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Partial vs full range of motion resistance
training: A systematic review and meta-
analysis
Received: 24th Sept. 2022
Supplementary materials:
https://osf.io/fmvrw/
*For correspondence:
milowolf@outlook.com
Twitter: @MiloWolf12
Milo Wolf1, Patroklos Androulakis-Korakakis1, James P. Fisher1, Bradley J. Schoenfeld2, James Steele1
1 Faculty of Sport, Health, and Social Sciences, Solent University, Southampton, United Kingdom
2 City University of New York, Lehman College, New York, USA
ABSTRACT
Background: Range of motion (ROM) during resistance training is of growing interest and is
potentially used to elicit differing adaptations (e.g. muscle hypertrophy and muscular strength and
power). To date, attempts at synthesising the data on ROM during resistance training have primarily
focused on muscle hypertrophy in the lower body.
Objective: Our aim was to meta-analyse and systematically review the effects of ROM on a variety of
outcomes including hypertrophy, strength, sport, power and body-fat type outcomes. Following pre-
registration and consistent with PRISMA guidelines, a systematic review of PubMed and
SportsDISCUS was performed. Data was extracted and a Bayesian multi-level meta-analysis was
performed. A range of exploratory sub-group and moderator analyses were performed.
Results: The main model revealed a trivial SMD (0.13; 95% CI: −0.01, 0.27) in favour of full ROM
compared to partial ROM. When grouped by outcome, SMDs all favoured full ROM, but SMDs were
trivial to small (all between 0.05 to 0.2). Sub-group analyses suggested there may be a muscle
hypertrophy benefit to partial ROM training at long muscle lengths compared to using a full ROM
(SMD=−0.28, 95% CI: −0.81, 0.16). Analysis also suggested the existence of a specificity aspect to
ROM, such that training in the ROM being tested as an outcome resulted in greater strength
adaptations. No clear differences were found between upper- and lower-body adaptations when
ROM was manipulated.
Conclusions: Overall, our results suggest that using a full or long ROM may enhance results for most
outcomes (strength, speed, power, muscle size, and body composition). Differences in adaptations
are trivial to small. As such, partial ROM resistance training might present an efficacious alternative
for variation and personal preference, or where injury prevents full-ROM resistance training.
Please cite as: Wolf, M., Androulakis-Korakakis, P., Fisher, J.P., Schoenfeld, B.J., & Steele, J., (2022). Partial
vs full range of motion resistance training: A systematic review and meta-analysis. DOI:
https://doi.org/10.51224/SRXIV.198
2
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Introduction
Resistance training (RT) is commonly used to induce muscle hypertrophy,
increase strength and improve sport performance. Indeed, resistance training is
employed across a variety of sports, notably sports in which muscularity is directly
rewarded (e.g. bodybuilding) or where resistance training is the sport itself (e.g.,
powerlifting and strongman) to ones in which resistance training can improve
performance on the field(e.g. enhance vertical jump, sprint time, etc.) [1,2].
In recent years, the range of motion (ROM) employed during RT has
become a controversial topic. Whilst some findings suggest a superiority of full
ROM (fROM) in some contexts (e.g. when muscle hypertrophy is desired in several
muscle groups), others argue for the use of partial ROM (pROM) in other contexts
(e.g. when muscle hypertrophy is only desired in specific muscle groups) [3,4].
Whilst, both pROM and fROM RT produce improvements in muscle size, it has
been reported that fROM RT is more efficacious for promoting muscle
hypertrophy in the lower body [3]. Evidence in the upper body is more equivocal
[5,6] and research has not been consolidated in review. Similarly, in regard to
performance outcomes such as strength, both pROM and fROM RT have been
shown to stimulate improvements [7–9]. Specifically, in resistance-trained men,
both pROM and fROM lower body RT have been shown to elicit improvements in
performance outcomes such as counter-movement jump height, 20 m sprint time
and Wingate Test peak and mean power [10].
Whilst both pROM and fROM RT have been shown to produce
improvements in a variety of muscle size and performance outcomes it is unclear
which strategy, if any, results in greater adaptations. Indeed, there are inherent
differences between pROM and fROM RT that could plausibly lead to meaningfully
different adaptations, both in magnitude and in transferability to performance
outcomes. For example, it has been shown that during isometric training, the
length at which a muscle is trained impacts the resulting adaptations [11].
Evidence suggests that isometrically training a muscle at longer lengths may
produce greater increases in muscle volume than training it at short lengths [11].
In addition, improvements in isometric peak force appear to be joint angle-
specific, such that training a muscle at shorter lengths likely results in greater
improvements in isometric peak force at shorter muscle lengths and vice versa
[11]. It is unclear whether these findings apply to dynamic resistance training.
