jürgen Giessing2
1School of Sport, Health, and Social Sciences, Southampton Solent University, Southampton,
United Kingdom
2Institute of Sport Science, University of Koblenz-Landau, Landau, Germany
3Faculty of Physical Education and Dance, Federal University of Goias, Goiânia, Brazil
ABSTRACT
‘Repetitions in Reserve’ (RIR) scales in resistance training (RT) are used to control effort
but assume people accurately predict performance a priori (i.e. the number of possible
repetitions to momentary failure (MF)). This study examined the ability of trainees
with different experience levels to predict number of repetitions to MF. One hundred
and forty-one participants underwent a full body RT session involving single sets to
MF and were asked to predict the number of repetitions they could complete before
reaching MF on each exercise. Participants underpredicted the number of repetitions
they could perform to MF (Standard error of measurements [95% confidence intervals]
for combined sample ranged between 2.64 [2.36–2.99] and 3.38 [3.02–3.83]). There
was a tendency towards improved accuracy with greater experience. Ability to predict
repetitions to MF is not perfectly accurate among most trainees though may improve
with experience. Thus, RIR should be used cautiously in prescription of RT. Trainers
and trainees should be aware of this as it may have implications for the attainment of
training goals, particularly muscular hypertrophy.
Subjects Anatomy and Physiology, Kinesiology
Keywords Fitness, Hypertrophy, Strength, Program evaluation, Health
INTRODUCTION
Resistance training (RT) is an exercise mode evidenced to provide a wide range of health
benefits (Steele et al., 2017c). As such, understanding prescription of RT to maximise these
outcomes is of considerable interest. One variable which may be of primary importance
is the intensity of effort employed i.e., whether or not RT is performed to momentary
failure (MF; Steele, 2014;Steele et al., 2017a). A plethora of recent work shows that when
effort is matched by having RT performed to MF, manipulations of other RT variables
have a lesser impact upon the magnitude of outcomes. These secondary variables include;
load (Morton et al., 2016;Schoenfeld et al., 2015;Schoenfeld et al., 2016;Fisher, Ironside &
Steele, 2016), repetition duration (Schoenfeld, Ogborn & Krieger, 2015), and the use of
advanced or complicated training methods such as pre-exhaustion (Fisher et al., 2014),
How to cite this article Steele et al. (2017), Ability to predict repetitions to momentary failure is not perfectly accurate, though improves
with resistance training experience. PeerJ 5:e4105; DOI 10.7717/peerj.4105
breakdown sets (Fisher, Carlson & Steele, 2015) or occlusion training (Barcelos et al., 2015;
Farup et al., 2015).
A number of recent reviews have also concluded that training to MF may confer greater
adaptations in strength (Fisher et al., 2011), hypertrophy (Fisher, Steele & Smith, 2013) and
possibly cardiorespiratory fitness (Steele et al., 2012) than training not to MF. Conversely,
more recent empirical work has shown contrasting results regarding the efficacy of
training to MF (Fisher, Blossom & Steele, 2016;Giessing et al., 2016a;Giessing et al., 2016b;
Izquierdo-Gabarren et al., 2010;Sampson & Groeller, 2016). Proximity to MF has been
considered a determinant of the effort employed during RT, and MF (as a set end-point)
has been suggested as the only way to objectively match inter- and intra-individual effort
due to the variations in number of repetitions possible prior to MF at the same relative
loads i.e., % one repetition maximum (%1RM; Steele, 2014;Steele et al., 2017a). In fact, it
has been argued that training to MF is the most appropriate way to control the application
of a RT stimulus (Dankel et al., 2016).
However, the current body of research has typically considered this variable
dichotomously (i.e., people training ‘to MF’ or ‘not to MF’). As a result, the dose–response
nature of sub maximal intensities of effort resulting from set end-points occurring at
different proximities to MF is unclear. Furthermore, it is unclear whether there is a
threshold of relative effort which optimises adaptations. As such, to understand sub
maximal effort some have developed scales to assess effort during RT relative to MF (Hackett
et al., 2012;Hackett et al., 2016;Zourdos et al., 2016;Helms et al., 2016). These ‘Repetitions
in Reserve’ (RIR) scales are designed as a way of assessing/controlling relative effort by
participants estimating how many repetitions they can perform before reaching MF.
In comparison with traditional rating of perceived effort (RPE) scales, RIR scales appear
more likely to offer valid representations of effort when training to, or close to, MF (Helms
et al., 2016) whereas traditional RPE often yields far less accurate ratings under such
conditions (Hackett et al., 2012). Indeed, even when training to MF, traditional RPE is
often less than maximal (Steele et al., 2017b). This in combination with the considerable
inter- and intra-individual variations in number of repetitions possible prior to MF at the
same relative loads suggests that RIR scales may offer an improvement in control of effort
during RT compared with either use of %1RM or traditional RPE.
