SYSTEMATIC REVIEW
Effects of Strength Training on the Physiological Determinants
of Middle- and Long-Distance Running Performance:
A Systematic Review
Richard C. Blagrove1,2 • Glyn Howatson2,3 • Philip R. Hayes2
Published online: 16 December 2017
 The Author(s) 2017. This article is an open access publication
Abstract
Background Middle- and long-distance running perfor-
mance is constrained by several important aerobic and
anaerobic parameters. The efﬁcacy of strength training
(ST) for distance runners has received considerable atten-
tion in the literature. However, to date, the results of these
studies have not been fully synthesized in a review on the
topic.
Objectives
This systematic review aimed to provide a
comprehensive critical commentary on the current litera-
ture that has examined the effects of ST modalities on the
physiological determinants and performance of middle-
and long-distance runners, and offer recommendations for
best practice.
Methods Electronic databases were searched using a
variety of key words relating to ST exercise and distance
running. This search was supplemented with citation
tracking. To be eligible for inclusion, a study was required
to meet the following criteria: participants were middle- or
long-distance runners with C 6 months experience, a ST
intervention (heavy resistance training, explosive resis-
tance training, or plyometric training) lasting C 4 weeks
was applied, a running only control group was used, data
on one or more physiological variables was reported. Two
independent assessors deemed that 24 studies fully met the
criteria for inclusion. Methodological rigor was assessed
for each study using the PEDro scale.
Results PEDro scores revealed internal validity of 4, 5, or
6 for the studies reviewed. Running economy (RE) was
measured in 20 of the studies and generally showed
improvements (2–8%) compared to a control group,
although this was not always the case. Time trial (TT)
performance (1.5–10 km) and anaerobic speed qualities
also tended to improve following ST. Other parameters
[maximal oxygen uptake ( _VO2max), velocity at
_VO2max,
blood lactate, body composition] were typically unaffected
by ST.
Conclusion Whilst there was good evidence that ST
improves RE, TT, and sprint performance, this was not a
consistent ﬁnding across all works that were reviewed.
Several important methodological differences and limita-
tions are highlighted, which may explain the discrepancies
in ﬁndings and should be considered in future investiga-
tions in this area. Importantly for the distance runner,
measures relating to body composition are not negatively
& Richard C. Blagrove
richard.blagrove@bcu.ac.uk
Glyn Howatson
glyn.howatson@nothumbria.ac.uk
Philip R. Hayes
phil.hayes@northumbria.ac.uk
1
Faculty of Health, Education and Life Sciences, School of
Health Sciences, Birmingham City University, City South
Campus, Westbourne Road, Edgbaston, Birmingham
B15 3TN, UK
2
Division of Sport, Exercise and Rehabilitation, Faculty of
Health and Life Sciences, Northumbria University,
Northumberland Building, Newcastle-upon-Tyne NE1 8ST,
UK
3
Water Research Group, Northwest University,
Potchefstroom, South Africa
123
Sports Med (2018) 48:1117–1149
https://doi.org/10.1007/s40279-017-0835-7
impacted by a ST intervention. The addition of two to three
ST sessions per week, which include a variety of ST
modalities are likely to provide beneﬁts to the performance
of middle- and long-distance runners.
Key Points
Strength training (ST) appears to provide beneﬁts to
running economy, time trial performance and
maximal sprint speed in middle- and long-distance
runners of all abilities
Maximal oxygen uptake, blood lactate parameters,
and body composition appear to be unaffected by the
addition of ST to a distance runner’s program
Adding ST, in the form of heavy resistance training,
explosive resistance training, and plyometric training
performed, on 2–3 occasions per week is likely to
positively affect performance.
1 Introduction
Distance running performance is the consequence of a
complex interaction of physiological, biomechanical, psy-
chological, environmental, and tactical factors. From a
physiological perspective, the classic model [1, 2] identi-
ﬁes three main parameters that largely inﬂuence perfor-
mance:
maximal
oxygen
uptake
( _VO2max),
running
economy (RE), and fractional utilization (sustainable per-
centage of _VO2max). Collectively, these determinants are
capable of predicting 16 km performance with more than
95% accuracy in well-trained runners [3]. The velocity
associated with _VO2max (v _VO2max) also provides a com-
posite measure of _VO2max and RE, and has been used to
explain differences in performance amongst trained dis-
tance runners [3, 4]. Whilst _VO2max values differ little in
homogenous groups of distance runners, RE displays a
high degree of interindividual variability [5, 6]. Deﬁned as
the oxygen or energy cost of sustaining a given sub-max-
imal running velocity, RE is underpinned by a variety of
anthropometric, physiological, biomechanical, and neuro-
muscular factors [7]. Traditionally, chronic periods of
running training have been used to enhance RE [8, 9];
however, novel approaches such as strength training (ST)
modalities have also been shown to elicit improvements
[10].
For middle-distance (800–3000 m) runners, cardiovas-
cular-related parameters associated with aerobic energy
production can explain a large proportion of the variance in
performance [11–17]. However a large contribution is also
derived from anaerobic sources of energy [14, 18].
Anaerobic capabilities can explain differences in physio-
logical proﬁles between middle- and longer-distance run-
ners
[14]
and
are
more
sensitive
to
discriminating
performance in groups of elite middle-distance runners
than
traditional
aerobic
parameters
[19].
Anaerobic
capacity and event-speciﬁc muscular power factors, such as
v _VO2max and the velocity achieved during a maximal
anaerobic running test (vMART) have also been proposed
as limiting factors for distance runners [12, 20, 21]. For an
800-m runner in particular, near-maximal velocities of
running are reached during the ﬁrst 200 m of the race [22],
which necessitate a high capacity of the neuromuscular and
anaerobic system.
Both RE and anaerobic factors, (i.e., speed, anaerobic
capacity and vMART) rely on the generation of rapid force
during ground contact when running [23, 24]. Programs of
ST provide an overload to the neuromuscular system,
which improves motor unit recruitment, ﬁring frequency,
musculotendinous stiffness, and intramuscular co-ordina-
tion, and therefore potentially provides distance runners
with a strategy to enhance their RE and event-speciﬁc
muscular power factors [19]. In addition, an improvement
in force-generating capacity would theoretically allow
athletes to sustain a lower percentage of maximal strength,
thereby reducing anaerobic energy contribution [25]. This
reduction in relative effort may therefore reduce RE and
blood lactate (BL) concentration. As v _VO2maxis a function
of RE, _VO2max and anaerobic power factors, it would also
be expected to show improvements following an ST
intervention. Several recent reviews in this area have pro-
vided compelling evidence that a short-term ST interven-
tion is likely to enhance RE [10, 26], in the order of * 4%
[10]. Whilst these reviews have provided valuable insight
into how ST speciﬁcally impacts RE, studies also typically
measure other important aerobic and anaerobic determi-
nants of distance running performance, which have not
previously been fully synthesized in a review. Body com-
position also appears to be an important determinant of
distance running performance, with low body mass con-
ferring an advantage [27, 28]. Resistance training (RT) is
generally associated with a hypertrophic response [29];
however, this is known to be attenuated when RT and
endurance training are performed concurrently within the
same program [30]. Changes in body composition as a
consequence of ST in distance runners have yet to be fully
addressed in reviews on this topic.
1118
R. C. Blagrove et al.
123
There are also a number of recent publications [31–38]
that have not been captured in previous reviews [10, 26] on
this topic, which potentially provide valuable additional
insight into the area. Previous papers that have reviewed
the impact of ST modalities on distance running perfor-
mance have done so alongside other endurance sports
[23, 39] or are somewhat outdated [40–42]. Furthermore,
although improvements in RE would likely confer a beneﬁt
to distance running performance, the outcomes from
studies that have used time trials have not been compre-
hensively reviewed. Performance-related outcome mea-
sures provide high levels of external validity compared to
physiological parameters, therefore it is likely that a col-
lective summary of results would be of considerable
interest to coaches and athletes.
Consequently the aim of this review was to systemati-
cally analyze the evidence surrounding the use of ST on
distance running parameters that includes both aerobic and
anaerobic qualities, in addition to body composition and
performance-related outcomes. This work also provides a
forensic, critical evaluation that, unlike previous work,
highlights areas that future investigations should address to
improve methodological rigor, such as ensuring valid
measurement of physiological parameters and maximizing
control over potential confounding factors.
2 Methods
2.1 Literature Search Strategy
The PRISMA statement [43] was used as a basis for the
procedures described herein. Electronic database searches
were carried out in Pubmed, SPORTDiscus, and Web of
Science using the following search terms and Boolean
operators: (‘‘strength training’’ OR ‘‘resistance training’’
OR ‘‘weight training’’ OR ‘‘weight lifting’’ OR ‘‘plyo-
metric training’’ OR ‘‘concurrent training’’) AND (‘‘dis-
tance running’’ OR ‘‘endurance running’’ OR ‘‘distance
runners’’ OR ‘‘endurance runners’’ OR ‘‘middle distance
runners’’) AND (‘‘anaerobic’’ OR ‘‘sprint’’ OR ‘‘speed’’
OR ‘‘performance’’ OR ‘‘time’’ OR ‘‘economy’’ OR ‘‘en-
ergy cost’’ OR ‘‘lactate’’ OR ‘‘maximal oxygen uptake’’
OR ‘‘ _VO2max’’ OR ‘‘aerobic’’ OR ‘‘time trial’’). Searches
were limited to papers published in English and from 1
January 1980 to 6 October 2017.
2.2 Inclusion and Exclusion Criteria
For a study to be eligible, each of the following inclusion
criteria were met:
•
Participants were middle- (800–3000 m) or long-dis-
tance runners (5000 m–ultra-distance). Studies using
triathletes and duathletes were also included because
often these participants possess similar physiology to
distance runners and complete similar volumes of
running training.
•
A ST intervention was applied. This was deﬁned as
heavy (less than 9 repetition maximum (RM) loads and/
or 80% of 1RM) or isometric resistance training (HRT),
moderate load (9–15 RM and/or 60–80% 1RM) RT,
explosive resistance training (ERT), reactive ST or
plyometric training (PT). Sprint training (SpT) could be
used in conjunction with one or more of the above ST
methods, but not exclusively as the only intervention
activity.
•
The intervention period lasted 4 weeks or longer. This
criteria was employed as neuromuscular adaptations
have been observed in as little as 4 weeks in non-
strength trained individuals [44, 45].
•
A running only control group was used that adopted
similar running training to the intervention group(s).
•
Data on one or more of the following physiological
parameters was reported: _VO2max, RE, velocity associ-
ated with v _VO2max, time trial (TT) performance, time to
exhaustion (TTE), BL response, anaerobic capacity,
maximal speed, measures of body composition.
•
Published in full in a peer-reviewed journal.
Studies were excluded if any of the following criteria
applied:
•
Participants were non-runners (e.g., students, untrai-
ned/less than 6 months running experience). Further
restrictions were not placed upon experience/training
status.
•
The running training and/or ST intervention was poorly
controlled and/or reported.
•
The intervention involved only SpT or was embedded
as part of running training sessions.
•
Participants were reported to be in poor health or
symptomatic.
•
Ergogenic aids were used as part of the intervention.
Using the mean _VO2max values provided within each
study, participants training status was considered as mod-
erately-trained (male _VO2max B 55 ml kg-1 min-1), well-
trained (male _VO2max 55–65 ml kg-1 min-1), or highly-
trained (male _VO2max C 65 ml kg-1 min-1) [10, 46]. For
female
participants, the
_VO2max
thresholds
were set
10 ml kg-1 min-1 lower [46]). In the absence of _VO2max
values, training status was based upon the training or
competitive
level
of
the
participants:
moderately-
trained = recreational
or
local
club,
well-
Effects of Strength Training on Distance Running
1119
123
trained = Collegiate or provincial, highly-trained = na-
tional or international.
2.3 Study Selection
Figure 1 provides a visual overview of the study selection
process. Search results were imported into a published software
for systematic reviews [47], which allowed a blind screening
process tobe performed bytwoindependent reviewers(RB and
PH). Any disagreements were resolved by consensus. The
initial search yielded 454 publications. Following the removal
of duplicates (n = 190), publications were ﬁltered by reading
the title and abstract [inter-rater reliability (IRR): 95.3%,
Cohens k = 0.86] leaving 19 review articles or commentaries,
and 47 potentially relevant papers, which were given full con-
sideration. Five additional records were identiﬁed as being
potentially relevant via manual searches of previously
published reviews on this topic and the individual study cita-
tions. These 52 studies were considered in detail for appropri-
ateness, resulting in a further 26 papers [34, 37, 48–71] being
excluded (IRR: 94.2%, Cohens k = 0.88) for the following
reasons: not published in full in a peer-reviewed journal
[50, 52, 60, 61], absence of a running only control group
[48, 49, 54, 57, 59, 62–67, 69], participants were non-runners
[51, 53, 56, 68], no physiological parameters were measured
[55], dissimilar running training was applied between groups
[71], the ST interventionwaspoorly controlled[54],andSTdid
not involve one of the aforementioned types [34, 37, 58, 70].