In addition, it has been suggested that pROM RT may promote greater
muscle deoxygenation and greater blood lactate accumulation compared to fROM
RT [6]. Mechanistically, these differences in acute responses to ROM may lead to
divergent training adaptations [12,13]. In addition, it is plausible that pROM RT
may lead to greater improvements in performance outcomes such as vertical
3
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jumps and sprint times compared to fROM RT [14]. Indeed, through training the
joint angles in a task-specific manner, pROM may be superior to fROM in inducing
these adaptations. There is likely no “one-size-fits-all” approach to ROM in training
for different sports/movement patterns. In rugby, for example, scrumming may
benefit moreso from pROM training, whereas tasks like baseball pitching, which
involve greater ranges of motion, may benefit from fROM training. Finally, pROM
training might be beneficial when an athlete/trainee has a musculoskeletal injury,
for example, where loading a muscle through a fROM may accentuate pain [15].
To summarize – it is unclear if and when using different ranges of motion may
lead to different results in morphological and/or musculoskeletal function
outcomes.
A previous systematic review by Schoenfeld & Grgic (2020) examined the
effect of ROM during RT on muscle hypertrophy [3]. Although data were limited
at the time of publication, this review suggested that greater ROM was superior
for hypertrophy in the lower body musculature, but the effects of ROM were less
clear in the muscles of the upper body. More recently, a meta-analysis and
systematic review on the effects of ROM on training adaptations was published
by Pallares et al. (2021) [16]. The findings suggested that full ROM was superior
for muscle strength, functional performance and lower-limb muscle hypertrophy.
The authors abstained from analysing data on upper-limb muscle hypertrophy
due to scarcity of evidence. Despite the currently available literature on the effect
of ROM on upper-limb hypertrophy and/or strength being limited, meta-
analytically assessing the totality of the available literature may allow us to better
understand the effect of pROM versus fROM on a multitude of musculoskeletal
and morphological outcomes. Additionally, previous research has not included
further sub-analyses on different moderators within the topic of full versus partial
ROM (e.g. muscle length at which pROM is performed). Thus, the current article
aims to both review and meta-analyse the available data on ROM and
musculoskeletal function and morphology.
Methods
This systematic review and meta-analysis were conducted in accordance
with Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) guidelines [17]. This study was pre-registered on the Open Science
Framework (OSF; https://osf.io/j96e7) using the International Prospective Register
of Systematic Reviews (PROSPERO) template, though some of the methods
adopted have changed since the original pre-registration. Where applicable,
changes from the initial pre-registration will be outlined and justified.
4
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Inclusion Criteria
Both full-text, peer-reviewed studies and doctoral/master’s theses were
included when they were available in English. Studies included needed to involve
a resistance training intervention with at least two groups/conditions using
varying ROM and measuring at least one outcome of interest (muscle size, muscle
strength, sports, power or bodyfat). No restrictions were placed on publication
date.
Search Strategy
PubMed/Medline and SportsDISCUS databases were searched for studies
up to August 2022. The following search string was used: ““resistance training”
AND “range of motion” AND (“muscle thickness” OR “cross sectional area” OR
“muscle volume” OR “muscle mass” OR “hypertrophy” OR “muscle strength”). Both
the abstracts/titles and the full-texts were examined for inclusion by MW and PAK.
Screening was performed using abstrackr (http://abstrackr.cebm.brown.edu/).
Studies deemed irrelevant were excluded. Once all studies returned through the
search had been screened for inclusion, the reference lists of included studies
were screened for inclusion. Publications that cited included studies were also
screened for inclusion.
Quality Assessment
The quality of studies that met inclusion criteria was assessed using the
TESTEX scale [18]. The TESTEX scale is an alternative to the PEDro scale designed
specifically for exercise science training studies [18]. It has been shown to be
reliable and is composed of 12 items relating to both study quality and study
reporting. Finally, a GRADE table of evidence (Table 2) was produced to clearly
communicate findings using GradePro (https://www.gradepro.org/) .
Data Extraction
The following data was extracted/coded from studies that met inclusion
criteria by MW: study design, weighted mean age, weighted mean height,
intervention duration, total study duration, sex of participants, training status,
population, ROM used by the pROM group/condition, ROM used by the fROM
group/condition, proportion of sets being performed with a full/PROM, muscle
length trained, training frequency, mean number of weekly sets performed, mean
repetition duration, mean number of repetitions performed per set, number of
exercises, mean proximity to momentary muscular failure, mean load, modality
of training, presence of auxiliary interventions and whether other exercises were
performed besides the exercise(s) on which ROM was manipulated. The pre-
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registration noted two groupings of outcomes (musculoskeletal function and
morphology) though noted that after the systematic search and review additional
outcomes would be extracted depending on what studies had measured. After
data extraction we opted to group outcomes into the following categories: body
composition outcomes, strength outcomes, power outcomes, and sport
outcomes. Finally, if an outcome measure favoured, that is to say may have been
biased towards, either full or pROM group/condition (e.g. partial squat 1RM
favouring a partial squat group), this was also noted. Where data was not available
in the full-text, the authors were contacted to request missing data. When their
contact information was unavailable, the institution at which the work was
performed was contacted to obtain it. If no response was received to the initial
request, a second email was sent a few weeks later. If no response was obtained
to the second attempt, data was obtained via WebPlotDigitizer (v4.4, Ankit
Rohatgi) where possible. The data were transcribed/imported into a .csv file.