However, the use of these scales assumes that trainees are able to accurately predict the
number of repetitions they could perform to MF with a particular load. Studies which
have shown differences between groups training to MF compared with those not training
to MF (where the participants were instructed to stop at the point they predicted MF on
the next repetition), might be explained by the participants’ inability to accurately predict
MF (i.e., they actually stopped >1 repetitions away from MF; Giessing et al., 2016a;Giessing
et al., 2016b). A study by Hackett et al. (2012) supports this, and revealed that trained
participants were not perfectly accurate at predicting the number of repetitions they could
perform to MF using a RIR scale, although their accuracy improved with subsequent sets.
This would question the value of using ‘intuitive’ approaches to RT such as the RIR scales.
Ability to predict proximity to MF may improve with training experience and thus it has
been noted that the use of RIR scales may present greater value in experienced trainees
Steele et al. (2017), PeerJ , DOI 10.7717/peerj.4105 2/14
(Helms et al., 2016). However, a follow-up study from Hackett et al. (2016) study suggested
that previous RT experience did not affect ability to predict proximity to MF.
In light of the potential value of training to MF, as well as the introduction of recent
RIR scales to control RT effort, it is of interest to examine trainees with differing levels of
RT experience in their ability to predict the number of repetitions they can perform to MF.
Further, examination of this in controlled yet ecologically valid conditions such as their
usual gym environment offers considerable practical information. As such the aim of this
study was to compare predicted with actual repetitions to MF in participants with a range
of RT experience.
METHODS
Experimental approach to the problem
Participants in this study underwent a single RT trial in order to examine whether they
were able to accurately predict the number of repetitions they could perform when training
to MF. All participants, grouped according to their RT experience, were asked to provide
a prediction of repetitions to MF and then undergo a test of actual repetitions to MF for
comparison.
Participants
One hundred and forty-one participants (males n=72, age 29 ±10 years; females n=69,
age 25 ±8 years) were recruited from the existing membership pool of a private exercise
facility in Germany. Participants were required to have no medical condition for which
RT would be contraindicated, and were grouped based upon duration of previous RT
experience; <1.5 months (orientation, n=15), 1.5 to six months (beginner, n=21), six to
12 months (experienced, n=21), 12 to 36 months (advanced, n=42), and >36 months
(expert, n=42). Written informed consent was provided by all participants and the study
was ethically approved by the author’s institution.
Procedures
Participants underwent a single RT session involving the following exercises: seated
row, chest press, leg press, elbow flexion, and pulldown, all using selectorised resistance
machines, and sit-ups using additional free weight loading. All participants were required to
have been performing these exercises in their pre-existing training programs and to have the
current training load they were using recorded in their training logs. Participants performed
a single set of each exercise to concentric MF according to recent definitions of this concept
i.e., the set ending when the trainee reached the point where, despite attempting to do so,
they could not complete the concentric portion of their current repetition without deviation
from the prescribed form of the exercise (Steele et al., 2017a). Participants were informed
to use the repetition duration they normally used during training for each exercise, to
retain familiarity. Exercises were performed in the order that the participants typically
performed them in their current training based upon their recorded training logs and
participants were permitted to rest between each exercise for as long they typically would
or felt necessary to ensure maximal performance on the subsequent exercise. This was
Steele et al. (2017), PeerJ , DOI 10.7717/peerj.4105 3/14
also to ensure that participant’s predictions were based upon the RT conditions that they
had previously experienced. All exercises were supervised by one of the investigators who
observed the participants whilst they performed the exercise without verbal encouragement
so as to ensure consistency across participants. The investigator counted repetitions in their
head and then noted these without the participant’s knowledge. Prior to beginning each
exercise participants were asked to consider the current load they were training with and to
provide a prediction of the number of repetitions they could complete before reaching MF.
Participants were informed that this was defined as the number of repetitions performed
with the current load whilst continuing to the point where, despite the greatest effort and
attempting to do so, they could not complete the current repetition (i.e., what repetition
number they thought they would reach MF on). Participants were also asked to report
their current training goals.
Statistical analysis
Agreement between predicted and actual repetitions to MF was examined using standard
error of measurement (SEM) and 95% confidence intervals (CI) in order to provide
an absolute indication of the agreement between the variables. This was performed for
each exercise. Calculations were performed using Microsoft Office Excel 2013 (Microsoft
Corporation, Redmond, WA, USA) and spreadsheets for analysis of validity by Hopkins
(2015) were used. Actual repetitions were considered the ‘criterion’ and predicted
repetitions were considered the ‘practical’.
RESULTS
Descriptive statistics suggested that on average participants underpredicted the number of
repetitions they could perform to MF (Table 1). For the combined sample SEMs (95%CIs)
were 2.91 (2.61–3.30) for the chest press, 2.64 (2.36–2.99) for the elbow flexion, 3.38
(3.02–3.83) for the leg press, 2.95 (2.64–3.35) for the pulldown, 2.71 (2.43–3.08) for
the seated row, and 3.36 (3.00–3.80) for the sit-up. SEMs and 95%CIs are reported in
Table 2 for each exercise and group. There was a tendency towards improved accuracy in
predicting actual repetitions to MF with greater experience across most exercises evidenced
by reduced SEMs and narrower ranges between upper and lower 95%CIs. The training
goal of muscular hypertrophy was reported with the highest frequency in the combined
participant sample and all groups. Table 3 shows the training goals for each group by
frequency.