2.4 Analysis of Results
The Physiotherapy Evidence Database (PEDro) scale was
subsequently used to assess the quality of the remaining 26
records [31–33, 36, 38, 72–92] by the two independent
reviewers. Two studies reported their results across two
papers [32, 38, 90, 92], therefore both are considered as
single studies hereafter, thus a total of 24 studies were
analyzed. The PEDro scale is a tool recommended for
assessing the quality of evidence when systematically
reviewing randomized-controlled trials [93]. Each paper is
scrutinized against 11 items relating to the scientiﬁc rigor
of the methodology, with items 2–11 being scored 0 or 1.
Papers are therefore awarded a rating from 0 to 10
depending upon the number of items which the study
methodology satisﬁes (10 = study possesses excellent
internal validity, 0 = study has poor internal validity). No
studies were not excluded based upon their PEDro scale
score and IRR was excellent (93.2%, Cohens k = 0.86).
Results are summarized as a percentage change and the p
value for variables relating to: strength outcomes, RE,
_VO2max, v _VO2max, BL response, time trial, anaerobic per-
formance, and body composition. Due to the heterogeneity
of outcome measures in the included studies and the limi-
tations associated with conditional probability, where pos-
sible, an effect size (ES) statistic (Cohens d) is also provided.
Effect size values are based upon those reportedin the studies
or were calculated using the ratio between the change score
(post-intervention value minus pre-intervention value) and a
pooled standard deviation at baseline for intervention and
control groups. Values are interpreted as trivial\0.2; small
0.2–0.6; moderate 0.6–1.2; and large[1.2.
3 Results
3.1 Participant Characteristics
A summary of the participant characteristics for the 24
studies which met the criteria for inclusion in this review is
presented in Table 1. A total of 469 participants (male
Fig. 1 Search, screening and selection process for suitable studies
1120
R. C. Blagrove et al.
123
Table 1 Participant characteristics and design of each study
Study
Participant characteristics
Study design
n (I/C)
Sex
Age (years)
_VO2max
(mL kg-1 min-1)
Training background (event
specialism)
Duration
(weeks)
Randomized?
Running
controlled?
ST added or
replace
running?
PEDro
score
Albracht &
Arampatzis
[84]
26 (13/13)
M
I = 27,
C = 25
–
Recreational (C 3 runs wk-1,
30–120 km wk-1)
14
No
No
Added
5
Beattie et al.
[33]
20 (11/9)
M = 19
F = 1
I = 29.5,
C = 27.4
I = 59.6,
C = 63.2
Collegiate and national level
(1500 m–10 km)
40
No
No
Added
4
Berryman
et al. [80]
28 (HRT
n = 12, PT
n = 11, C
n = 5)
M
HRT = 31,
PT = 29,
C = 29
HRT = 57.5,
PT = 57.5,
C = 55.7
3–7 runs wk-1. Provincial
level (5 km–marathon)
8
Yes
Yes
Added
5
Bertuzzi et al.
[85]
22 (RTWBV
n = 8, RT
n = 8, C
n = 6)
M
RTWBV = 34,
RT = 31,
C = 33
RTWBV = 56.3,
RT = 57.4,
C = 56.1
Local 10 km (35–45 min)
race competitors
6
Yes
No
(monitored)
Added
6
Bonacci et al.
[83]
8 (3/5)
M = 5
F = 3
21.6
–
Moderately-trained triathletes
(34.8 km wk-1)
8
Yes
No
(monitored)
Added
5
Damasceno
et al. [89]
18 (9/9)
M
I = 34.1,
C = 32.9
I = 54.3,
C = 55.8
Local 10 km (35–45 min)
race competitors
8
Yes
No
(monitored)
Added
6
Ferrauti et al.
[81]
20 (11/9)
M = 14
F = 6
40.0
I = 52.0,
C = 51.1
Experienced (8.7 years)
recreational (4.6 h wk-1)
8
Yes
No
(monitored)
Added
6
Fletcher et al.
[82]
12 (6/6)
M
I = 22.2,
C = 26.3
I = 67.3,
C = 67.6
Regional/national/
international level (1500 m–
marathon)
8
Yes
No
Added
6
Giovanelli
et al. [36]
25 (13/12)
M
I = 36.3,
C = 40.3
I = 55.2,
C = 55.6
Experienced
(11.7 years,[60 km wk-1)
ultra-distance competitors
12
Yes
No
(monitored)
Added
6
Johnston
et al. [72]
12 (6/6)
F
30.3
I = 50.5,
C = 51.5
[1 year experience, 20–30
miles wk-1, 4–5 days wk-1
10
Yes
No
(monitored)
Added
6
Karsten et al.
[31]
16 (8/8)
M = 11F = 5
I = 39,
C = 30
I = 47.3,
C = 47.0
Recreational triathletes
([2 years, 3–5 days wk-1,
180–300 min wk-1)
6
Yes
No
Added
6
Mikkola et al.
[78]
25 (13/12)
M = 18
F = 7
I = 17.3,
C = 17.3
I = 62.4,
C = 61.8
High-school runners
([2 years)
8
No
No
(monitored)
Replace (I:
19%, C: 4%)
4
Millet et al.
[74]
15 (7/8)
M
I = 24.3,
C = 21.4
I = 69.7,
C = 67.6
Experienced (6.8 years)
triathletes (n = 7 national/
international)
14
Yes
No
(monitored)
Added
6
Effects of Strength Training on Distance Running
1121
123
Table 1 continued
Study
Participant characteristics
Study design
n (I/C)
Sex
Age (years)
_VO2max
(mL kg-1 min-1)
Training background (event
specialism)
Duration
(weeks)
Randomized?
Running
controlled?
ST added or
replace
running?
PEDro
score
Paavolainen
et al. [73]
18 (10/8)
M
I = 23,
C = 24
I = 63.7,
C = 65.1
Experienced (8 years) cross-
country runners
(545 h year-1)
9
Unclear
(matched on
_VO2max and
5 km)
Yes
Replace (I:
32%, C: 3%)
4
Pellegrino
et al. [91]
22 (11/11)
M = 14
F = 8
I = 34.2,
C = 32.5
I = 48.0,
C = 47.7
Experienced recreational
(local clubs and races)
6
Yes
No
Added
6
Piacentini
et al. [86]
16 (HRT
n = 6, RT
n = 5, C
n = 5)
M = 6
F = 4
HRT = 44.2
RT = 44.8
C = 43.2
–
Local ([5 years,
4–5 days wk-1) masters
runners (10 km – marathon)
6
Yes
No
Added
4
Ramı´rez-
Campillo
et al. [87]
32 (17/15)
M = 9
F = 13
22.1
–
National/international
competitive level (1500 m –
marathon)
6
Yes
No
(monitored)
Added
6
Saunders
et al. [77]
15 (7/8)
M
I = 23.4,
C = 24.9
I = 67.7,
C = 70.4
National/international
competitive level (3 km)
9
Yes
No
(monitored)
Added (but C
matched
with
stretching/
CS)
6
Schumann
et al.
[90, 92]
27 (13/14)
M
33
–
Recreational
([12 months; C 2
runs wk-1)
24
Unclear
(matched by
performance)
Yes
Added
5
Skovgaard
et al. [88]
21 (12/9)
M
31.1
59.4
Experienced (7.5 years)
recreational (29.7 km wk-1,
3.3 runs wk-1)
8
Yes
Yes (I only)
Replace (I:
42%)
6
Spurrs et al.
[75]
17 (8/9)
M
25
I = 57.6,
C = 57.8
Experienced (10 years);
60–80 km wk-1
6
Yes
No
(monitored)
Added
6
Støren et al.
[79]
17 (8/9)
M = 9
F = 8
I = 28.6,
C = 29.7
I = 61.4,
C = 56.5
Well-trained (5 km:
M = 18.42, F = 19.23)
8
Yes
No
(monitored)
Added
6
Turner et al.
[76]
18 (10/8)
M = 8
F = 10
I = 31,
C = 27
I = 50.4,
C = 54.0
Basic training
([6 months; C 3
runs wk-1)
6
Yes
No
(monitored)
Added
6
Vikmoen
et al.
[32, 38]
19 (11/8)
F
I = 31.5,
C = 34.9
53.3
Well-trained (duathletes)
11
Yes
Yes
Added
5
C control group, CS core stability, F female, h hours, HRT heavy resistance training, I intervention group, M male, PT plyometric training, RT resistance training, RTWBV resistance training with
whole body vibration, _VO2max maximal oxygen uptake, wk week
1122
R. C. Blagrove et al.
123
n = 352, female n = 96) are included, aged between 17.3
and 44.8 years. Maximal oxygen uptake data was reported
for all but ﬁve studies [83, 84, 86, 87, 90, 92] and ranged
from 47.0 to 70.4 mL kg-1 min-1. Based upon weighted
mean values in the studies that reported participant char-
acteristics for each group, age (30.2 vs. 29.0 years), body
mass (68.1 vs. 70.0 kg), height (1.74 vs. 1.74 m), and
_VO2max (57.3 vs. 57.7 mL kg-1 min-1) appeared to differ
little at baseline for ST groups and control groups respec-
tively. Moderately trained or recreational level runners
were used in nine studies [31, 72, 76, 81, 83, 84, 86,
90–92], well-trained participants in ten studies [32, 33,
36, 38, 73, 75, 79, 80, 85, 88, 89], and highly-trained or
national/international runners were used in four studies
[74, 77, 82, 87]. National caliber junior runners were also
used in one investigation [78]. Participants took part or
competed in events ranging from the middle-distances to
ultra-marathons,
and
several
studies
used
triathletes
[31, 74, 83] or duathletes [32, 38].
3.2 Study Design and PEDro Scores
Table 1 also provides an overview of several important
features of study design, including PEDro scale scores.
Studies lasted 6–14 weeks with the exception of two
investigations, which lasted 24 [90, 92] and 40 weeks [33].
Fourteen studies provided detailed accounts of the running
training undertaken by the participants. However, these
were usually reported from monitoring records, thus only
three studies were deemed to have appropriately controlled
for the volume and intensity of running in both groups
[32, 38, 73, 80, 90, 92]. Six studies provided little or no
detail on the running training that participants performed
[31, 33, 82, 84, 86, 91]. Strength training in all but three
investigations [73, 78, 88] was supplementary to running
training, and one paper provided the control group with
alternative activities (stretching and core stability) matched
for training time [77].
Studies were all scored a 4, 5, or 6 on the PEDro scale.
All investigations had points deducted for items relating to
blinding of participants, therapists, and assessors. Differ-
ences in the scores awarded were mainly the result of
studies not randomly allocating participants to groups and
failing to obtain data for more than 85% of participants
initially allocated to groups; or this information not being
explicitly stated.
3.3 Training Programs
Table 2 provides a summary of the training characteristics
associated with the ST intervention and running training
used concurrently as part of the study period. The ST
activities used were RT or HRT [31, 32, 38, 72,
78, 79, 81, 82, 84–86, 89], PT [75, 76, 80, 87, 91], ERT
[80], or a combination of these methods [33, 36, 77,
83, 90, 92], which in some cases also included SpT
[73, 74, 88].
All studies utilized at least one multi-joint, closed
kinetic chain exercise with the exception of two studies that
used isometric contractions on the ankle plantarﬂexors
[82, 84]. One study employed only resistance machine
exercises for lower limb HRT [81], whereas all other
studies used free weights, bodyweight resistance or a
combination of machines and free weights. Strength
training (using lower limb musculature) was scheduled
once [33, 80, 81], twice [31–33, 38, 75, 78, 85–87,
89, 90, 92], three times [36, 72, 74–77, 79, 82, 83, 88], or
four times [84] per week. One study used 15 sessions over
a 6-week period [91] and one study reported 2.7 h of ST
activity per week [73].