Meta-Analysis
All analysis code utilized is presented in the supplementary materials
(https://osf.io/fmvrw/). Given the aim of this research, we opted to take an
estimation-based approach [19], conducted within a Bayesian framework [20]. For
all analyses, effect estimates and their precision, along with conclusions based
upon them, were interpreted continuously and probabilistically, considering data
quality, plausibility of effect, and previous literature, all within the context of each
outcome [21]. The main exploratory meta-analysis was performed using the
‘brms’ package [22] with posterior draws taken using ‘tidybayes’ [23] and
‘emmeans’ in R (v 4.0.2; R Core Team, https://www.r-project.org/) [24]. All data
visualizations were made using ‘ggplot2’ [25], and ‘patchwork’ [26].
As the included studies often had multiple groups/conditions and reported
effects within these for multiple sessions/exercises/sets - we opted to calculate
effect sizes as a nested structure. Therefore, multilevel mixed-effects meta-
analyses were performed with both inter-study and intra-study groups included
as random effects in the model. Effects were weighted by inverse sampling
variance to account for the within- and between-study variance. A main model
included all effects for all outcomes in the included studies. We also conducted
several exploratory meta-regression and sub-group analyses of moderators (i.e.,
predictors of effects) to explore study protocols and participant characteristics.
Moderators examined included the outcome subcategory (strength, muscle size,
body fat, power, or sport performance proxies), study design (between- or within-
participant), upper vs lower body, the length at which muscles where trained in
the pROM condition (short, middle, or long; this was also specifically explored for
muscle size outcomes alone), the modality of resistance (free weights, resistance
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machines, or a combination), whether the outcome measures were in any way
specifically biased towards either fROM or pROM (e.g., a fROM 1RM outcome
would perhaps be biased towards fROM, and vice versa for a pROM 1RM outcome
for pROM), participants’ mean height (considered to be related to limb lengths),
intervention duration, the proportion of volume performed with a fROM, the
proportion of fROM used by the pROM condition, time under load per repetition,
and for muscle size whether proximal or distal muscle sites where measured.
For all models, we used uninformed priors; recent meta-analyses might
have been used to inform priors, however this would constitute a form of ‘double
counting’ given the studies that were included in them have also been included in
the likelihoods for the present models. Models were estimated using 23
1
Monte
Carlo Markov Chains with 2000 warmup and 6000 sampling iterations. Trace plots
were used to examine chain convergence and posterior predictive checks to
examine model validity. Draws were taken from the posterior distributions to
construct probability density functions for plotting. We then calculated the mean
and the 95%, 80%, and 50% quantile intervals (‘credible’ or ‘compatibility’ intervals)
from the posterior probability density functions for each group effect estimate.
These gave us the most probable value of the parameter for a given level of
probability.
Results
The search string identified 576 publications/theses for potential inclusion,
while 19 others were identified through websites and citation searching. Once
duplicates were removed, 344 studies remained. The titles and abstracts were
screened, and, where deemed appropriate, full-text versions were sought to
determine eligibility. Ultimately, 26 studies were included in review. One study
was eventually excluded during the data extraction due to excessive missing data.
Two further theses were excluded because they contained the same data as
another publication that was already included. Figure 1 details this process. Table
1 provides summary data of the 23 studies that were included for analysis.
1
C -1 where C was the number of cores available on the computer used to run the analysis (build
available here: https://uk.pcpartpicker.com/list/C6VXRT).
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Table 1. Summary of studies included
Study
N
Design
Program
duration
(weeks)
Upper or
Lower
Body
pROM
fROM
Summary of findings
TESTEX
Score
(/12)
(Graves et al.,
1989)[27]
44
Between
participant, 3
groups
10
L
60 °
120 °
Some significant between-group differences –
i.e. training a specific ROM I resulted in greater
isometric strength gains in that ROM
5
(Graves et al.,
1992) [28]
48
Between
participant, 3
groups
12
U
36 °
72 °
Some significant between-group differences –
i.e. training a specific ROM I resulted in greater
isometric strength gains in that ROM
5
(Weiss et al.,
2000) [29]
?
Between
participant
8
L
55 °*
110 °*
fROM experienced significantly greater fROM
1RM improvements than pROM
4
(Crocker,
2000)[30]
22
Between
participant
7
L
68.5 °/
113.6°/
fROM saw significantly greater improvements in
1RM and CMJ velocity
6
(Massey et al.,
2004)[31]
56
Between
participant
10
U
?
?
No significant between-group differences for
1RM
4
(Massey et al.,
2005) [32]
21
Between
participant
10
U
?
?
Significantly greater improvement in 1RM for
fROM than pROM
3
(Clark et al.,
2011) [14]
22
Between
participant
5
U
?
?
Significant differences in favour of pROM for
bench throw height & ½ ROM force. No other
differences.