DISCUSSION
The current study examined the ability of participants to predict the number of repetitions
they could perform to MF, with a given load, across a number of exercises and range of
levels of experience. It was anticipated that participants would not be perfectly accurate
in predicting actual repetitions to MF, in spite of performing exercises and using loads
with which they were familiar. It was also hypothesised that there would be increased
accuracy with greater RT experience. Descriptive data suggested participants on average
Steele et al. (2017), PeerJ , DOI 10.7717/peerj.4105 4/14
Table 1 Descriptive data (mean ±SD) for each exercise and group.
Combined Orientation Beginner Experienced Advanced Expert
Chest press
Predicted 12.38 ±3.45 15.40 ±2.77 14.86 ±3.00 14.00 ±3.46 11.14 ±2.55 10.48 ±2.86
Actual 14.21 ±5.52 20.47 ±6.36 18.76 ±6.78 15.86 ±4.16 12.17 ±3.39 10.93 ±3.17
Elbow flexion
Predicted 11.53 ±2.99 15.40 ±2.77 13.38 ±2.85 12.67 ±3.07 10.60 ±2.06 9.60 ±1.58
Actual 12.48 ±4.32 18.20 ±4.95 16.57 ±4.65 13.14 ±2.67 10.74 ±2.36 9.81 ±2.11
Leg press
Predicted 12.50 ±3.41 15.40 ±2.77 14.95 ±3.14 13.67 ±3.65 11.45 ±2.62 10.71 ±2.79
Actual 16.40 ±6.29 23.87 ±7.04 22.19 ±5.75 17.62 ±4.50 14.52 ±4.13 12.10 ±3.79
Pulldown
Predicted 12.48 ±3.47 15.40 ±2.777 15.27 ±3.13 13.29 ±3.36 11.55 ±2.93 10.57 ±2.77
Actual 13.81 ±5.19 20.27 ±6.92 17.86 ±5.76 14.57 ±4.04 11.95 ±2.81 10.95 ±2.83
Seated row
Predicted 12.38 ±3.45 15.40 ±2.77 14.86 ±3.00 14.00 ±3.46 11.14 ±2.55 10.48 ±2.86
Actual 14.09 ±5.29 19.67 ±5.38 18.33 ±5.80 15.81 ±4.24 12.26 ±3.60 10.93 ±3.48
Sit-up
Predicted 14.62 ±3.05 15.93 ±2.40 16.43 ±2.62 15.71 ±2.99 13.88 ±2.92 13.43 ±3.16
Actual 16.99 ±3.65 18.73 ±5.19 17.71 ±3.99 17.76 ±2.02 17.31 ±3.23 15.29 ±3.39
Table 2 SEMs and 95%CIs for each exercise and group.
Orientation Beginner Experienced Advanced Expert
Chest press 4.33 (3.14–6.97) 4.00 (3.04–5.84) 2.58 (1.96–3.76) 1.91 (1.57–2.45) 1.57 (1.29–2.00)
Elbow flexion 4.30 (3.12–6.93) 6.39 (4.86–9.33) 1.51 (1.15–2.20) 1.95 (1.60–2.50) 1.27 (1.04–1.62)
Leg press 4.98 (3.61–8.03) 3.37 (2.56–4.92) 3.07 (2.34–4.49) 2.49 (2.05–3.19) 1.73 (1.42–2.21)
Pulldown 4.15 (3.01–6.68) 3.89 (2.96–5.68) 2.49 (1.89–3.64) 1.83 (1.50–2.34) 1.35 (1.11–1.73)
Seated row 4.51 (3.27–7.27) 3.50 (2.66–5.11) 2.28 (1.73–3.33) 2.06 (1.69–2.63) 1.71 (1.40–2.19)
Sit-up 5.13 (3.72–8.26) 4.08 (3.10–5.96) 2.06 (1.57–3.01) 2.87 (2.36–3.68) 2.29 (1.88–2.94)
underpredicted the number of repetitions they could perform to MF, though the average
difference was reduced with greater experience. The SEMs indicated that participants
indeed were not perfectly accurate at predicting repetitions to MF with SEMs for the
combined sample ranging from 2.64 to 3.38 repetitions. In contrast with the descriptive
data, SEMs suggested this was the case even for groups with greater experience, although
there did still appear to be an improvement in accuracy with greater experience across most
exercises. Considering the predominant training goal reported by the participants in this
study (muscular hypertrophy), a less than perfectly accurate ability to predict repetitions
to MF may have implications for achieving this goal.
Training to MF involves giving a maximal effort and is also anecdotally associated with
higher discomfort. The less than perfectly accurate predictive ability reported herein may
be a result of participants anchoring their prediction based upon discomfort. As we have
recently noted in several papers (Steele, 2014;Steele et al., 2017b;Steele et al., 2017a), and
Steele et al. (2017), PeerJ , DOI 10.7717/peerj.4105 5/14
Table 3 Participants training goals by frequency.