Heavy RT was typically prescribed in 2–6 sets of 3–10
repetitions per exercise at relatively heavy loads (higher
than 70% 1RM or to repetition failure). Plyometric training
prescription consisted of 1–6 exercises performed over 1–6
sets of 4–10 repetitions, totaling 30–228 foot contacts per
session. Most studies applied the principle of progressive
overload and some authors reported periodized models for
the intervention period [32, 33, 36, 38, 77, 88, 89]. Studies
which included SpT tended to utilize short distances
(20–150 m),
over
4–12
sets
at
maximal
intensity
[73, 74, 88]. Strength training was supervised or part-su-
pervised across all studies with the exception of three, one
that was unsupervised [76] and two where it was unclear
from the report [73, 74].
Running
training
varied
considerably
(16–170 km
week-1, 3–9 sessions week-1) across the studies, with
various levels of detail provided regarding weekly volume
and intensity. Importantly, all studies that added ST
reported that running training did not differ between
groups.
3.4 Strength Outcomes
All but two studies [31, 83] measured at least one strength-
related parameter (Table 3). Across all studies that used
1RM testing [33, 72, 74, 78, 79, 85, 86, 88–90, 92], the
intervention produced a statistically signiﬁcant improve-
ment (4–33%, ES: 0.7–2.4). Maximal voluntary contraction
(MVC) was also used to assess strength capacity in seven
papers, with the majority reporting improved (7–34%, ES:
0.38–1.65) scores following ST [73, 75, 78, 81, 84] but
others reporting no difference compared to a control group
[81, 82, 90, 92]. Performance on a jump test was shown to
improve
(3–9%,
ES:
0.25–0.65)
in
some
studies
[32, 73, 74, 80, 87]; however, other studies showed no
Effects of Strength Training on Distance Running
1123
123
Table 2 Intervention and running training variables
Study
Intervention
type
Main exercises
Frequency
Volume per session
Intensity
ST
supervised?
Recovery between
sessions
Running training
Albracht &
Arampatzis
[84]
HRT
(isometric)
Ankle plantarﬂexion
(5 dorsiﬂexion,
knee extended, 40
hip ﬂexion)
4 per week
4 sets 9 4 reps (3 s loading,
3 s relaxation)
90% MVC
(adjusted
weekly)
Yes
–
I: 66 km wk-1
C: 62 km wk-1
Beattie et al.
[33]
HRT/ERT/
PT
PT: pogo jumps,
depth jumps, CMJ
HRT: back squat,
RDL, lunge
ERT: jump squats
Wk 1–20: 2
per week;
Wk 21–40:
1 per week
9–12 sets (2–3 sets per
exercise); PT: 4–5 reps,
HRT: 3–8 reps, ERT: 3
reps
Load progressed
when
competent
Yes
C48 h between
sessions (wk
1–20). Separate
session to
running
Not reported (usual running
training)
Berryman
et al. [80]
ERT and PT
ERT: concentric
squats
PT: DJ
1 per week
ERT and PT: 3–6 sets 9 8
reps
ERT:[95% PPO
PT: 20–60 cm so
rebound[95%
CMJ
Yes
–
2 9 AIT (1 9 peak speed,
1 9 80% peak speed)
1 9 LSD (30–60 min)
Bertuzzi et al.
[85]
RT and
RTWBV
Half-squats
2 per week
3–6 sets 9 4–10 reps
periodized
70–100% 1RM
over 12 wk
Yes
Different days to
runs
57–61 km wk-1
Bonacci et al.
[83]
PT/ERT
PT: CMJ, knee lifts,
ankle jumps,
bounds, skips,
hurdle jumps
ERT: Squat jumps,
back ext.,
hamstring curls
3 per week
PT: 1–5 sets 9 5–10 reps or
20–30 m
RT: 2–5 sets 9 8–15 reps
Max height/fast
velocity
Yes
–
Same as previous 3 months. I:
swim (7.3 km), cycle
(137.6 km), run (34.8 km)
C: swim (10.1 km), cycle
(147.5 km), run (29.0 km)
Damasceno
et al. [89]
HRT
Half-squat, leg press,
calf raise, knee ext
2 per week
2–3 sets 9 3–10 reps
10RM periodized
to 3RM
Yes
72 h between
HRT sessions.
Different days to
runs
36–41 km wk-1 @50–70%
_VO2max
Ferrauti et al.
[81]
HRT
Machines: leg press,
knee ext., knee
ﬂexion, hip ext.,
ankle ext.; UB
exercises
1 per week
LB; 1 per
week UB
LB: 4 sets 9 3–5 reps
3–5 RM
Yes
–
I: 240 min wk-1, C:
276 min wk-1
Fletcher et al.
[82]
HRT
(isometric)
Plantarﬂexions
3 per week
4 sets 9 20 s
80% MVC
Yes
–
70–170 km wk-1
1124
R. C. Blagrove et al.
123
Table 2 continued
Study
Intervention
type
Main exercises
Frequency
Volume per session
Intensity
ST
supervised?
Recovery between
sessions
Running training
Giovanelli
et al. [36]
CS/RT
(4wk)
HRT/ERT/
PT (8wk)
CS: 6 exercises (e.g.,
planks)
RT/HRT: single leg
half-squat, step-up,
lunges
ERT: CMJ, split
squat
PT: jump rope, high
knees
3 per week
5–8 exercises, 1–3
sets 9 6–15 reps (30 s rest)
–
Partly (only
wk 1 and
2)
C48 h between
sessions. Not
day after races/
AIT
I: normal running training
C: 70–140 km wk-1, 5–7
sessions wk-1
Johnston
et al. [72]
HRT
Squats, lunge, heel
raises (straight- and
bent-leg), knee
ext./ﬂexion, 8xUB
exercises
3 per week
3 sets 9 6 reps squat and
lunge; 2 sets 9 20/12 reps
bent–/straight–leg heel
raise; 3 sets 9 8 reps knee
ext./ﬂexion
RM each set
Yes
C48 h between
HRT
sessions. C 5 h
between HRT
and running
sessions.
4–5 days wk-1,
32–48 km wk-1
Karsten et al.
[31]
HRT
RDL, squat, calf
raises, lunges
2 per week
4 sets 9 4 reps
80% 1RM
Yes
C48 h between
HRT sessions.
3–5 sessions/
180–300 min wk-1
Mikkola et al.
[78]
HRT
Hamstring curl, leg
press, seated press,
squat, leg ext., heel
raise
2 per week
3–5 sets 9 3–5 reps
[90% 1RM
(reassessed
every 3 wk)
Yes
Separate session
to running
Total: I = 7 h wk-1,
C = 6.6 h wk-1;
Running: I = 48 km wk-1,
C = 44 km wk-1
Millet et al.
[74]
SpT/PT/
ERT
PT: alternative, calf,
squat, hurdle jumps
ERT: Squat, calf
raise, hurdle jump,
leg ext./curl
3 per week
(each
intervention
type once)
SpT: 5–10 sets 9 30–150 m
PT/ERT: 2–3 sets 9 6–10
reps
PT: BW
ERT: low load,
high velocity
Unclear
–
I: 8.8 h wk-1,
C: 8.5 h wk-1
Paavolainen
et al. [73]
SpT/PT/
ERT
PT: alternative, drop
and hurdle jumps,
CMJ, hops
ERT: leg press, knee
ext. and ﬂexion
Not reported;
2.7 h per
week
SpT: 5–10 sets 9 20–100 m
PT/ERT: 5–20 reps.set-1/
30–200 reps.session-1
PT: BW or
barbell
ERT: 0–40%
1RM
Unclear
–
I: 8.4 h wk-1
(9 sessions) C: 9.2 h wk-1 (8
sessions)
Pellegrino
et al. [91]
PT
Modiﬁed version of
Spurrs et al.
(jumps, bounds,
hops)
15 sessions
total
60–228 foot contacts
Progressively
increased
Yes
–
I: 34.4–36.2 km wk-1
C: 29.5–31.3 km wk-1
Piacentini
et al. [86]
HRT and RT
Squat, calf press,
lunges, eccentric
quad, calf raise, leg
press ? UB
exercises
2 per week
HRT: 4 sets 9 3–4 reps
RT: 3 sets 9 10 reps
HRT: 85–90%
1RM
RT: 70% 1RM
Yes
–
4–5 days wk-1, 50 km wk-1
Effects of Strength Training on Distance Running
1125
123
Table 2 continued
Study
Intervention
type
Main exercises
Frequency
Volume per session
Intensity
ST
supervised?
Recovery between
sessions
Running training
Ramı´rez-
Campillo
et al. [87]
PT
DJ
2 per week
60 contacts (6 sets 9 10
reps)
20 reps @20 cm,
20 reps
@40 cm, 20
reps @60 cm
Yes
C48 h between
PT sessions.
Performed
before runs.
I: 64.7 km.wk-1
C: 70.0 km.wk-1 (AIT
preferred)
Saunders
et al. [77]
PT/HRT
PT: CMJ, ankle
jumps, bounds,
skips, hurdle
jumps, scissor
jumps
HRT: back ext., leg
press, hamstring
curls
3 per week
PT: Progress from 1 to 6
sets 9 6–10 reps/10–30 m
HRT: 1–5 sets 9 6–10 reps
(except back ext.)
PT: fast GCT
HRT: Leg press
60% 1RM
Yes
–
107 km.wk-1 (3x AIT,
1 9 LSD 60–150 min,
3 9 LSD 30–60 min,
3–6 9 LSD 20–40 min)
Schumann
et al.
[90, 92]
HRT/ERT/
PT
HRT: leg press, knee
ﬂexion, calf raise
?UB/core
exercises
ERT: Squat jumps,
step-ups
PT: Drop jumps,
hurdle jumps
2 per week
HRT (wk 5–24): 5–12 reps
per set
HRT (wk 5–24):
60–85% 1RM
ERT: 20–30%
1RM
Yes
Same session as
running.
[48 h between
sessions
Weekly: 2x run (35–45 min/
65–85% HRmax), 2 9 LSD
(35–40 min & 70–125 min/
60–65% HRmax),
1–2 9 AIT and HIIT
Skovgaard
et al. [88]
SpT/HRT
HRT: squat, deadlift,
leg press
SpT 9 2 per
week
HRT 9 1 per
week
SpT: 4–12 sets 9 30 s
(3 min rest)
HRT: 3–4 sets 9 6–8 reps
wk 1–4; 4 sets 9 4 reps wk
5–8
SpT: maximal
effort
HRT: 15RM to
8RM wk 1–4;
4RM wk 5–8
Yes
3–4 d between
SpT/HRT
sessions.
Different days to
runs
I: AIT (4 9 4 ? 2 min @85%
HRmax); 50 min @75–85%
HRmax
C: 40 km total (4 km AIT)
Spurrs et al.
[75]
PT
Jumps, bounds, hops
2–3 per week
60–180 foot contacts
Bilateral
progressed to
unilateral and
greater height
Yes
Separate session
to running
60–80 km per week
Støren et al.
[79]
HRT
Half-squats
3 per week
4 sets 9 4 reps
4RM
Yes
–
I: 253 min wk-1 (? 119 min
other ET)
C: 154 min wk-1 (?120 min
other ET)
Turner et al.
[76]
PT
Vertical jumps and
hops (continuous
and intermittent),
split jumps, uphill
jumps
3 per week
40–110 foot contacts (5–30 s
per exercise)
Bodyweight,
short contact
time
No
(logbooks)
Performed in
running sessions
Continued regular running
(C 3 runs wk-1, C 10
miles wk-1)
1126
R. C. Blagrove et al.
123
change compared to a control group [33, 76–78, 90–92]
and in one study the control group improved to a greater
extent than the intervention group [86]. Changes in an
ability to produce force rapidly also showed mixed results,
with some studies showing improvements in peak power
output [80] and rate of force development [78, 79] and
others showing no change in these parameters [36, 75, 77].
Similarly, stiffness, when measured directly or indirectly
(using reactive strength index) during non-running tasks,
has been shown to improve (ES: 0.43–0.90) [75, 84, 86, 87]
and remain unchanged [33, 74, 89] following ST. Vertical
or leg stiffness during running showed improvements
(10%, ES: 0.33) at relatively slow speeds [36] and also at
3 km race pace (ES: 1.2) following ST [74].
3.5 Running Economy
An assessment of RE was included in all but four
[31, 85, 87, 90, 92] of the studies in this review (Table 3).