5
(Hartmann et
al., 2012) [33]
39
Between
participant
10
L
20 °*
110 °*
Significant between-group differences in favour
of fROM for fROM 1RMs and in favour of pROM
for pROM 1RMs.
5
(Pinto et al.,
2012) [5]
30
Between
participant
10
U
50 °
130 °
Significantly greater improvements in 1RM for
fROM than pROM
5
(Steele et al.,
2013) [7]
22
Between
participant
12
U
36 °
72 °
No significant differences between groups for
lumbar extension strength
5
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(Bloomquist et
al., 2013) [34]
17
Between
participant
12
L
60 °
120 °
Significant differences in favour of pROM for
pROM 1RM & in favour of fROM for fROM 1RM.
All CSA sites & SJ height
6
(Bazyler et al.,
2014) [35]
17
Between
participant
7
L
80 °*
110 °*
A few significant differences in favour of pROM
in impulse & IPFa at 120°
5
(McMahon et al.,
2014) [36]
16
Between
participant
8
L
50 °
90 °
Some significant differences in favour of fROM
for aCSA & a few in favour of pROM for MVC in
pROM angles
5
(Rhea et al.,
2016) [37]
28
Between
participant, 3
groups
16
L
60 or
90 °*
110 °*
Significant differences in favour of fROM for
fROM 1RM & pROM for pROM 1RM, vert. jump
& sprint test.
7
(Valamatos et al.,
2018) [38]
11
Within Participant
15
L
60 °
100 °
No significant differences for muscle size.
Maximum torque improved significantly more
in respective ROMs.
7
(Goto et al., 2019)
[39]
44
Between
participant
8
U
45 °
120 °
Significantly greater improvements in muscle
CSA & isometric strength for pROM than
fROM.
5
(Esmaeeldokht,
2019) [40]
14
Between
participant
8
L
?
?
No significant between-group differences for
1RMs/ bodyfat%.
3
(Martinez-Cava et
al., 2019) [41]
24
Between
participant
10
U
?
?
More ROM generally led to better 1RM and
MPV at %1RM outcomes & strength gains were
greatest in trained ROM
5
(Kubo et al.,
2019)[42]
17
Between
participant
10
L
90 °
140 °
Significantly greater improvements in fROM
1RM & adductor/gluteus maximus growth for
fROM than pROM.
6
(Pallares et al.,
2020)[10]
36
Between
participant, 3
groups
10
L
? & 90
°
?
More ROM generally led to better 1RM and
MPV at %1RM outcomes & strength gains were
greatest in trained ROM. No significant
differences for WGT, CMJ/sprint time.
7
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(Whaley et al.,
2020) [43]
36
Between
participant
7
L
?
?
Similar improvements in VJ height, full squat
1RM and power output when increasing ROM
from pROM to fROM compared to
continuously training with fROM.
7
(Sadacharan &
Seo, 2021) [44]
34
Within participant
3
Both
60 °
?
Both pROM and fROM generally led to
improvements in MVIC.
4
(Werkhausen et
al., 2021) [9]
15
Within participant
10
L
9 °
79 °/
pROM & fROM generally led to similar
improvements in peak torque, force, power,
RTD & muscle thickness
6
(Pedrosa et al.,
2021) [45]
45
Within participant,
4 groups
12
L
35 °
70 °
Training pROM at longer muscle lengths
generally resulted in greater muscular and
strength adaptations than fROM or pROM at
shorter muscle lengths.
5
*= ROM assumed based on existing biomechanical analyses of squat depth. Details available in supplementary materials.
/= ROM digitized using manuscript. Details available in supplementary materials.
DOI: 10.31236/osf.io/eq485
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Summary of study characteristics
Range of motion control
The methods used to control ROM varied from study-to-study. Some studies
used mechanical stops built-in to the equipment being used – such as isokinetic
dynamometers/electric goniometers/tensiometers [27]. In other studies,
participants’ ROM was controlled using physical stops like the metallic bars used to
delineate partial ROM by Pedrosa et al. (2021) & Pinto et al. (2012) [5,45]. Finally, in
some studies, the ROM used was less clearly defined and participants were
supervised by personnel to ensure the ROM being used was correct – though the
accuracy of this method may not be ideal [31].
It is also interesting to note that few studies individualised the ROM being used
to the individual’s fROM [31]. In other words, for most studies, a certain amount of
ROM was deemed a “full” ROM, regardless of what each individual participant’s fROM
truly was. The specifics of ROMs being used can be found in Table 1.
Muscle length of partial range of motion training
It is worth noting that most studies (19/23 studies) examined pROM when
performed – at least for some of the volume performed – at short muscle lengths. In
contrast, relatively few studies have examined pROM at either moderate muscle
lengths (1/23) (defined as the middle of fROM) or long muscle lengths (6/23). The
specific findings of all studies can be seen in Table 1 and a GRADE table of evidence
can be seen in Table 2.