Combined
(n=141)
Orientation
(n=15)
Beginner
(n=21)
Experienced
(n=21)
Advanced
(n=42)
Expert
(n=42)
Fitness 6 (4.3%) 1 (6.6%) 2 (9.5%) 2 (9.5%) 0 1 (2.4%)
Maintenance 3 (2.1%) 0 1 (4.8%) 0 1 (2.4%) 1 (2.4%)
Muscular definition 8 (5.7%) 0 1 (4.8%) 1 (4.8%) 2 (4.8%) 3 (7.1%)
Muscular hypertrophy 107 (75.9%) 10 (66.6%) 14 (66.6%) 14 (66.6%) 36 (85.7%) 33 (78.6%)
Strength 4 (2.8%) 0 1 (4.8%) 0 1 (2.4%) 2 (4.8%)
Weight loss 13 (9.2%) 4 (26.6%) 2 (9.5%) 4 (19.0%) 1 (2.4%) 2 (4.8%)
as have others, differentiation between perceptions of effort and discomfort are important
(Abbiss et al., 2015;Marcora, 2009;Smirnaul, 2012) particularly within RT (Steele, 2014;
Steele et al., 2017b). In studies using traditional rating of perceived exertion scales higher
ratings are given, despite conditions being controlled by supposedly training to MF, with
lower loads for lower body exercise (Shimano et al., 2006), as set volume increases (Silva
et al., 2014), with increased volume-load (Pritchett et al., 2009), and with increased work
rate (Hiscock et al., 2016;Hiscock, Dawson & Peeling, 2015) supporting that participants
may have expressed their feelings of increasing discomfort (Steele, 2014;Steele et al., 2017b;
Steele et al., 2017a).
In some studies there have been attempts to differentiate between effort and discomfort
during RT. Though participants appear able to report different values for each, there is a
similar pattern for both responses. Hollander et al. (2003) and Hollander et al. (2008) found
that, though effort is typically reported as being higher than discomfort (the authors used
the term pain) under a range of RT conditions (different loads and muscle actions), both
respond in a similar pattern. Such a relationship may be inherent; however, perception
of effort is independent from afferent feedback mechanisms (Marcora, 2009). This would
seem to disagree with observations of higher perceived efforts under conditions known
anecdotally to induce higher feelings of discomfort (e.g., fatiguing low load lower body
exercise). It is possible that participants were either consciously or unconsciously anchoring
their effort and discomfort responses upon one another. When instructed to differentiate
the two, participants are able to do so during RT (Steele et al., 2017b;Fisher, Ironside &
Steele, 2016;Fisher, Farrow & Steele, 2017). But there appears to be a tendency to anchor
one upon the other without such instruction.
Anchoring of perception of effort upon discomfort thus may have implications for
whether a person is truly training to, or close to enough to, MF. Another point to consider
is that participants in this study likely based their prediction upon prior experience of
training whilst unsupervised as most persons train in this manner. Thus, the not perfectly
accurate predictive ability of participants in this study might reflect that under unsupervised
conditions participants are not reaching MF during training despite thinking that they
may be, possibly due to the discomfort associated with such training. As such, persons
training alone may find difficulty in training to MF unless highly self-motivated. Numerous
studies report that strength and body composition changes are poorer when participants
train unsupervised versus training under supervision (Coutts, Murphy & Dascombe, 2004;
Steele et al. (2017), PeerJ , DOI 10.7717/peerj.4105 6/14
Gentil & Bottaro, 2010;Mazzetti et al., 2000). When participants self-select RT load they
often choose to train with lower loads than those recommended (Elsangedy et al., 2013;
Glass & Stanton, 2004) and, considering the typical ranges of repetitions performed to MF
by trainees at these loads (Shimano et al., 2006), are likely not training anywhere close to
MF. Indeed, the RPE reported when participant’s self-select load and repetition range,
in addition to trainer observation, support this (Glass & Stanton, 2004). Instead, under
supervision participants are more likely to train with heavier loads but also to report higher
RPE (Ratamess et al., 2008). In fact it has been suggested that the poorer adaptations as
a result of unsupervised training may be due to participants not training with sufficient
proximity to MF and thus with lower effort (Gentil & Bottaro, 2010).
Evidently there may be implications for whether a person is able to achieve their training
goals if they are unable to accurately perceive whether they are training to true MF or not.
However, as noted there is disagreement within the literature as to whether performing
RT to MF is indeed desirable and further that the consideration of MF in a dichotomous
fashion (i.e., people training ‘to MF’ or ‘not to MF’) renders difficulty in understanding the
nature of sub-maximal efforts during RT (Steele et al., 2017a). As a result, RIR scales have
been developed to be used in controlling sub maximal effort in RT as an improvement
upon the typical %1RM and traditional RPE based approaches (Hackett et al., 2012;Hackett
et al., 2016;Zourdos et al., 2016). The results reported here are in agreement with other
research (Hackett et al., 2012;Hackett et al., 2016) that participants are likely not perfectly
accurate at predicting the number of repetitions they can perform to MF and thus suggest
there may be reason to question the value of RIR scales. The use of RIR scales assumes a
trainee is able to accurately predict the number of repetitions they could perform to MF.
However, if a trainee is not perfectly accurate at making such a prediction then it is likely
that they will be systematically training with a lower than desired effort level which may
impact upon their adaptation to RT.