Running economy was quantiﬁed as the oxygen cost of
running at a given speed in every case, except in three
studies where a calculation of energy cost was used
[82, 84, 91]. Statistically signiﬁcant improvements (2–8%,
ES: 0.14–3.22) in RE were observed for at least one speed
in 14 papers. A single measure of RE was reported in four
of these papers [31, 79, 80, 88], and a further four studies
assessed RE across multiple different speeds and found
improvements across all measures taken [72, 74, 75, 84].
Six papers reported a mixture of signiﬁcant and non-sig-
niﬁcant results from the intensities they used to evaluate
RE [36, 73, 76–78, 86]. Six studies failed to show any
signiﬁcant improvements in RE compared to a control
group [32, 81–83, 89, 91].
3.6 Maximal Oxygen Uptake
No statistically signiﬁcant changes were reported in
_VO2max or _VO2peak for any group in the majority of studies
that assessed this parameter [31, 32, 36, 72, 74, 75,
77–80, 85, 88, 89]. Three papers observed improvements
for _VO2max in the intervention group, but the change in
score did not differ signiﬁcantly from that of the control
group [33, 81, 91]. One study detected a signiﬁcant
improvement (4.9%) in
_VO2max for the control group
compared to the intervention group [73].
3.7 Velocity Associated with _VO2max
Nine studies provided data on v _VO2max or a similar metric
[31–33, 36, 74, 78, 80, 85, 89]. Just two of these papers
reported statistically signiﬁcant improvements (3–4%, ES:
0.42–0.49) in the ST group compared to the control group
Table 2 continued
Study
Intervention
type
Main exercises
Frequency
Volume per session
Intensity
ST
supervised?
Recovery between
sessions
Running training
Vikmoen
et al.
[32, 38]
HRT
Machines: Half-
squats, unilateral
leg press, cable hip
ﬂexion, calf raises
2 per week
3 sets 9 4–10 reps
(periodized 3wk cycles)
Sets performed to
RM failure
Partly (1
session
per wk
3–11)
HRT ﬁrst session
or performed on
different days
4.3 sessions wk-1; 3.7 h
@60–82% HRmax, 1.1 h
@83–87% HRmax, 0.8 h
@[87% HRmax
AIT aerobic interval training, BW body weight, CMJ counter-movement jump, C control group, CS core stability, DJ drop jump, ERT explosive resistance training, ET endurance training (e.g.,
cycling, swimming, roller skiing), GCT ground contact time, h hours, HIIT high-intensity interval training, HRmax maximum heart rate (predicted from 220-age), HRT heavy resistance training,
I intervention group, LB lower body, LSD long slow distance run, MVC maximum voluntary contraction, PPO peak power output, PT plyometric training, RDL Romanian deadlift, RM
repetition maximum, RT resistance training, SpT sprint training, ST strength training, UB upper body, RTWBV resistance training with whole body vibration
Effects of Strength Training on Distance Running
1127
123
Table 3 Outcomes of the studies. Percentage changes, effect size (ES) and p value only reported for statistically signiﬁcant group results or ES[0.2. All results presented are for the
intervention (I) group unless stated (e.g., C = control). Variables measured where no-signiﬁcance (NS) difference for time (pre- vs. post-score) and no group 9 time (G 9 T) interaction was
detected, are also listed
Study
Main strength outcomes
Economy
_VO2max= _VO2peak
v _VO2max
Blood lactate
Time trial
Anaerobic
measures
Body composition
Albracht and
Arampatzis
[84]
Plantarﬂexion MVC
(6.7%, ES = 0.56,
p = 0.004), max
Achilles tendon force
(7.0%, ES = 0.55,
p\0.01), Tendon
stiffness (15.8%,
ES = 0.90, p\0.001)
_VO2@10.8 km h-1 (5.0%,
ES = 0.79)
@12.6 km h-1 (3.4%,
ES = 0.51)
EC@10.8 km h-1 (4.6%,
ES = 0.61)
@12.6 km h-1 (3.5%,
ES = 0.50), all p\0.05
–
–
BL@10.8 and
12.6 km h-1, NS
–
–
Body mass, NS
Beattie et al.
[33]
1RM back squat (wk
0–20: 19.3%,
ES = 1.2, p = 0.001)
DJRSI (wk 0–20: 7.3%,
ES = 0.3, NS G 9 T;
wk 0–40: 14.6%,
ES = 0.5, NS G 9 T)
CMJ (wk 0–20: 11.5%,
ES = 0.5, NS G 9 T;
wk 0–40: 11.5%,
ES = 0.6, NS G 9 T)
Ave. of 5 speeds
Wk 0–20: 5.0%, ES = 1.0,
p = 0.01.
Wk 0–40: 3.5%, ES = 0.6,
NS.
Wk 0–20: 0.1%,
ES = 0.1,
p = 0.013.
Wk 0-40, I:
7.4%,
ES = 0.5,
p = 0.003, C:
2.8%,
ES = 0.6, NS
Wk 0-20:
3.5%,
ES = 0.7,
NS.
Wk 0-40:
4.0%,
ES = 0.9,
NS
v2 mmol L-1,
v4 mmol L-1, NS
–
–
Body mass, fat and
lean muscle, NS
Berryman
et al. [80]
PPO (ERT: 15.4%,
ES = 0.98, p\0.01;
PT: 3.4%, ES = 0.24,
p\0.01).
CMJ (ERT: 4.5%,
ES = 0.25, p\0.01;
PT: 6.0%, ES = 0.52,
p\0.01)
@12 km h-1
ERT: 4%, ES = 0.62,
p\0.01.
PT: 7%, ES = 1.01,
p\0.01
NS
ERT: 4.2%,
ES = 0.43,
p\0.01.
PT: 4.2%,
ES = 0.49,
p\0.01
–
3 km TT
ERT: 4.1%,
ES = 0.37.
PT: 4.8%,
ES = 0.46.
C: 3.0%,
ES = 0.20;
all p\0.05,
G 9 T NS
–
Body mass, NS
Bertuzzi et al.
[85]
1RM half squat (RT:
17%, p B 0.05;
RTWBV: 18%,
p B 0.05)
–
NS
NS
–
–
–
–
Bonacci et al.
[83]
–
@12 km h-1 (after 45 min
AIT cycle) NS
–
–
–
–
Body mass, skinfolds,
thigh and calf girth,
NS
1128
R. C. Blagrove et al.
123
Table 3 continued
Study
Main strength outcomes
Economy
_VO2max= _VO2peak
v _VO2max
Blood lactate
Time trial
Anaerobic
measures
Body composition
Damasceno
et al. [89]
1RM half–squat (23%,
ES = 1.41, p\0.05),
DJRSI, wingate test NS
@12 km h-1 NS
NS
v _VO2max
(2.9%,
ES = 0.42,
p\0.05)
–
10 km TT
(2.5%,
p = 0.039),
increased
speed in
ﬁnal 7 laps
(p\0.05)
30 s Wingate
test, NS
Body mass and
skinfold, NS
Ferrauti et al.
[81]
Leg extension MVC
(33.9%, ES = 1.65,
p\0.001); leg ﬂexion
MVC (9.4%,
ES = 0.38, NS)
@LT (ES = 0.40, p\0.05,
NS G 9 T)
@8.6 and 10.1 km h-1, NS
FU@10.1 km h-1
(ES = 0.61, p = 0.05 G
9 T)
5.6%,
ES = 0.40,
NS G 9 T
–
BL@10.1 km h-1 (I:
13.1%, C: 12.1%,
NS G 9 T).
v4 mmol L-1 (I:
4.2%, C: 2.6%, NS
G 9 T).
–
–
Body mass, NS
Fletcher et al.
[82]
Isometric MVC (I:
21.6%, C: 13.4%), NS
G 9 T
EC@75,85,95% sLT, NS
–
–
BL@ 75,85,95% sLT,
NS.
–
–
–
Giovanelli
et al. [36]
SJ PPO, NS
kleg@10 km h-1, (9.5%,
ES = 0.33,
p = 0.034),
@12 km h-1 (10.1%,
ES = 0.33,
p = 0.038).
kvert
@8,10,12,14 km h-1,
NS
@8 km h-1 (6.5%,
ES = 0.43, p = 0.005),
@10 km h-1 (3.5%,
ES = 0.48, p = 0.032),
@12 km h-1 (4.0%,
ES = 0.34, p = 0.020),
@14 km h-1 (3.2%,
ES = 0.35, p = 0.022),
@RCP NS
NS
NS
–
–
–
Body mass, FFM, fat
mass, NS
Johnston
et al. [72]
1RM squat (40%,
p\0.05), knee ﬂexion
(27%, p\0.05)
@12.8 km h-1 (4.1%,
ES = 1.76, p\0.05),
@13.8 km h-1 (3.8%,
ES = 1.61, p\0.05)
NS
–
–
–
–
Body mass, fat mass,
FFM, limb girth,
NS
Karsten et al.
[31]
–
–
NS
NS
–
5 km TT
(3.5%,
ES = 1.06,
p = 0.002)
ARD, NS
–
Effects of Strength Training on Distance Running
1129
123
Table 3 continued
Study
Main strength outcomes
Economy
_VO2max= _VO2peak
v _VO2max
Blood lactate
Time trial
Anaerobic
measures
Body composition
Mikkola et al.
[78]
MVC (8%), 1RM (4%),
RFD (31%) on leg
press; all p\0.05.
CMJ and 5–bounds, NS
@14 km h-1 (2.7%,
ES = 0.32, p\0.05),
@10,12,13 km h-1, NS
NS
NS
BL@12 km h-1
(12%, p\0.05),
@14 km h-1 (11%,
p\0.05)
–
vMART
(3.0%,
p\0.01),
v30 m sprint
(1.1%,
p\0.01)
Body mass (2%,
ES = 0.32,
p\0.01).
Thickness of QF (I:
3.9%, ES = 0.35,
p\0.01; C: 1.9%,
ES = 0.10,
p\0.05); fat %,
lean mass, NS
Millet et al.
[74]
1RM half–squat (25%,
p\0.01), 1RM heel
raise (17%, p\0.01),
hop height (3.3%,
p\0.05)
kleg@3 km pace
(ES = 1.2, p\0.05)
GCT, hop stiffness, NS
@75% v _VO2max (7.4%,
ES = 1.14, p\0.05)
@ * 92% _VO2max (5.9%,
ES = 1.15, p\0.05)
NS
2.6%,
ES = 0.57,
p\0.01,
NS G 9 T
–
–
–
Body mass, NS
Paavolainen
et al. [73]
MVC knee extension
(7.1%, p\0.01), 5BJ
(4.6%, p\0.01)
@15 km h-1 (8.1%,
ES = 3.22, p\0.001)
@13.2 km h-1, NS
_VO2@LT, NS
C: (4.9%,
p\0.05)
_VO2max
demand
(3.7%,
p\0.05, NS
G 9 T)
–
–
5 km TT
(3.1%,
p\0.05)
v20 m (3.4%,
ES = 0.77,
p\0.01)
vMART
(ES = 1.98,
p\0.001)
Body mass, fat %,
calf and thigh girth,
NS
Pellegrino
et al. [91]
CMJ (5.2%, p = 0.045,
NS G 9 T)
@10.6 km h-1 (1.3%,
p\0.05 group) NS G 9
T @7.7, 9.2, 12.1, 13.5,
15.0, 16.4 km h-1, NS.
5.2%,
ES = 0.49,
p = 0.03, NS
G 9 T
–
sLT, NS
3 km TT
(2.6%,
ES = 0.20,
p = 0.04)
–
–
Piacentini
et al. [86]
1RM leg press (HRT:
17%, ES = 0.69,
p\0.05), CMJ (C: 7%,
ES = 0.63, p\0.05),
SJ (C: 13%,
ES = 0.83, p\0.01),
Stiffness (RT: 13%,
ES = 0.64, p\0.05)
@10.75 km h-1/marathon
pace (HRT: 6.2%,
p\0.05).
@9.75,11.75 km h-1, NS
–
–
–
–
–
Body mass, fat mass,
FFM, RMR, NS
Ramı´rez-
Campillo
et al. [87]
CMJ (8.9%, ES = 0.51,
p\0.01), DJ @20 cm
(12.7%, ES = 0.43,
p\0.01), DJ @40 cm
(16.7%, ES = 0.6,
p\0.05)
–
–
–
–
2.4 km TT
(3.9%,
ES = 0.4,
p\0.05)
20 m sprint
(2.3%,
ES = 0.3,
p\0.01)
Body mass, NS
1130
R. C. Blagrove et al.
123
Table 3 continued
Study
Main strength outcomes
Economy
_VO2max= _VO2peak
v _VO2max
Blood lactate
Time trial
Anaerobic
measures
Body composition
Saunders
et al. [77]
SJ RFD and peak force,
NS.