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Table 2 GRADE Table of evidence
Certainty assessment
№ of patients
Effect
Certainty
Importance
№ of
studies
Study
design
Risk of
bias
Inconsistency
Indirectness
Imprecision
Other
considerations
Full range
of motion
partial
range of
motion
Relative
(95% CI)
Absolute
(95% CI)
Muscle Strength (follow-up: median 10 weeks; assessed with: Isometric Strength, Isometric Torque, Partial ROM 1RM, Full ROM 1RM, Relative Peak Force, 6RM, Peak Force, Maximum
Voluntary Contraction, Specific Tension, Fascicle Force, Specific Torque, Relative Full ROM 1RM, Relative Partial ROM 1RM)
24
randomised
trials
not
serious
not serious
not serious
seriousa
none
311
428
-
SMD 0.14
SD
higher
(0.01
lower to
0.29
higher)b
⨁⨁⨁◯
Moderate
Sport (follow-up: median 10 weeks; assessed with: Standing Vertical Jump Height, Depth Jump Height, Counter-Movement Jump Vertical Take-Off Velocity, Counter-Movement Jump
Height, Counter-Movement Jump Force, Squat Jump Height, 40 yard sprint time, 20 meter sprint time)
7
randomised
trials
not
serious
not serious
not serious
not seriousa
none
82
108
-
SMD 0.02
SD
higher
(0.22
lower to
0.26
higher)
⨁⨁⨁⨁
High
Power (follow-up: median 10 weeks; assessed with: Relative Peak Power, Counter-Movement Height, Counter-Movement Force, Half-ROM Force, Unilateral Maximal Rate of Force
Development, Isometric Rate of Force Development, Mean Propulsive Velocity at different %1RM and ROMs, Peak and Mean Power during Wingate Test, Peak Power, Peak Velocity, )
8
randomised
trials
not
serious
not serious
not serious
seriousa
none
99
127
-
SMD 0.19
SD
higher
(0.01
higher to
0.37
higher)
⨁⨁⨁◯
Moderate
Muscle size (follow-up: median 10 weeks; assessed with: Muscle Thickness, Regional Cross-Sectional Area, Muscle Volume)
8
randomised
trials
not
serious
not serious
not serious
seriousa
none
96
116
-
SMD 0.04
SD
higher
(0.17
lower to
0.25
higher)
⨁⨁⨁◯
Moderate
Body Fat (follow-up: median 8 weeks; assessed with: Body Fat Percentage, Regional Subcutaneous Fat Thickness, Skinfold Body Fat, Waist to Hip Ratio )
4
randomised
trials
not
serious
not serious
not serious
not seriousa
none
30
29
-
SMD 0.12
SD
higher
(0.36
lower to
0.6
higher)
⨁⨁⨁⨁
High
CI: confidence interval; SMD: standardised mean difference
Explanations
a. SMDs were as large as ~1.25. Further, data was unavailable even upon request for several studies.
b. SMD were used.
Meta-Analysis Results
Main Model – all outcomes
The main model – including all effects on all outcomes across 23 studies –
revealed a trivial SMD (0.12; 95% CI: –0.02, 0.26) in favour of fROM compared to
pROM (Figure 2). All effect sizes (ticks), posterior probability distributions and the
overall estimate are displayed below in Figure 2.
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Fig. 2 Overall Model
Sub-Group Analyses
Grouped by outcome type
Outcomes were grouped by type (such as “power” and “muscle size” outcomes)
and sub-group analyses were performed. For “strength” type outcomes (e.g. 1RM
test), analysis revealed a trivial SMD (0.14; 95% CI: -0.01, 0.29) in favour of fROM. For
“sport” type outcomes (e.g. sprint time), analysis suggested a trivial SMD (0.02; 95%
CI: –0.22, 0.26) in favour of fROM. For “power” type outcomes (e.g. rate of force
development), analysis showed a trivial SMD (0.19; 95% CI: 0.01, 0.37) in favour of
fROM. For “muscle size” type outcomes (e.g. muscle cross-sectional area), analysis
revealed a trivial SMD (0.04; 95% CI: –0.17, 0.25) in favour of fROM. Finally, for “Body
Fat” type outcomes (e.g. bodyfat %), analysis suggested a trivial SMD (0.12; 95% CI: –
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0.36, 0.6) in favour of fROM. Figure 3 displays individual effect sizes as ticks, posterior
probability distributions and overall estimates for each outcome.
Fig. 3 Outcome Sub-Group Analysis
Study design
Studies were categorized as either being within-subject designs (e.g. the same
subjects used different ranges of motion for different limbs) or between-subject
designs (e.g. subjects were assigned to performing either a fROM intervention or a
pROM intervention). For within-participant designs, analysis revealed a small SMD
(0.22; 95% CI: –0.16, 0.6) favouring fROM for all outcomes. For between-participant
designs, analysis revealed a trivial SMD (0.1; 95% CI: –0.06, 0.25) favouring fROM.
Figure 4 displays individual effect sizes as ticks, posterior probability distributions
and overall estimates for each outcome.