For untrained persons this may not be of considerable practical concern. In this case,
even when using a lower than intended effort during RT on an individual set, cumulative
fatigue can be induced by increased volume resulting in an increased effort, and thus closer
proximity to MF, in later sets (Fisher, Blossom & Steele, 2016;Giessing et al., 2016b).
However, experience may a play role in a trainee’s ability to predict proximity to MF
and indeed the results reported here support this notion. There was a relationship between
the level of experience of participants and the SEMs and width of 95%CIs found, with
the most experienced group underpredicting by ∼1–2 repetitions compared with the least
experienced underpredicting by ∼4–5 repetitions. Hackett et al. (2012) found experienced
trainees (8 ±3 years RT experience) were initially not perfectly accurate at predicting
repetitions to MF using the RIR scale over the first 1–2 sets (mean difference ranging
0.8–1.9 repetitions). However, on average accuracy improved in later sets. This suggests
that, similar to our findings, even experienced trainees are still not perfectly accurate at
predicting repetitions to MF, yet acute practice/experience appears to improve predictive
ability. Further supporting the effect of experience, Zourdos et al. (2016) found that their
RIR scale reflected more experienced lifters giving a more accurate estimation of their
effort, particularly when using heavier loads, based upon average repetition velocities.
Steele et al. (2017), PeerJ , DOI 10.7717/peerj.4105 7/14
Thus the novice trainees in their study likely overestimated their effort and therefore were
likely underpredicting how many repetitions away from MF they were.
Though increased experience would appear to increase predictive ability experienced
trainees still under estimate by ∼1–2 repetitions. Thus using sub-maximal effort based
RT prescriptions based upon RIR scales will result in most training at a lower than
intended effort. For trained persons this may have a bigger impact upon adaptations.
When attempting to stop a set of repetitions at a set end-point corresponding to a ‘self
determined repetition maximum’ (where the participants were instructed to stop at the
point they predicted MF on the next repetition) strength and hypertrophic outcomes may
be sub-optimal (Giessing et al., 2016a). Thus, the use of ‘intuitive’ approaches that involve
a person’s ability to accurately predict the number of repetitions they could perform
to MF may be questionable as an approach to prescribing and controlling effort in RT.
However, the use of self determined repetition maximum based training compared with
training to MF in experienced participants has only been examined with use of single set
approaches (Giessing et al., 2016b). As accuracy of predictive ability improves with multiple
sets of an exercise (Hackett et al., 2012) then RIR scales may have more utility in multiple
set RT programs. Indeed, similarly to in untrained populations (Fisher, Blossom & Steele,
2016;Giessing et al., 2016b), even if training with a systematically lower than intended
effort, the use of multiple sets, and thus the accumulation of fatigue, combined with
improved predictive ability, makes it likely that in later sets trainees would be closer to
achieving desired intensities of effort. As such, though even ‘expert’ participants in this
study underpredicted by ∼1–2 repetitions, it is likely that this represents an acceptable
degree of error if RIR scale based approaches to training are being utilised in multiple set
routines. However, it seems as though even this degree of error has implications when using
single set routines and as such predictive ability appears unacceptable for this approach.
It is worth considering the strengths and limitations of the present study. Firstly
the present study was able to recruit a large sample size sufficient for examining
validity/agreement between different measures (Hopkins, 2000). Due to this we were
also able to sub group into a range of different experience levels. However, in order to
achieve this large sample, participants were recruited from a private facility and testing
conducted at this facility. This meant participants performed the testing using their current
training equipment and load and, based upon the average repetitions, relative loads
typically increased with experience. As such, the effects of experience level on the SEMs
reported may be confounded as a result of differing ability to predict repetitions to MF
when training using heavier or lighter loads. Greater predictive ability may therefore occur
with heavier loads (Zourdos et al., 2016;Helms et al., 2017). This may be reflective of the
conflation between effort and discomfort described above as greater perceived discomfort
occurs with lower load RT (Fisher, Ironside & Steele, 2016;Fisher, Farrow & Steele, 2017).
Future research should examine the impact that manipulation of other RT variables such
as load, and its interaction with perceived discomfort, has upon ability to accurately predict
repetitions to MF. A final limitation could be that we asked participants to predict the
number of repetitions they could perform to MF prior to the execution of the exercise.
Prior studies have asked participants during the execution of the set (Hackett et al., 2012;
Steele et al. (2017), PeerJ , DOI 10.7717/peerj.4105 8/14
Hackett et al., 2016) and thus participants may be able to make better predictions during
the gestalt experience of actually performing the exercise. However, it should be noted
that in studies where participants have attempted to stop one repetition prior to MF sub
optimal adaptations have still been reported (Giessing et al., 2016a;Giessing et al., 2016b).
Also, we did not control when the participant’s penultimate training session prior to the
testing sessions were, nor did we control and match other factors such as time of day,
diet, sleep, etc. As such, there is still scope for further work to identify what factors may
positively or negatively impact upon a person’s predictive ability in performing repetitions
to MF.