5CMJ, NS
@18 km h-1 (4.1%,
ES = 0.35, p\0.05)
@14,16 km h-1, NS
NS
–
BL
@14,16,18 km h-1,
NS
–
–
Body mass, NS
Schumann
et al.
[90, 92]
1RM leg press (I: NS, C:
–4.7%, p = 0.011),
MVC leg ﬂexion (–
9.7%, p = 0.031,
ES = 0.96, NS G 9
T), MVC leg press NS,
MVC knee ext. NS,
CMJ NS
–
–
–
BL during 6 9 1 km
(I: NS, C:, 21%, NS
G 9 T)
v4 mmol L-1 (I: 6%,
C: 8%, NS G 9 T).
1 km TT after
5x 1 km,
60 s rec. (I:
9%, C: 13%,
NS G 9 T)
–
Body mass, NS;
CSA vastus lateralis
(group diff. I: 7%,
C: -6%, NS G 9 T);
Total and leg lean
mass (I: 2%, NS G
9 T)
Skovgaard
et al. [88]
1RM squat (wk 4: 3.8%,
wk 8: 12%, p\0.001);
1RM leg press (wk 4:
8%, p\0.05; wk 8:
18%, p\0.001), 5RM
deadlift (wk 4: 14%,
wk8: 22%, p\0.001)
@12 km h-1 (wk 8: 3.1%,
ES = 1.53, p\0.01)
NS
–
–
10 km TT
(wk 4:
3.8%,
ES = 1.50,
p\0.05)
1500 m TT
(wk 8:
5.5%,
ES = 0.67,
p\0.001)
–
Body mass, NS
Spurrs et al.
[75]
MTS @75% MVC (left:
14.9%, right: 10.9%,
p\0.05), Calf MVC
(left: 11.4%, right:
13.6%, p\0.05).
RFD NS
@12 km h-1 (6.7%,
ES = 0.45), 14 km h-1
(6.4%, ES = 0.45),
16 km h-1 (4.1%,
ES = 0.30), all p\0.01
NS
–
–
3 km TT
(2.7%,
ES = 0.13,
p\0.05, NS
G 9 T)
–
Body mass, NS
Støren et al.
[79]
1RM (33.2%, p\0.01)
and RFD (26%,
p\0.01) half–squat
@70% _VO2max (5%,
ES = 1.03, p\0.01)
NS
–
sLT, LT % _VO2max,
NS
–
–
Body mass, NS
Turner et al.
[76]
CMJ and SJ, NS
Ave. of 3 speeds: M = 9.6,
11.3, 12.9, F = 8.0, 9.6,
11.3 km h-1 (2–3%,
p B 0.05)
@9.6 km h-1, NS
–
–
–
–
–
–
Effects of Strength Training on Distance Running
1131
123
[80, 89]. One study [74] reported a 2.6% improvement (ES:
0.57) and another [33] a 4.0% increase (ES: 0.9) after a
40-week intervention; however, these changes were not
signiﬁcantly different to the control group.
3.8 Blood Lactate Parameters
Blood lactate value was measured at ﬁxed velocities in six
studies [77, 78, 81, 82, 84, 92] and velocity assessed for
ﬁxed concentrations of BL (2–4 mmol L-1) or lactate
threshold (LT) in six studies [32, 33, 79, 81, 90, 91]. One
study using young participants observed signiﬁcantly
greater improvements (11–12%) at two speeds compared to
the control group [78]. Other studies found no signiﬁcant
changes following the intervention [32, 33, 77, 79,
82, 84, 91] or a change which was not superior to the
control group [81, 90, 92].
3.9 Time-Trial Performance
To assess the impact of ST directly upon distance running
performance, studies utilized time trials over 1000 m
(preceded by 5 9 1 km) [90, 92], 1500 m [88], 2.4 km
[87], 3 km [75, 80, 91], 5 km [31, 73], 10 km [88, 89],
5 min [32], and 40 min [38]. There were similarities to
competitive scenarios in most studies, including perfor-
mances taking place under race conditions [31, 75,
87, 90–92], on an outdoor athletics track [31, 87–89], on
an indoor athletics track [73, 75, 80, 90–92], and fol-
lowing a prolonged (90-min) submaximal run [38]. Per-
formance
improvements
were
statistically
signiﬁcant
compared to a control group for eight of the 12 trials. The
exceptions were a 40-min time trial [38], a 1000-m rep-
etition [90, 92], and two studies that used a 3 km time trial
[75, 80]. Statistically signiﬁcant 3 km improvements were
observed for all groups in one case [80]; however, the ES
was larger for the two intervention groups (0.37 and 0.46)
compared to the control group (0.20). Improvements over
middle-distances (1500–3000 m) were generally moderate
(3–5%, ES: 0.4–1.0). Moderate to large effects (ES:[1.0)
were observed for two studies [31, 88] that evaluated
performance over longer distances (5–10 km); however,
the relative improvements were quite similar (2–4%) over
long
distances
compared
to
shorter
distances
[31, 73, 88, 89].
3.10 Anaerobic Outcomes
Tests relating to anaerobic determinants of distance run-
ning performance were used in ﬁve investigations. Sprint
speed over 20 m [73, 87] and 30 m [78] showed statis-
tically signiﬁcant improvements following ST (1.1–3.4%).
Two
studies
provided
evidence
for
enhancement
of
Table 3 continued
Study
Main strength outcomes
Economy
_VO2max= _VO2peak
v _VO2max
Blood lactate
Time trial
Anaerobic
measures
Body composition
Vikmoen
et al.
[32, 38]
1RM half–squat (45%,
ES = 2.4, p\0.01), SJ
(8.9%, ES = 0.83,
p\0.05), CMJ (5.9%,
ES = 0.65, p\0.05)
@10 km h-1, NS
NS
NS
v3.5 mmol L-1, NS
5 min TT
(4.7%,
ES = 0.95,
p\0.05).
40 min TT,
NS
I: Leg mass (3.1%,
ES = 1.69,
p=p\0.05), body
mass, NS
C: Leg mass (-2.2%),
body mass decrease
(-1.2%, p\0.05)
ARD anaerobic running distance, BJ broad jump, BL blood lactate, CMJ counter-movement jump, C control group, DJ drop jump, DJRSI drop jump reactive strength index, EC energy cost,
EMG electromyography, ERT explosive resistance training, FFM fat-free mass, FU fractional utilization, GCT ground contact time, GRF ground reaction force, HR heart rate, HRT heavy
resistance training, I intervention group, kleg leg stiffness, kvert vertical stiffness, (s)LT (speed at) lactate threshold, MAS maximal aerobic speed, MTS musculotendinous stiffness, MVC
maximum voluntary contraction, PPO peak power output, PT plyometric training, QF quadriceps femoris, RCP respiratory compensation point (VE/VCO2), RFD rate of force development, RM
repetition maximum, RMR resting metabolic rate, RT resistance training, RTWBV resistance training with whole body vibration, SJ squat jump, TT time trial, TTE time to exhaustion, v velocity,
vMART velocity during maximal anaerobic running test, _VO2 oxygen uptake, _VO2max= _VO2peak highest oxygen uptake associated with a maximal aerobic exercise test, v _VO2max velocity
associated with _VO2max, wk week
1132
R. C. Blagrove et al.
123
vMART [73, 78], and one further study showed no
change in anaerobic running distance after 6 weeks of
HRT [31]. A 30-s Wingate test was also used in one
paper; however, no differences in performance were noted
[89].
3.11 Body Composition
Body mass did not change from baseline in 18 of the
studies [32, 33, 36, 38, 72–75, 77, 79–81, 83, 84, 86–89];
however, one investigation reported a signiﬁcant increase
(2%, ES: 0.32) following ST [78]. This study also docu-
mented changes in the thickness of quadriceps femoris
muscle in both the intervention (3.9%, ES: 0.35) and
control group (1.9%, ES: 0.10) [78]. Similarly, an increase
in total lean mass (3%) and leg lean mass (3%) was found
following 12 weeks of ST despite little alteration in cross-
sectional area of the vastus lateralis and body mass being
noted [90, 92]. Another study observed a signiﬁcant
decrease (- 1.2%) in body mass in the control group, with
no change in the intervention group [32]. A signiﬁcant
increase in leg mass (3.1%, ES: 1.69) was also noted in this
study [32, 38]. Other indices of body composition that
exhibited
no
signiﬁcant
changes
were:
fat
mass
[33, 36, 72, 73, 78, 86], fat-free mass [36, 72, 86], lean
muscle mass [33, 78], skinfolds [83, 89], and limb girth
measurements [72, 73, 83].
4 Discussion
The aim of this systematic review was to identify and
evaluate current literature which investigated the effects of
ST exercise on the physiological determinants of middle-
and long-distance running performance. The addition of
new research published in this area, and the application of
more liberal criteria provided results for 50% more par-
ticipants (n = 469) compared to a recent review on RE
[10]. Based upon the data presented herein, it appears that
ST activities can positively affect performance directly and
provide beneﬁts to several physiological parameters that
are important for distance running. However, inconsisten-
cies exist within the literature, that can be attributed to
differences in methodologies and characteristics of study
participants, thus practitioners should be cautious when
applying generalized recommendations to their athletes.
Despite the moderate PEDro scores (4, 5, or 6), the quality
of the works reviewed in this paper are generally consid-
ered acceptable when the unavoidable constraints imposed
by a training intervention study (related to blinding) are
taken into account.
4.1 Running Economy
Running economy, deﬁned as the oxygen or energy cost to
run at a given sub-maximal velocity, is inﬂuenced by a
variety of factors, including force-related and stretch–
shortening cycle qualities, which can be improved with ST
activities.
In
general,
an
ST
intervention,
lasting
6–20 weeks, added to the training program of a distance
runner appears to enhance RE by 2–8%. This ﬁnding is in
agreement with previous meta-analytical reviews in this
area that show concurrent training has a beneﬁcial effect
(* 4%) on RE [10, 26]. In real terms, an improvement in
RE of this magnitude should theoretically allow a runner to
operate at a lower relative intensity and thus improve
training and/or race performance. No studies attempted to
demonstrate this link directly, although inferences were
made in studies, which noted improvements in RE and
performance separately [73, 80, 88]. Other works provide
evidence that small alterations in RE (* 1.1%) directly
translate to changes (* 0.8%) in sub-maximal [94] and
maximal running performance [95]. The typical error of
measurement of RE has been reported to be 1–2% [96–99]
and the smallest worthwhile change * 2% [94, 98, 100],
which is thought to represent a ‘‘real’’ improvement and
not simply a change due to variability of the measure.
Taken together, it is therefore likely that the improvements
seen in RE following a period of concurrent training would
represent a meaningful change in performance.
Improvements were observed in moderately-trained
[72, 76, 84, 86], well-trained [33, 36, 73, 75, 79, 80, 88]
and highly-trained participants [74, 77], suggesting runners
of any training status can beneﬁt from ST. Different modes
of ST were utilized in the studies, with RT or HRT
[72, 78, 79, 84, 86], ERT [80], PT [75, 76, 80], and a
combination of these activities [33, 36, 77], all augmenting
RE to a similar extent. Single-joint isometric RT may also
provide a beneﬁt if performed at a high frequency (4
day week-1) [84]. Several studies adopted a periodized
approach to the types of ST prioritized during each 3- to
6-week cycle [33, 36, 77, 88], which is likely to provide the
best strategy to optimize gains long-term [101].
Six studies [32, 81–83, 89, 91] failed to show any
improvement in RE and a further six [36, 73, 76–78, 86]
observed both improvements and an absence of change at
various velocities. This implies beneﬁts are more likely to
occur under speciﬁc conditions relating to the choice of
exercises, participant characteristics, and velocity used to
measure RE. In most studies that observed a beneﬁt,
exercises
with
free
weights
were
utilized
[33, 36, 72, 74, 86, 88]. Multi-joint exercises using free
weights are likely to provide a superior neuromuscular
stimulus compared to machine-based or single-joint exer-
cises as they demand greater levels of co-ordination, multi-
Effects of Strength Training on Distance Running
1133
123
planar control, activation of synergistic muscle groups
[102, 103] and usually require force to be produced from
closed-kinetic chain positions. These types of exercise also
have a greater biomechanical similarity to the running
action so are therefore likely to provide a greater level of
speciﬁcity and hence transfer of training effect [104]. An
insufﬁcient overload or a lack of movement pattern
speciﬁcity may therefore be the reason for the absence of
an effect in studies that used only resistance machines
[32, 81] or a single-joint exercise [82]. These studies were
also characterized by a lower frequency of sessions com-
pared to studies that used similar RT exercises but did
observe an improvement in RE [78, 84].