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Fig. 4 Study Design Sub-Group Analysis
Proximal vs Distal Muscle Hypertrophy
Hypertrophy outcome assessments were grouped as being either “proximal”
(i.e. <50% of muscle length from origin) or “distal” (i.e. >50% of muscle length from
origin) when regional muscle hypertrophy assessment methods were used. For
proximal muscle hypertrophy, a trivial SMD (0.17; 95% CI: –1.29, 1.72) was found in
favour of fROM. For distal muscle hypertrophy, a small SMD (0.31; 95% CI: –1.14, 1.86)
was found in favour of fROM. Individual effect sizes, posterior probability
distributions and overall sub-group estimates can be found in Figure 5.
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Fig. 5 Regional Hypertrophy Sub-Group Analysis
Resistance Training Modality
Resistance training interventions were categorized into using either resistance
machines, free weights, or a combination of both. For interventions using exclusively
resistance machines, sub-group analysis revealed a trivial SMD (0.17; 95% CI: –0.06,
0.38) in favour of fROM. For interventions using exclusively free weights, analysis
showed a trivial SMD (0.05; 95% CI: –0.15, 0.26) in favour of fROM. Finally, for
interventions using a combination of these two modalities, analysis revealed a small
SMD (0.27; 95% CI: –0.28, 0.83) in favour of fROM. Individual effect sizes, posterior
probability distributions and overall sub-group estimates can be found in Figure 6.
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Fig. 6 Resistance Modality Sub-Group Analysis
Upper vs Lower Body
Studies were grouped into training either the lower- or the upper-body. For
upper-body interventions, analysis showed a trivial SMD (0.07; 95% CI: –0.18, 0.33)
favouring fROM. Likewise, for lower-body interventions, analysis also revealed a
trivial SMD (0.1; 95% CI: –0.07, 0.27) favouring fROM. Individual effect sizes, posterior
probability distributions and overall sub-group estimates can be found in Figure 7.
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Fig. 7 Upper- vs Lower- Body Sub-Group Analysis
Outcome Bias
Outcomes were grouped into being either “biased” (in the sense that training
performed was more alike the test being used as an outcome) in favour of the pROM
group, the fROM group or there not being a clear bias for the outcome. Analysis
revealed a trivial SMD (–0.12; 95% CI: –0.31, 0.07) in favour of pROM for outcomes
that were biased in favour of the pROM group, a trivial SMD (0.02; 95% CI: –0.15, 0.19)
in favour of fROM for outcomes with no clear bias and a small SMD (0.32; 95% CI:
0.14, 0.49) in favour of fROM for outcomes that were biased in favour of the fROM
group. Individual effect sizes, posterior probability distributions and overall sub-
group estimates can be found in Figure 8.
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Fig. 8 Outcome Bias Sub-Group Analysis
Muscle Length & Muscle Hypertrophy
pROM interventions were categorized as training muscle groups at either
“short” or “long” muscle lengths
2
. When the average assumed muscle length during
the pROM condition was lower than during the fROM condition, this was regarded as
“short” and vice versa for “long” muscle lengths. Analysis revealed a trivial SMD (0.08;
95% CI: –0.24, 0.42) in favour of fROM for muscle hypertrophy when pROM was
performed at short muscle lengths. Conversely, when pROM was performed at long
muscle lengths, analysis showed a small SMD (–0.28; 95% CI: –0.81, 0.16) in favour of
pROM for muscle hypertrophy. Individual effect sizes, posterior probability
distributions and overall sub-group estimates can be found in Figure 9.
2
It is important to acknowledge the assumption that joint angle and muscle length likely don’t correlate
perfectly; for the purposes of this exploratory sub-group analysis, this assumption was deemed
acceptable.
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Fig. 9 Muscle Length during pROM sub-group analysis
Meta-regression analyses
Proportion of sets done with a fROM
Only non-warm-up sets were accounted for. The proportion of sets done with
a fROM had a trivial impact on outcomes with a slope of β= 0.01 (95% CI: –0.92, 0.95).
Quantile intervals can be seen in Figure 10 below.
Fig. 10 Proportion of volume as full ROM meta-regression
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Proportion of fROM done by the pROM condition
The proportion of fROM done by the pROM condition had a trivial impact on
outcomes with a slope of β= 0.01 (95% CI: –0.87, 0.91). Quantile intervals can be seen
in Figure 11 below.
Fig. 11 Proportion of fROM that pROM trained meta-regression
Height
The height of participants had a trivial impact on outcomes with a slope of β=
0.03 (95% CI: –0.00, 0.06). Quantile intervals can be seen in Figure 12 below.
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Fig. 12 Participant Height Meta-Regression
Intervention Duration
The duration of the training intervention had a trivial impact on outcomes with
a slope of β= –0.02 (95% CI: –0.06, 0.03). Quantile intervals can be seen in Figure 13
below.
Fig. 13 Intervention Duration Meta-Regression
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Time Under Load
The time under load per repetition had a trivial impact on outcomes with a
slope of β= –0.06 (95% CI: –0.31, 0.18). Quantile intervals can be seen in Figure 14
below.