CONCLUSION
Management of effort within RT by manipulation of whether a trainee reaches MF or not is
a common approach by trainees and practitioners. Effort, and thus proximity to MF, may
have implications for the optimisation of adaptations, in particular hypertrophy which for
most commercial gym attendees is the most common training goal. Recently, RIR scales
have been promoted as a means of controlling this and represent an improvement on
%1RM and traditional RPE scales. However, they assume the trainee can make accurate
predictions regarding their ability to perform repetitions to MF. The findings of the
present study reveal that ability to predict repetitions to MF is not perfectly accurate
amongst most trainees. However, there may be some increase in predictive ability with
greater RT experience.
These results have implications regarding training adaptations from RT as most persons
train unsupervised and thus are likely not training to actual MF in their current training
programs. Further, for those not employing MF in their training but instead using sub-
maximal efforts based upon proximity to MF, it is likely that they are systematically training
with a lower than intended effort. These results suggest that RIR scales should be used
with caution in most trainees. It appears that experience may improve a trainee’s ability to
predict repetitions to MF and therefore RIR scales may be more appropriate for experienced
trainees. Lastly, these results apply to single set applications of RT. Prior research suggests
with multiple sets predictive ability increases. As such, RIR scales may have the greatest
utility in experienced trainees using multiple set RT programs.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
The authors received no funding for this work.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
•James Steele analyzed the data, wrote the paper, prepared figures and/or tables, reviewed
drafts of the paper.
Steele et al. (2017), PeerJ , DOI 10.7717/peerj.4105 9/14
•Andreas Endres and Jürgen Giessing conceived and designed the experiments, performed
the experiments, reviewed drafts of the paper.
•James Fisher and Paulo Gentil analyzed the data, reviewed drafts of the paper.
Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
Due to the nature of the study conducted the University of Koblenz-Landau Institutional
Review Board does not require formal submission of ethics as no blood or tissue samples
are being taken.
Data Availability
The following information was supplied regarding data availability:
The raw data has been provided as Data S1.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.4105#supplemental-information.
REFERENCES
Abbiss CR, Peiffer JJ, Meeusen R, Skorski S. 2015. Role of ratings of perceived exer-
tion during self-paced exercise: what are we actually measuring? Sports Medicine
45:1235–1243 DOI 10.1007/s40279-015-0344-5.
Barcelos LC, Nunes PR, De Souza LR, De Oliviera AA, Furlanetto R, Marocolo M,
Orsatti FL. 2015. Low-load resistance training promotes muscular adaptation
regardless of vascular occlusion, load, or volume. European Journal of Applied
Physiology 115:1559–1568 DOI 10.1007/s00421-015-3141-9.
Coutts AJ, Murphy AJ, Dascombe BJ. 2004. Effect of direct supervision of a strength
coach on measures of muscular strength and power in young rugby league players.
Journal of Strength and Conditioning Research 18:316–323.
Dankel SJ, Jessee MB, Mattocks KT, Mouser JG, Counts BR, Buckner SL, Loenneke
JP. 2016. Training to fatigue: the answer for standardization when assessing muscle
hypertrophy? Sports Medicine 47(6):1021–1027 DOI 10.1007/s40279-016-0633-7.
Elsangedy HM, Krause MP, Krinski K, Alves RC, Hsin Nery Chao C, Da Silva
SG. 2013. Is the self-selected resistance exercise intensity by older women con-
sistent with the American College of Sports Medicine guidelines to improve
muscular fitness? Journal of Strength and Conditioning Research 27:1877–1884
DOI 10.1519/JSC.0b013e3182736cfa.
Farup J, De Paoli F, Bjerg K, Riis S, Ringgard S, Vissing K. 2015. Blood flow restricted
and traditional resistance training performed to fatigue produce equal muscle
hypertrophy. Scandinavian Journal of Medicine and Science in Sports 25:754–763
DOI 10.1111/sms.12396.
Steele et al. (2017), PeerJ, DOI 10.7717/peerj.4105 10/14
Fisher JP, Blossom D, Steele J. 2016. A comparison of volume equated knee ex-
tensions to failure, or not to failure, upon rating of perceived exertion and
strength adaptations. Applied, Physiology, Nutrition, and Metabolism 41:168–174
DOI 10.1139/apnm-2015-0421.
Fisher JP, Carlson L, Steele J. 2015. The effects of breakdown set resistance training on
muscular performance and body composition in young males and females. Journal of
Strength and Conditioning Research 30:1425–1432
DOI 10.1519/JSC.0000000000001222.
Fisher JP, Carlson L, Steele J, Smith D. 2014. The effects of pre-exhaustion, exercise
order, and rest intervals in a full-body resistance training intervention. Applied,
Physiology, Nutrition, and Metabolism 39:1–6 DOI 10.1139/apnm-2014-0162.
Fisher J, Farrow J, Steele J. 2017. Acute fatigue, and perceptual responses to resistance
exercise. Muscle and Nerve 56(6):E141–E146 DOI 10.1002/mus.25645.
Fisher J, Ironside M, Steele J. 2016. Heavier- and lighter-load resistance training to
momentary failure produce similar increases in strength with differing degrees of
discomfort. Muscle and Nerve 56(4):797–803 DOI 10.1002/mus.25537.