Moderately-trained runners were used in three of the six
studies showing an absence of effect [81, 83, 91] and one
used triathletes who performed a relatively low volume of
running (34.8 km week-1) as part of their training [83].
However, a similar number of studies who used recre-
ational athletes did show a positive effect [72, 76, 84, 86],
suggesting that training level is unlikely to be the reason
for the lack of response in these studies. This is also con-
ﬁrmed by recent observations that showed improvement in
RE following a period of concurrent training was similar
across individuals irrespective of training status and the
number of sessions per week ST was performed [10].
The velocity used to assess RE may also explain the
discrepancies in results across studies. It has been sug-
gested that runners are most economical at the speeds they
practice at most [98], and for investigations that utilized
PT, stretch–shortening cycle improvements are likely to
manifest at high running speeds where elastic mechanisms
have greatest contribution [83, 105]. Therefore a velocity-
speciﬁc measurement of RE may be the most valid strategy
to establish whether an improvement has occurred. For
example,
Saunders
and
associates
[77]
observed
an
improvement (p = 0.02, ES: 0.35) at 18 km h-1 in elite
runners, but an absence of change at slower speeds. Sim-
ilarly, Millet and colleagues [74] noted large (ES:[1.1)
improvements
at
speeds
faster
than
75%
v _VO2max
(* 15 km h-1) in highly-trained triathletes, and Paavo-
lainen et al. [73] detected changes at 15 km h-1 but not
slower speeds in well-trained runners. Furthermore, Pia-
centini and co-workers [86] found improvement at race-
pace in recreational marathon runners but not at a slower
and a faster velocity. Improvements observed at faster
compared to slower speeds may also reﬂect improvements
in motor unit recruitment as a consequence of ST. As
running speed increases there is a requirement for greater
peak vertical forces due to shorter ground contact times,
which elevates metabolic cost [25]. To produce higher
forces, yet overcome a reduction in force per motor unit as
a consequence of a faster shortening velocity, more motor
unit recruitment is required [106]. Thus, an increase in
absolute motor unit recruitment following a period of ST
would result in a lower relative intensity reducing the
necessity to recruit higher threshold motor units during
running [25]. Several studies that failed to show any
response used a single velocity to assess RE [32, 83, 89],
perhaps indicating that the velocity selected was unsuit-
able to capture an improvement. Furthermore, only a small
number of studies used relative speeds [33, 74, 79, 81, 82],
with most choosing to assess participants at the same
absolute intensity. A given speed for one runner may rep-
resent a high relative intensity, whereas for another runner
it may be a relatively low intensity. Therefore selecting the
same absolute speed in a group heterogeneous with respect
to _VO2max, may not provide a true reﬂection of any changes
which take place following an intervention. Moreover, this
may also confound any potential improvements observed
in fractional utilization of _VO2max.
Several common procedural issues exist in the studies
reviewed, which may inﬂuence the interpretation of results
and therefore conclusions drawn. The majority of studies
quantiﬁed RE and _VO2max as a ratio to body mass; how-
ever, oxygen uptake does not show a linear relationship
with increasing body size [107]. It is also known that the
relationship between body size and metabolic response
varies across intensities, with a trend for an increasing size
exponent as individuals move from low-intensity towards
maximal exercise [108, 109]. Moreover, allometric scaling
is likely to decrease interindividual variability [110],
potentially improving the reliability of observations [99].
Ratio-scaling RE for all velocities to body mass is therefore
theoretically and statistically inappropriate [111]. Just two
studies [79, 80] used an appropriate allometric scaling
exponent (0.75) to account for the non-linearity associated
with oxygen uptake response to differences in body mass,
both establishing a large ES in their results. The unsuit-
ability of ratio-scaling as a normalization technique when
processing physiological data is likely to have inﬂuenced
the statistical outcomes of some studies and thus inaccurate
conclusions may have been generated.
Running economy was expressed as oxygen cost in all
but three studies [82, 84, 91], which quantiﬁed RE using
the energy cost method. As the energy yield from the
oxidation of carbohydrates and lipids differs, subtle alter-
ations in substrate utilization during exercise can confound
measurement of RE when expressed simply as an oxygen
uptake value. Energy cost is therefore the more valid
[112, 113] and reliable [99] metric for expressing econ-
omy, compared to traditional oxygen cost, as metabolic
energy expenditure can be calculated using the respiratory
exchange ratio, thus accounting for differences in substrate
utilization. Despite attempts to control for confounding
1134
R. C. Blagrove et al.
123
variables such as diet and lifestyle in most studies, equiv-
alence in inter-trial substrate utilization cannot be guaran-
teed, which may have impacted upon the measurement of
RE.
4.2 Maximal Oxygen Uptake
Maximal oxygen uptake is widely regarded as one of the
most important factors in distance running success [114],
therefore the objective for any distance runner is to maxi-
mize their aerobic power [9]. An individual’s _VO2max is
limited by their ability to uptake, transport and utilize
oxygen in the mitochondria of working muscles. Endur-
ance training involving prolonged continuous bouts of
exercise or high intensity interval training induces adap-
tations primarily within the cardiovascular and metabolic
systems that results in improvements in _VO2max [9, 115].
Conversely, ST is associated with a hypertrophy response
that increases body mass and has been reported to decrease
capillary density, oxidative enzymes and mitochondrial
density [116–118], which would adversely impact aerobic
performance. Theoretically there is therefore little basis for
ST as a strategy to enhance aerobic power. However it is
important to address whether in fact _VO2max is negatively
affected when distance running is performed concurrently
with ST.
Thirteen works in this review found no change in
_VO2max following the intervention period, demonstrating
that although ST does not appear to positively inﬂuence
_VO2max, it also does not hinder aerobic power. Although
ST in most studies was supplementary to running training,
it appears that the additional physiological stimulus pro-
vided by ST was insufﬁcient to elicit changes in cardio-
vascular-related
parameters
[119].
Three
studies
did
observe signiﬁcant increases in aerobic power that did not
differ to the change observed in the control group [33, 81,
91], and one further study found an improvement in
_VO2max in the control group only [78]. It is perhaps sur-
prising that more studies did not ﬁnd an increase in _VO2max
(in any group) given that participants continued their nor-
mal running training through the study period. Improve-
ments in _VO2max of 5–10% have been shown following
relatively short periods (\6 weeks) of endurance training
[9]; however, the magnitude of changes is dependent upon
a variety of factors including the initial ﬁtness level of
individuals and the duration and nature of the training
programoo [120]. Maximal oxygen uptake is known to
have an innate upper limit for each individual, therefore in
highly-trained and elite runners, long-term performance
improvement is likely to result from enhancement of other
physiological
determinants,
such
as
RE,
fractional
utilization and v _VO2max [4, 121, 122]. A number of studies
used moderately-trained participants [23, 72, 76, 81, 91],
who would be the most likely to show an improvement in
_VO2max following a 6- to 14-week period of running, with
two investigations demonstrating improvements for both
groups [81, 91]. The absence of _VO2max improvement in
other papers suggests that the duration of the study and/or
the training stimulus, was insufﬁcient to generate an
improvement [120]. Indeed, one study of 40 weeks’ dura-
tion in Collegiate level runners observed similar improve-
ments (ES: 0.5–0.6) in
_VO2max in both groups [33],
suggesting a longer time period may be required to detect
changes in runners with a higher training status. High-in-
tensity aerobic training ([80% _VO2max) is a potent stim-
ulus for driving changes in _VO2max[123]; however, some
studies reported runners predominantly utilized low-inten-
sity (\70%
_VO2max) continuous running [74, 78, 89],
which may also explain the lack of changes observed.
4.3 Velocity Associated with _VO2max
An individual’s v _VO2max is inﬂuenced by their _VO2max, RE
and anaerobic factors including neuromuscular capacity [4,
124]. The amalgamation of several physiological qualities
into this single determinant appears to more accurately
differentiate performance, particularly in well-trained run-
ners [3, 98, 125, 126], therefore v _VO2max has been labelled
as an important endurance-speciﬁc measure of muscular
power [127].
Improvements for v _VO2max (3–4%, ES: 0.42–0.49) were
found in two investigations [80, 89], with a further two
studies observing improvements (2.6–4.0%, ES: 0.57–0.9)
that could not be ascribed to the training differences
between the groups [33, 74]. A number of studies also
found little change in v _VO2max following an intervention
[31, 32, 36, 78, 85]. As v _VO2max is the product of the
interaction between aerobic and anaerobic variables, a
small improvement in one area of physiology may not
necessarily result in an increase in v _VO2max. Damasceno
et al. [89] found an improvement in v _VO2max (2.9%,
p\0.05, ES: 0.42) despite detecting no change in _VO2max,
RE or Wingate performance, therefore attributed the
change to the large improvements (23%, ES: 1.41) in the
force-producing ability they observed in participants.
Conversely, Berryman and associates [80] found changes
in v _VO2max (4.2%, ES: 0.43–0.49) alongside improvements
in RE (4–7%, ES: 1.01), moderate increases in power
output, and no change in _VO2max scores. Beattie and co-
workers [33] credited the change in v _VO2max they observed
(20-weeks:
3.5%,
ES:
0.7)
to
the
accumulation
of
Effects of Strength Training on Distance Running
1135
123
improvements in RE, _VO2max and anaerobic factors; how-
ever, these were not sufﬁciently large enough to provide a
signiﬁcant group 9 time interaction. Millet and colleagues
[74] found notable improvements in RE (7.4%, ES: 1.14);
however, changes in RE could not explain the changes
observed in v _VO2max (r = - 0.46, p = 0.09). It may also
be the case that longer periods of ST are required before an
improvement in v _VO2max is detected, as studies showing an
improvement (2.6–4.0%, ES: 0.57–0.9) from baseline las-
ted 14 weeks or more [33, 74], and studies showing little
change tended to be 6–8 weeks in duration [31, 78, 85].
The conﬂicting results could also be explained by the
inconsistency in methods used to deﬁne v _VO2max. A
number of different protocols and predictive methods have
been suggested to assess v _VO2max [4], including determi-
nation from the _VO2-velocity relationship [128] and the
peak running speed attained during a maximal test using
speed increments to achieve exhaustion [21, 127]. All
studies that measured v _VO2max in this review did so via an
incremental run to exhaustion progressed using velocity.
Velocity at _VO2max was taken as the highest speed that
could be maintained for a full 60-s stage [78, 80, 85], an
average of the ﬁnal 30-s [31, 36], the mean velocity in the
ﬁnal 120-s [32], or the minimum velocity that elicited
_VO2max [33, 74]. Although a direct approach to the mea-
surement of v _VO2max has been recommended [4], due to
the velocity increments (0.5–1.0 km h-1) used in these
investigations, this may not provide sufﬁcient sensitivity to
detect a change following a short- to medium-term inter-
vention.
Damasceno
and
associates
[89]
calculated
v _VO2max using a more precise method based upon the
fractional time participants reached through the ﬁnal stage
of the test multiplied by the increment rate. This perhaps
provided a greater level of accuracy which allowed the
authors to identify the differences in changes which existed
between the groups. Taken together, there is weak evidence
that v _VO2max can be improved following an ST interven-
tion, despite constituent physiological qualities often
exhibiting change. Differences in the protocols used to
determine v _VO2max makes comparison problematic; how-
ever, a more precise measurement of v _VO2max
that
accounts for partial completion of a ﬁnal stage is likely to
provide the sensitivity to identify subtle changes that may
occur.
The critical velocity model, which represents exercise
tolerance in the severe intensity domain, potentially offers
an alternative to measurement of v _VO2max that is currently
uninvestigated in runners [35, 129]. Two main parameters
can be assessed using the critical velocity model; critical
velocity itself, which is deﬁned as the lower boundary of
the severe intensity domain which when maintained to
exhaustion leads to attainment of _VO2max, and the curva-
ture constant of the velocity–time hyperbola above critical
velocity, which is represented by the total distance that can
be covered prior to exhaustion at a constant velocity [130].