Fig. 14 Time Under Load per Repetition Meta-Regression
Quality Assessment
Quality of the evidence
The TESTEX scale was used to assess study quality. As can be seen in Table 1,
the range of TESTEX scores was 3-8/12. The most commonly met criteria for study
quality included groups being similar at baseline, titration/progression of relative
training intensity across the program and at least some of the statistical tests’ results
being reported. The least commonly met criteria included complete reporting of the
outcome data (including measures of variance) using point estimates and measuring
and/or reporting adherence during the intervention.
Potential bias in the review process
One of this review’s unique features is the inclusion of Master’s/Doctoral
Theses. Indeed, by including theses, more data can be analysed and greater
confidence can be had in the findings of this review. Further, this review screened
abstracts from three separate databases, in addition to reference/citation checking.
As such, it is hoped that, if not the entirety of the literature on ROM, the vast majority
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of the relevant literature was included. Inclusion criteria were purposely kept simple
and lenient for that reason. The use of the TESTEX scale also provides a gauge of
study quality. With that being said, this review also suffers from a few meaningful
limitations. Firstly, the inclusion of theses may result in the inclusion of data that has
not undergone a peer review process as rigorous as published data. Secondly,
though an effort has been made throughout the manuscript to indicate that sub-
group or regression analyses are deemed exploratory, it is worth reiterating that
many of these analyses lack the data and statistical power to make any confident
inferences. Finally, while an effort was made to obtain as much of the data as
possible, we were unable to obtain some of the data. Thus, it is possible that the
results of this review could have been meaningfully different had all the data been
available.
Discussion
This article aimed to review and meta-analyse the effects of ROM during RT on a
range of outcomes. The major finding from this systematic review and meta-analysis
was that ROM during RT appears to have at most a modest impact on outcomes of
interest. When all outcomes were pooled, the impact of ROM was trivial to small.
Our results suggest that different ROMs may be appropriate for different goals.
For example, when training for a specific performance outcome (e.g. a partial squat
1RM in a powerlifting competition), it appears that training in a similar ROM may
maximise improvements by a trivial to small margin. These results strongly suggest
that the principle of specificity applies to ROM – though the benefit may be more
modest than commonly assumed. When looking at outcomes grouped by category
(e.g. muscle size, strength, etc.), differences in results between pROM and fROM were
largely trivial. That said, it is noteworthy that all effect sizes, although small in
magnitude, directionally favoured fROM. As such, utilising a fROM during resistance
training may prove to be an effective “default” strategy. It is important to note that
the use of ROM is not necessarily a binary decision as some training can be fROM
while other training may be pROM.
Our analyses also supported the hypothesis that performing pROM RT at long
muscle lengths results in greater muscle hypertrophy than both pROM RT at short
muscle lengths and fROM RT. This suggests that if muscle hypertrophy is the goal,
trainees may wish to use pROM RT at long muscle lengths in their training. There is
substantial supporting evidence for the concept of resistance training at long muscle
lengths for optimising hypertrophy. Oranchuk et al. (2019)’s systematic review on the
effects of isometric training on adaptations suggested that across three studies that
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included isometric training at different muscle lengths, longer muscle length training
resulted in greater increases in muscle size in all three [11].
The evidence directly comparing the effects of pROM RT at different muscle
lengths on muscle size is also reasonably consistent. Six studies exist in this area. As
reviewed above, Pedrosa et al. (2021) [45] showed greater quadriceps growth
following pROM RT at longer compared to shorter muscle lengths. A similar previous
study by McMahon et al. (2014) [54] had seen similar results in the vastus lateralis.
Further, Maeo et al. (2020) also saw greater hypertrophy in the biarticular segments
of the hamstrings following RT at longer muscle lengths compared to RT at shorter
muscle lengths [46]. Similar results were found by both Sato et al. (2021) in the elbow
flexors [47]. A further study by Maeo et al. (2022) featured a within-subject design
comparing “neutral-arm” and “overhead-arm” elbow extensions and showed greater
hypertrophy in all 3 heads of the triceps brachii in the longer muscle length condition
[48]. This finding is noteworthy, since only the long head of the triceps brachii was
trained at longer muscle lengths during the “overhead” condition; yet the lateral and
medial heads of the triceps brachii also saw greater hypertrophy. In contrast with
this study, a study by Stasinaki et al. (2018) found no significant differences in triceps
brachii long head hypertrophy following pROM RT at longer vs shorter muscle
lengths [49]. Further, long muscle lengths generally appear to result in a greater
degree of passive tension as passive tissues begin to reach maximal length and
provide resistance to further increases in muscle length [50]. Tension itself has been
suggested to activate the mTORC1 pathway which is associated with muscle
hypertrophy [51]. A greater degree of passive tension during pROM RT at long muscle
lengths may thus contribute to greater mTORC1 pathway activation and thus greater
muscle hypertrophy than during pROM RT at short muscle lengths. Further,
emerging evidence also suggests stretch-mediated hypertrophy may play a
substantial role in humans. In a recent investigation by Warneke et al. (2022) [52], the
gastrocnemius muscle showed substantial hypertrophy when stretched at the
maximally dorsiflexed position for an hour per day for six weeks.