Fisher J, Steele J, Smith D. 2013. Evidence-based resistance training recommendations
for muscular hypertrophy. Medicina Sportiva 17:217–235
DOI 10.5604/17342260.1081302.
Fisher J, Steele J, Smith J, Bruce-Low S. 2011. Evidence based resistance training
recommendations. Medicina Sportiva 15:147–162 DOI 10.2478/v10036-011-0025-x.
Gentil P., Bottaro M. 2010. Influence of supervision ratio on muscle adaptations to
resistance training in nontrained subjects. Journal of Strength and Conditioning
Research 24:639–643 DOI 10.1519/JSC.0b013e3181ad3373.
Giessing J, Eichmann B, Steele J, Fisher J. 2016a. A comparison of low volume ‘high-
intensity-training’ and high volume traditional resistance training methods on
muscular performance, body composition, and subjective assessments of training.
Biology of Sport 33:241–249 DOI 10.5604/20831862.1201813.
Giessing J, Fisher J, Steele J, Rothe F, Raubold K, Eichmann B. 2016b. The effects of
low volume resistance training with and without advanced techniques in trained
participants. Journal of Sports Medicine and Physical Fitness 56(3):249–258.
Glass SC, Stanton DR. 2004. Self-selected resistance training intensity in novice
weightlifters. Journal of Strength and Conditioning Research 18:324–327.
Hackett DA, Cobley S, Favies T, Michael S, Halaki M. 2016. Accuracy in estimating
repetitions to failure during resistance exercise. Journal of Strength and Conditioning
Research 31(8):2162–2168 DOI 10.1519/JSC.0000000000001683.
Hackett DA, Johnson NA, Halaki M, Chow C. 2012. A novel scale to assess resistance-
exercise effort. Journal of Sports Sciences 30:1405–1413
DOI 10.1080/02640414.2012.710757.
Helms ER, Brown SR, Cross MR, Storey A, Cronin J, Zourdos M. 2017. Self-
rated accuracy of rating of perceived exertion-based load prescription in pow-
erlifters. Journal of Strength and Conditioning Research 31(10):2938–2943
DOI 10.1519/JSC.0000000000002097.
Steele et al. (2017), PeerJ, DOI 10.7717/peerj.4105 11/14
Helms ER, Cronin J, Storey A, Zourdos M. 2016. Application of the repetitions in
reserve-based rating of perceived exertion scale for resistance training. Strength and
Conditioning Journal 38:42–49 DOI 10.1519/SSC.0000000000000218.
Hiscock DJ, Dawson B, Donnelly CJ, Peeling P. 2016. Muscle activation, blood lactate,
and perceived exertion responses to changing resistance training programming
variables. European Journal of Sport Science 16:536–544
DOI 10.1080/17461391.2015.1071880.
Hiscock DJ, Dawson B, Peeling P. 2015. Perceived exertion responses to changing
resistance training programming variables. Journal of Strength and Conditioning
Research 29:1564–1569 DOI 10.1519/JSC.0000000000000775.
Hollander DB, Duran RJ, Trynicki JL, Larock D, Castracane VD, Hebert EP, Krae-
mer RR. 2003. RPE, pain, and physiological adjustment to concentric and ec-
centric contractions. Medicine and Science in Sports and Exercise 35:1017–1025
DOI 10.1249/01.MSS.0000069749.13258.4E.
Hollander DB, Kilpatrick MW, Ramadan ZG, Reeves GV, Francois M, Blakeney A,
Castracane VD, Kraemer RR. 2008. Load rather than contraction type influences
rate of perceived exertion and pain. Journal of Strength and Conditioning Research
22:1184–1193 DOI 10.1519/JSC.0b013e31816a8bc2.
Hopkins WG. 2000. Measures of Reliability in Sports Medicine and Science. Sports
Medicine 30:1–15 DOI 10.2165/00007256-200030010-00001.
Hopkins WG. 2015. Spreadsheets for analysis of validity and reliability. Sportscience
19:36–42.
Izquierdo-Gabarren M, Gonzalez De Txbarri Exposito R, Garcia-pallares J, Sanchez-
Medina J, De Villarreal ES, Izquierdo M. 2010. Concurrent endurance and strength
training not to failure optimises performance gains. Medicine and Science in Sports
and Exercise 42:1191–1199 DOI 10.1249/MSS.0b013e3181c67eec.
Marcora S. 2009. Perception of effort during exercise is independent of afferent feedback
from skeletal muscles, heart, and lungs. Journal of Applied Physiology 106:2060–2062
DOI 10.1152/japplphysiol.90378.2008.
Mazzetti SA, Kraemer WJ, Volek JS, Dunca ND, Ratamess NA, Gomez AL, Newton
RU, Häkkinen K, Fleck SJ. 2000. The influence of direct supervision of resistance
training on strength performance. Medicine and Science in Sports and Exercise
32:1175–1184 DOI 10.1097/00005768-200006000-00023.