Middle-distance running performance (800 m) is strongly
related to critical velocity models (r = 0.83–0.94) in
trained runners [131], and may be more important than RE
in well-trained runners [35]. Evidence from studies using
untrained participants has demonstrated that the total
amount of work that can be performed above critical power
during
high-intensity
cycling
exercise
is
improved
(35–60%) following 6–8 weeks of RT [132, 133]. Future
investigations should therefore address the dearth in liter-
ature around how ST might positively inﬂuence parameters
related to the critical velocity model [35].
4.4 Blood Lactate Markers
A runner’s velocity at a reference point on the lactate-
velocity curve (e.g., LT) or BL for a given running speed
are important predictors of distance running performance
[134–136]. A runners LT also corresponds to the fractional
utilization of _VO2max that can be sustained for a given
distance [114], therefore an increase in LT also allows a
greater proportion of aerobic capacity to be accessed.
In contrast to RE, ST appears to have little impact upon
BL markers. This is quite surprising as an improvement in
RE should theoretically result in an enhancement in speed
for a ﬁxed BL concentration. This suggests that adaptations
to RE can occur independently to changes in metabolic
markers of performance. An absence of change in BL also
implies that ST does not alter anaerobic energy contribu-
tion during running, thus assuming aerobic energy cost of
running is reduced following ST, it can be inferred that
total energy cost (aerobic plus anaerobic energy) is also
likely to be reduced. Previous studies have shown as little
as 6 weeks of endurance training can improve BL levels or
the velocity corresponding to an arbitrary BL value in
runners [137–139]. The intensity of training is important to
elicit improvement in BL parameters [140], therefore it
appears that the running training prescription may have
been insufﬁcient to stimulate improvements, or the training
status of participants meant a longer period was required to
realize a meaningful change. In addition, the inter-session
reliability of BL measurement between 2–4 mmol L-1
is * 0.2 mmol L-1 [99], therefore over a short study
duration this metric may not provide sufﬁcient sensitivity
to detect change.
Training at an intensity above the LT is likely to result
in a reduction in the rate of BL production (and therefore
accumulation), or an improved lactate clearance ability
from the blood [9]. Short duration high-intensity bouts of
1136
R. C. Blagrove et al.
123
activity generate high levels of BL so drive metabolic
adaptations which can result in an improvement in per-
formance [141–143]. Studies that have utilized high-repe-
tition, low-load RT in endurance athletes therefore have the
potential to produce high BL concentrations so may pro-
vide an additional stimulus to improve performance via BL
parameters. This theory is supported by works that have
demonstrated improvements in BL-related variables in
endurance athletes following an intervention that uses a
strength-endurance style of conditioning with limited rest
between sets [54, 62, 144]. The ST prescription in the
studies reviewed was predominantly low-repetition, high-
intensity RT or PT, which is unlikely to have provided a
metabolic environment sufﬁcient to directly enhance
adaptations related to BL markers.
4.5 Time-Trial Performance
Physiological parameters such as _VO2max, v _VO2max, RE
and LT are clearly important determinants that can be
quantiﬁed in a laboratory; however, for a runner, TT per-
formance possesses a far higher degree of external validity.
Similar improvements in TT performance were observed
for middle-distance events (3–5%, ES: 0.4–1.0) and long-
distance events up to 10 km (2–4%, ES: 1.06–1.5). In the
majority of these studies, time trials took place in a similar
environment and under comparable conditions to a race,
therefore these ﬁndings have genuine applicability to ‘‘real-
life’’ scenarios. These improvements are likely to be a
consequence of signiﬁcant enhancements in one or more
determinants of performance. Interestingly, Damasceno
and co-authors [89] found an improvement in 10 km TT
performance due to the attainment of higher speeds in the
ﬁnal 3 km, despite observing no change in RE during a
separate assessment. This suggests that greater levels of
muscular strength may result in lower levels of relative
force production per stride, thereby delaying recruitment of
higher threshold muscle ﬁbers and thus providing a fatigue
resistant effect [145]. This subsequently manifests in a
superior performance during the latter stages of long-dis-
tance events [89].
Four studies observed no difference in performance
change compared to a control group [38, 75, 80, 90, 92].
Vikmoen and colleagues [38] attributed a lack of effect in
their 40 min TT to the slow running velocity caused by the
5.3% treadmill inclination used in the test. This was also
the only study to use a treadmill set to a pre-determined
velocity which participants could control once the test had
commenced. The absence of natural self-pacing may
therefore have prevented participants achieving their true
potential on the test. Spurrs et al. [75] and Berryman et al.
[80] both found improvements in 3 km performance
compared to a pre-training measure of a comparable
magnitude to other studies (2.7–4.8%, ES: 0.13–0.46);
however, changes were not signiﬁcantly different to a
control group, suggesting ST provided no additional beneﬁt
or there was a practice effect associated with the test.
It could be possible that enhancement of physiological
qualities in some studies could be attributed to RT being
positioned immediately after low-intensity, non-depleting
running sessions [146]. This arrangement of activities in
concurrent training programs has been shown to provide a
superior stimulus for endurance adaptation compared to
performing separate sessions, and without compromising
the signaling response regulating strength gains [147, 148].
This, however, appears not to be the case, as most studies
reported ST activities took place on different days to run-
ning sessions [85, 88, 89] or were at least performed as
separate sessions within the same day [33, 36, 38, 72, 75,
78]. Only three studies performed ST and running imme-
diately after one another, with one positioning PT before
running [87] and one lacking clarity on sequencing [76].
Schumann and colleagues [90, 92] observed no additional
beneﬁt to both strength and endurance outcomes compared
to a running only group, when ST was performed imme-
diately following an incremental running session (65–85%
maximal heart rate), citing residual fatigue which com-
promised quality of ST sessions as the reason.
4.6 Anaerobic Running Performance
The contribution of anaerobic factors to distance running
performance is well established [127, 149]. In particular,
anaerobic capacity and neuromuscular capabilities are
thought to play a large role in discriminating performance
in runners who are closely matched from an aerobic per-
spective [124, 150]. An individual’s v _VO2max perhaps
provides the most functional representation of neuromus-
cular power in distance runners; however, measures of
maximal running velocity and anaerobic capacity are also
potentially important [127].
Tests for pure maximal sprinting velocity (20–30 m)
were used in three studies [73, 78, 87] and showed
improvements (1.1–3.4%) following ST in every case. This
conﬁrms results from previous studies that have shown
sprinting performance can be positively affected by an ST
intervention in shorter-distance specialists [151–153]. This
ﬁnding has important implications for distance runners, as
competitive events often involve mid-race surges and
outcomes are frequently determined in sprint-ﬁnishes,
particularly at an elite level [154–157]. Middle-distance
runners also beneﬁt from an ability to produce fast running
speeds at the start of races [158], therefore improving
maximum speed allows for a greater ‘‘anaerobic speed
Effects of Strength Training on Distance Running
1137
123
reserve’’ [159], resulting in a lower relative work-rate, and
thus decreasing anaerobic energy contribution [41]. Inter-
estingly, endurance training in cyclists has been shown to
improve critical power [160] but reduce work capacity for
short duration exercise [161, 162]. It is unknown whether
long-term aerobic training has a similar effect on anaerobic
running qualities; however, ST offers a strategy to avoid
this potential negative consequence.
The velocity attained during a maximal anaerobic run-
ning test provides an indirect measure of anaerobic and
neuromuscular performance, and has a strong relationship
(r = 0.85) to v _VO2max [19]. The vMART is particularly
relevant to middle-distance runners because it requires
athletes to produce fast running speeds under high-levels of
fatigue caused by the acidosis and metabolites derived
from glycolysis [163]. Both studies that included this test
observed signiﬁcant improvements in vMART (1.1–3.4%),
which can be attributed to changes observed in neuro-
muscular power as a result of the ST intervention [73, 78].
One study showed no alteration in the predicted distance
achieved on an anaerobic running test following 6 weeks
of HRT; however, the validity and reliability of the test was
questioned by the authors [31]. Performance on a 30 s
Wingate test was also unchanged following 8 weeks of
running training combined with HRT in recreational par-
ticipants [89]. This ﬁnding perhaps underlines the impor-
tance of selecting tests which are speciﬁc to the training
which has been performed in the investigation.
4.7 Strength Outcomes
Changes in strength outcomes were evident in most studies
despite all but one [78] observing no change in body mass.
Since strength changes can be ascribed to both neurological
and morphological adaptations [164], it is therefore likely
that improvements are primarily underpinned by alterations
in intra- and inter-muscular co-ordination. It is also known
that initial gains in strength in non-strength trained indi-
viduals are the consequence of neural adaptations rather
than structural changes [118]. An improvement in force
producing capability is perhaps expected in individuals
who have little or no strength-training experience [165];
however, concurrent regimens of training have consistently
been shown to attenuate strength-related adaptation [30].
The seminal paper published by Hickson et al. [48] was
the ﬁrst to identify the potential for endurance exercise to
mitigate strength gains, when both training modalities were
performed concurrently within the same program. Follow-
up
investigations
have
since
shown
mixed
results
[166–171], but evidence from this review clearly demon-
strates that, for the distance runner at least, strength-related
improvements are certainly possible following a concurrent
period of training. Nevertheless, the study designs adopted
by the works under review did not include a strength-only
training group, thus it is not possible to determine whether
strength adaptation was in fact negated under a concurrent
regimen. One study using well-trained endurance cyclists
with no ST experience, observed a blunted strength
response in a group who added ST to their endurance
training compared to a group who only performed ST
[170]. Based upon this ﬁnding and other similar observa-
tions [167, 172, 173] it seems likely that although distance
runners can signiﬁcantly improve their strength using a
concurrent approach to training, strength outcomes are
unlikely to be maximized. Moreover, the degree of inter-
ference with strength-adaptation also appears to be exac-
erbated when volumes of endurance training are increased
and the duration of concurrent training programs is longer
[30, 146].
4.8 Body Composition
Resistance training performed 2–3 times per week is
associated with increases in muscle cross-sectional area as
a principal adaptation [174]. Although gains in gross body
mass may appear to be an unfavorable outcome for dis-
tance runners, the addition of muscle mass to proximal
regions of the lower limb (i.e., gluteal muscles) should
theoretically provide an advantage, via increases in hip
extension forces, minimizing moment of inertia of the
swinging limb, and reducing absolute energy usage [25]. It
is somewhat surprising that virtually all studies demon-
strated an absence of change in body mass, fat-free mass,
lean muscle mass, and limb girths. Other than one inves-
tigation [33], the duration of the studies that observed no
effect on measures of body composition was\14 weeks,
suggesting this may not have been sufﬁciently long to
demonstrate a clear hypertrophic response. There is also a
possibility that small increases in muscle mass within
speciﬁc muscle groups (e.g., gluteals) were present, and
contributed to the improvements observed in RE, but these
may not have been detectable using a gross measure of
mass. Evidence for this may have occurred in the Schu-
mann et al. study [90, 92], who observed increases in total
lean mass (3%) despite noting no signiﬁcant change in
body mass or cross-sectional area of the vastus lateralis
compared to baseline measures.
The interference effect observed during concomitant
integration of endurance and ST as part of the same pro-
gram may also provide an explanation for the lack of
change in measures of mass. Following a bout of exercise,
a number of primary and secondary signaling messengers
are up regulated for 3–12 h [175], which initiate a series of
molecular events that serve to activate or suppress speciﬁc
1138
R. C. Blagrove et al.
123
genes. The signaling messengers which are activated, relate
to the speciﬁc stress which is imposed on the physiological
systems involved in an exercise bout. Strength training
causes mechanical perturbation to the muscle cell, which
elicits a multitude of signaling pathways that lead to a
hypertrophic response [176]. In particular, the secretion of
insulin-like growth factor-1 as a result of intense muscular
contraction is likely to cause a cascade of signaling events
which increase activity of phosphoinositide-3-dependent
kinase (Pl-3 k) and the mammalian target of Rapamycin
(mTOR) [177–179]. There is strong evidence that mTOR is
responsible for mediating skeletal muscle hypertrophy via
activation of ribosome proteins which up regulate protein
synthesis [180]. Prolonged exercise bouts, such as those
associated with endurance training, activate metabolic
signals related to energy depletion, uptake and release of
calcium ions from the sarcoplasmic reticulum and oxida-
tive stress in cells [181]. Adenosine monophosphate acti-
vated kinase (AMPK) is a potent secondary messenger
which functions to monitor energy homeostasis [182] and
when activated, modulates the release of peroxisome pro-
liferator
co-activator-1a,
which
along
with
calcium-
calmodulin-dependent
kinases
increase
mitochondrial
function to enhance aerobic function [181, 183, 184].