While these bodies of literature are perhaps not convincing enough on their own,
when considered in combination, the evidence converges to suggest that training at
longer muscle lengths is very likely of benefit when seeking to maximise muscle
growth. It is possible that fROM RT is only superior to pROM RT if and when it includes
longer muscle lengths. Further, it is a possibility that pROM RT at long muscle lengths
– and even isometric contractions at long muscle lengths – may be equal to or
superior to fROM RT for inducing muscle hypertrophy, however, this area requires
more research.
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Given how effective pROM at long muscle lengths appears to be, previous reviews
on muscle hypertrophy and ROM may have over-estimated the beneficial impact of
fROM on muscle hypertrophy. Specifically, in their meta-analysis, Pallarés et al. (2021)
found a large effect size (0.88) in favour of fROM for muscle hypertrophy [16].
Notably, only lower-limb hypertrophy was analysed; as such, only 4 studies were
included. In contrast, when looking only at muscle hypertrophy outcomes, our
analysis revealed a trivial SMD of 0.05 (Figure 3.). This difference is likely explained
by the inclusion of more data; studies including upper-body muscle groups as well
as studies that have been published after the analysis by Pallarés et al. (2021) [16].
In their systematic review, Schoenfeld & Grgic (2020) concluded that evidence
suggested that fROM RT was superior to pROM RT for lower-limb hypertrophy but
that the effects were less clear in the upper body [3]. The difference between this
article’s results and theirs likely stems from the inclusion of trials that have been
published since the publication of Schoenfeld & Grgic's (2020) review article [3]. They
also surmised that the response to ROM during RT may be muscle-specific. Our sub-
group analysis (Figure 7.) comparing upper- vs- lower body outcomes does not
support this idea, though further research would be helpful in testing this hypothesis.
Several studies have found greater distal hypertrophy (defined as >50% of the
muscle length from the origin) following fROM RT or pROM RT at long muscle lengths
compared to pROM RT at short muscle lengths, but similar proximal hypertrophy
[8,34,45]. That said, sub-group analysis of regional hypertrophy (Figure 5.) only
showed a small SMD (0.31; 95% CI: –1.28, 1.85) in favour of fROM RT for distal
hypertrophy and a trivial SMD (0.16; 95% CI: –1.43, 1.73) in favour of fROM RT for
proximal hypertrophy. If a difference in regional hypertrophy does exist between
pROM and fROM RT or shorter and longer muscle length training, further data are
required to give this conclusion further credibility.
It is important to note that for some outcomes (such as bodyfat) and some
sub-group or moderator analyses (such as proximal vs distal hypertrophy), the
analyses are based on very few data and are relatively underpowered. As such,
caution is advised when drawing conclusions.
The reader can adopt two viewpoints. The first best befits researchers and is
more conceptual. It consists in regarding ROM as a relatively inconsequential
variable, many of these analyses as being underpowered and viewing range of
motion research as an area in its infancy, lacking the data required to come to any
sort of consensus on the topic.
The second viewpoint aims to minimize “false negative” errors and best befits
practitioners. Using one range of motion vs. another has little to no practical
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downside. Therefore, even if the benefit of one strategy over the other is small and
uncertain, it is likely still worth adopting provided there are no contraindications such
as personal preference, load availability or injury management. The practitioner may
also recognize the value in small effects whose existence is relatively uncertain, as
even these small potential gains may be meaningful to many coaches and athletes,
competitive and recreational alike [53].
Conclusion
fROM outperformed pROM for all outcome types, but effect sizes ranged from
trivial to small at best. It appears that there may be small differences in outcomes
depending on exactly how ROM was manipulated (e.g., short vs long muscle lengths
for regional hypertrophy), so coaches/athletes may wish to adopt the ROM strategy
most appropriate to their goals. The principle of specificity likely also applies to ROM,
such that training should usually replicate the ROM of the outcome of interest. While
using a fROM approach may be a good “default” approach, overall, these results
suggest that a variety of ROMs can be used to good effect, whether that be due to
injury management or personal preference.
The researchers would be interested in seeing future studies compare the
adaptations following pROM training at different muscle lengths compared to a
fROM. For example, a study examining muscle thickness adaptations following
resistance training in two pROM conditions at different muscle lengths and one fROM
condition. For ease of future analysis and/or replication, future research should also
ensure data is either openly available or, at least, easier to extract. Failing this, efforts
should be made to provide data upon request.
Funding information
Funding for the lead investigator’s PhD project was provided by Renaissance Periodization.
Data and Supplementary Material Accessibility
All materials, data, and code are available on the Open Science Framework project page for
this study https://osf.io/fmvrw/
Author contributions
MW wrote the first draft of the manuscript. MW and PAK performed the literature search.
JS performed the meta-analyses. All authors were involved in the interpretation of the
meta-analyses, read, revised, and approved the final manuscript.
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