Morton RW, Oikawa SY, Wavell CG, Mazara N, McGlory C, Quadrilatero J,
Baechler BL, Baker SK, Phillips SM. 2016. Neither load nor systemic hor-
mones determine resistance training-mediated hypertrophy or strength gains
in resistance-trained young men. Journal of Applied Physiology 121:129–138
DOI 10.1152/japplphysiol.00154.2016.
Pritchett RC, Green JM, Wickwire PJ, Pritchett KL, Kovacs MS. 2009. Acute and session
RPE responses during resistance training: bouts to failure at 60% and 90% of 1RM.
South African Journal of Sports Medicine 21:23–26.
Steele et al. (2017), PeerJ, DOI 10.7717/peerj.4105 12/14
Ratamess NA, Faigenabum AD, Hoffman JR, Kang J. 2008. Self-selected resistance
training intensity in healthy women: the influence of a personal trainer. Journal of
Strength and Conditioning Research 22:103–111 DOI 10.1519/JSC.0b013e31815f29cc.
Sampson JA, Groeller H. 2016. Is repetition failure critical for the development of muscle
hypertrophy and strength? Scandinavian Journal of Medicine and Science in Sports
26:375–383 DOI 10.1111/sms.12445.
Schoenfeld BJ, Ogborn DI, Krieger JW. 2015. Effect of repetition duration during
resistance training on muscle hypertrophy: a systematic review and meta-analysis.
Sports Medicine 45:577–585 DOI 10.1007/s40279-015-0304-0.
Schoenfeld BJ, Peterson MD, Ogborn D, Contreras B, Sonmez GT. 2015. Effects of
low- versus high-load resistance training on muscle strength and hypertrophy in
well-trained men. Journal of Strength and Conditioning Research 29:2954–2963
DOI 10.1519/JSC.0000000000000958.
Schoenfeld BJ, Wilson JM, Lowery RP, Krieger JW. 2016. Muscular adaptations in
low-versus high-load resistance training: a meta-analysis. European Journal of Sport
Science 16:1–10 DOI 10.1080/17461391.2014.989922.
Shimano T, Kraemer WJ, Spiering BA, Volek JS, Hatfield DL, Silvestre R, Vingren JL,
Fragala MS, Maresh CM, Fleck SJ, Newton RU, Spreuwenberg LP, Häkkinen K.
2006. Relationship between the number of repetitions and selected percentages of
one repetition maximum in free weight exercises in trained and untrained men.
Journal of Strength and Conditioning Research 20:819–823.
Silva VL, Azevedo AP, Cordeiro JP, Duncan MJ, Cholewa JM, Siqueira-Filho MA,
Zanchi NE, Guimaraes-Ferreira L. 2014. Effects of exercise intensity on perceived
exertion during multiple sets of bench press to volitional failure. Journal of Trainol-
ogy 3:41–46 DOI 10.17338/trainology.3.2_41.
Smirnaul BDPC. 2012. Sense of effort and other unpleasant sensations during exercise:
clarifying concepts and mechanisms. British Journal of Sports Medicine 46:308–311
DOI 10.1136/bjsm.2010.071407.
Steele J. 2014. Intensity; in-ten-si-ty; noun. 1. Often used ambiguously within resistance
training. 2. Is it time to drop the term altogether? British Journal of Sports Medicine
48:1586–1588 DOI 10.1136/bjsports-2012-092127.
Steele J, Fisher J, Giessing J, Gentil P. 2017a. Clarity in reporting terminology and
definitions of set end points in resistance training. Muscle and Nerve 56(3):368–374
DOI 10.1002/mus.25557.
Steele J, Fisher J, McGuff D, Bruce-Low S, Smith D. 2012. Resistance training to
momentary muscular failure improves cardiovascular fitness in humans: a review
of acute physiological responses and chronic physiological adaptations. Journal of
Exercise Physiology 15(3):53–80.
Steele J, Fisher J, McKinnon S, McKinnon P. 2017b. Differentiation between perceived
effort and discomfort during resistance training in older adults: reliability of trainee
ratings of effort and discomfort, and reliability and validity of trainer ratings of
trainee effort. Journal of Trainology 6(1):1–8 DOI 10.17338/trainology.6.1_1.
Steele et al. (2017), PeerJ, DOI 10.7717/peerj.4105 13/14
Steele J, Fisher J, Skivington M, Dunn C, Arnold J, Tew G, Batterham AM, Nunan
D, O’Driscoll JM, Mann S, Beedie C, Jobson S, Smith D, Vigotsky A, Phillips S,
Estabrooks P, Winett R. 2017c. A higher effort-based paradigm in physical activity
and exercise for public health: making the case for a greater emphasis on resistance
training. BMC Public Health 17:300 DOI 10.1186/s12889-017-4209-8.
Zourdos MC, Klemp A, Dolan C, Quiles JM, Schau KA, Jo E, Helms E, Esgro B, Duncan
S, Garcia-Merino Blanco R. 2016. Novel resistance training-specific rating of
perceived exertion scale measuring repetitions in reserve. Journal of Strength and
Conditioning Research 30:267–275 DOI 10.1519/JSC.0000000000001049.
Steele et al. (2017), PeerJ , DOI 10.7717/peerj.4105 14/14