Crucially though, AMPK also acts to inhibit the Pl-3 k/
mTOR stage of the pathway via activation of the tuberous
sclerosis complex thereby suppressing the ST induced up
regulation of protein synthesis [185, 186]. This conﬂict
arising at a molecular signaling level therefore appears to
impair the muscle ﬁber hypertrophy response to ST and
attenuate increases in body mass [186].
4.9 Muscle–Tendon Interaction Mechanisms
The
potential
mechanisms
for
the
positive
changes
observed in physiological parameters underpinning running
performance were directly investigated in three studies [82,
84, 91], and were inferred from gait measures [36, 73–75,
77] and strength outcomes in others. It is well documented
that muscle–tendon unit stiffness correlates well with RE
[187–189]. Tendons are also highly adaptable to mechan-
ical loading and have been shown to increase in stiffness in
response to HRT and PT [84, 190, 191]. Despite observing
no statistical effect for HRT on RE, Fletcher and col-
leagues [82] also found a relationship between the change
in RE and the changes observed in Achilles tendon stiff-
ness. Despite these associations, it is likely that improve-
ments in RE are a consequence of the interaction between
adaptations to tendon properties and improvements in
motor unit activation which inﬂuence behavior of force–
length-velocity properties of muscles [25]. It tends to be
assumed that improved tendon stiffness allows the body to
store and return elastic energy more effectively, which
results in a reduction in muscle energy cost due to a greater
contribution from the elastic recoil properties of tendons
[192]. Indeed, authors of studies in the present review have
argued that the improvements observed in RE following a
period of ST are due to an enhanced utilization of elastic
energy during running [36, 73–75]. An alternative pro-
posal, based upon more recent evidence, suggests the
Achilles tendon provides a very small contribution to the
total energy cost of running therefore improvements in
stiffness provide a negligible reduction in energy cost [193,
194]. Instead, a tendon with an optimal stiffness con-
tributes to reducing RE by minimizing the magnitude and
velocity of muscle shortening, thus allowing muscle fas-
cicles to optimize their length and remain closer to an
isometric state [25]. A reduction in the amount and velocity
of ﬁber shortening therefore reduces the level of muscle
activation required and hence the energy cost of running
[193].
The improvements observed in maximal and explosive
strength, which can be attributed to increases in motor unit
recruitment and ﬁring frequency, enable the lower limb to
resist eccentric forces during the early part of ground
contact [165] and thus contribute to the attainment of a
near isometric state during stance. As the force required to
sustain speed during distance running performance is
submaximal, the level of motor unit activation needed can
be minimized when fascicles contract isometrically [25].
This enables the Achilles tendon in particular to accom-
modate a greater proportion of the muscle–tendon unit
length change during running thereby reducing metabolic
cost [194]. Variables which provide an indirect measure of
the neuromuscular systems ability to produce force rapidly
and utilize tendon stiffness were found to improve in other
studies that showed improvements in running performance
and/or key determinants [73, 74, 78–80, 87]. However,
some studies found improvements in running-related
parameters despite observing no alterations in jump per-
formance [33, 76–78, 91], rate of force development [36,
75, 77], or stiffness [33, 74, 89] illustrating that measures
were insufﬁciently sensitive to detect change, or a combi-
nation of mechanisms is likely to be contributing towards
the enhancements observed.
Heavy RT causes a shift in muscle ﬁber phenotype, from
the less efﬁcient myosin heavy chain (MHC) IIx to more
oxidative MHC IIa, [195, 196]. A higher proportion of
MHC IIa has been shown to relate to better running
economy [91, 197, 198]; however, whether changes to
MHC properties as a result of ST contribute to an
improvement in RE and performance remains to be deter-
mined. One previous study provided evidence that 4 weeks
of sprint running (30-s bouts) improve RE and also the
percentage of MHC IIx [199]; however, the absence of
endurance
training
may
partly
explain
the
shift
in
Effects of Strength Training on Distance Running
1139
123
phenotype. Over a longer period (6 weeks), Pellegrino and
co-workers [91] found no measurable changes in MHC
isoforms following a PT intervention despite a signiﬁcant
improvement in 3 km TT performance, suggesting that a
contribution from this mechanism is unlikely for distance
running.
It could also be speculated that improvements in RE due
to improved strength might have resulted in subtle changes
to running kinematics, thus enabling participants to per-
form less work for a given submaximal speed [72]. There is
currently little direct support for this conjecture; however,
previous work has shown that running technique is an
important component of RE [200, 201], and improving hip
strength can reduce undesirable frontal and transverse
plane motion in the lower limb during running [202]. One
study in this review did observe a reduction in EMG
amplitude in the superﬁcial musculature of the lower limb
following ST; however, this wasn’t accompanied by an
improvement in RE [83]. This suggests that favorable
adaptations in neuromuscular control do not necessarily
translate to reducing the metabolic cost of running. Addi-
tionally,
two
studies
showed
signiﬁcant
increases
(3.0–4.4%) in ground contact time during submaximal
running after an ST intervention [36, 81]; however, only
Giovanelli and colleagues [36] found a corresponding
improvement in RE. Several papers have demonstrated an
inverse relationship between RE and ground contact times
[201, 203, 204], since a lower peak vertical force is
required to generate the same amount of impulse during
longer compared to short ground contacts [25]. Although
there is currently minimal evidence to suggest an ST
intervention increases ground contact time during sub-
maximal running, this mechanism may in part explain the
improvements in RE.
4.10 Strength-Training Prescription
4.10.1 Modality and Exercise Selection
The works included in this review used a variety of ST
modalities; however, the most effective type of training is
currently difﬁcult to discern. Adaptations are speciﬁc to the
demands placed upon the body, therefore it would be
expected that HRT, ERT and PT produce somewhat dif-
ferent outcomes [205]. This can be observed in the study by
Berryman and co-workers [80], who observed larger
improvements in explosive concentric power in a group
following an ERT program compared to a group who used
PT. The opposite result occurred for the counter-movement
jump, which places a greater reliance on a plyometric
action; the PT group displayed greater improvements than
the ERT group [80]. Heavy RT, which is characterized by
slow velocities of movement, is likely to improve agonist
muscle activation via enhanced recruitment of the motor
neuron pool, whereas ERT, which involves lighter loads
being moved rapidly, tends to enhance ﬁring frequency and
hence improve rate of force development [164, 165]. Ply-
ometric training develops properties related to the stretch–
shortening cycle function [206], and uses movements pat-
terns which closely mimic the running action (e.g., hopping
and skipping). It is therefore likely that although a variety
of ST methods are capable of improving physiological
parameters relating to distance running performance, the
mechanisms underpinning the response may differ.
In less strength-trained individuals, such as those used in
the studies reviewed, any novel ST stimulus is likely to
provide a sufﬁcient overload to the neuromuscular system
to induce an adaptation in the short term [207]. This is
perhaps why ST is effective even in highly-trained distance
runners [74, 77, 87]. Studies that have attempted to com-
pare ST techniques in distance runners have generally
shown HRT to be superior to ERT or a mixed methods
approach at improving aerobic parameters [57, 63] and
maximal anaerobic running speed [62]. Plyometric training
has also shown superiority to ERT for improvement of RE
in moderately trained runners [80]. Other investigations
have found no differences in the physiological changes
between groups using HRT, ERT or a mixture of modali-
ties [62, 65]. A number of studies have also shown HRT
and/or ERT to be more beneﬁcial to a muscular endurance
style of ST [59, 64, 65, 67, 86]. The addition of whole body
vibration to RT also provides no extra beneﬁt [85].
Although ERT and PT may have more appeal compared to
HRT due to their higher-level of biomechanical similarity
to running, an initial period of HRT is likely to provide an
advantage long-term in terms of reducing injury risk [208]
and eliciting a more pronounced training effect [209].
Taken together, it seems that long-term, a mixed modality
approach to ST is most effective, as this provides the
variety and continual overload required to ensure the
neuromuscular system is constantly challenged. One study
that used a longer intervention period lends support to this
notion, as signiﬁcant improvements were observed in
strength and physiological measures after 20 and 40 weeks
with a periodized methodology that used several types of
ST [33]. Further research is required to ascertain the long-
term beneﬁts of various ST modalities and the relative
merits of different approaches to sequencing and pro-
gressing these modalities.
As discussed in Sect. 4.1, the exercises selected in an ST
program can potentially inﬂuence the magnitude of neu-
romuscular adaptation and thus the impact on physiological
determinants of performance. Exercises using free weights,
which require force to be generated from the leg extensor
muscles in a close-kinetic chain position, are the most
likely to positively transfer to running performance [210].
1140
R. C. Blagrove et al.
123
Examples of RT exercises commonly used include: barbell
squat, deadlifts, step-ups and lunging movement patterns
[31, 33, 36, 72, 79, 85, 88]. Isometric HRT may also have
value for the plantarﬂexors [84]. Explosive RT, by its very
nature, should avoid a deceleration phase, therefore exer-
cises such as squat jumps and Olympic weightlifting
derivatives should be utilized [33, 80]. To maximize
transfer to distance running performance, particularly at
faster speeds, PT exercises should exhibit short ground
contact times (\0.2 s) [36, 72], which approximates the
contact times observed in competitive middle- [211] and
long-distance running [212], and encourages a rapid exci-
tation–contraction coupling sequence and improved mus-
culotendinous stiffness
[36, 73–75]. Exercises which
possess a low to moderate eccentric demand such as depth
jumps (from a 20–30 cm box), skipping, hopping, speed
bounding appear most suitable [33, 73, 75, 77, 80, 83].
4.10.2 Intra-Session Variables
For non-strength trained individuals, exercise prescription
and gradual progression is important to avoid injury and
overtraining [213]. Most studies initially used 1–2 sets and
progressed to 3–6 sets over the course of the intervention
period for HRT, ERT and PT, which appears appropriate to
circumvent these risks. Several studies utilized a low (3–5)
repetition range in every HRT session [31, 79, 81, 86] at
loads which approached maximum (C 80% 1RM or repe-
tition failure), but did not observe superior beneﬁts com-
pared to investigations that prescribed RT at moderate
loads (60–80% 1RM) and higher repetition ranges (5–15
repetitions). Sets were performed to RM in a number of
studies [32, 38, 72, 79, 81, 88, 89], which was likely
employed as a means of standardizing the intensity of each
set in the absence of 1RM data for participants. Performing
sets which leads to repetition failure induces a high level of
metabolic and neuromuscular fatigue, which may delay
recovery [214]. Although training to repetition failure may
be more important than the load lifted for inducing a
hypertrophy response [215], this is both unfavorable and
unnecessary to optimize gains in strength compared to a
non-repetition failure strategy [216]. Not working to rep-
etition failure also appears to become a more important
feature of RT as ST status increases [216]. Participants
were often instructed to move the weights as rapidly as
possible when performing the concentric phase of RT
exercises, which increases the likelihood of maximizing
neuromuscular adaptations [217]. Plyometric training is
characterized by high eccentric forces compared to running
and RT, therefore repetitions per set were typically low
(4–10 repetitions). Total foot contacts progressed from 30
to 60 repetitions in the ﬁrst week of an intervention up to
110–228 repetitions after 6–9 weeks [73, 75, 76, 91].
Plyometric exercises were all performed without additional
external resistance in all but one study [73] and in many
cases a short ground contact time [76, 77, 83] and maximal
height [80, 83] were cued to amplify the intensity. An inter-
set recovery period of 2–3 min was typical for HRT, ERT
and PT, which is in line with recommendations for these
training techniques [213]. Where SpT was incorporated
into
ST
programs,
repetition
distances
were
short
(20–150 m) and performed at or close to maximal running
speed [73, 74, 88].
4.10.3 Inter-Session Variables
The majority of studies that demonstrated improvements in
running physiology scheduled ST 2–3 times per week,
which is in line with the guidelines for non-strength trained
individuals [213]. One study used just one session per week
(ERT or PT) and achieved moderate improvements in
strength outcomes and RE after 8 weeks of training [80].
Beattie and associates [33] observed small improvements
(ES: 0.3) in RE using a single ST session (mixed activities)
each week for 20 weeks; however, the participants had
already experienced moderate improvement (ES: 1.0) in
this parameter using a twice weekly program in the
20 weeks prior. For well-trained runners who complete
8–13 running sessions per week [73, 77], it would be useful
to establish the minimal ST dosage required to elicit a
beneﬁcial effect to reduce the risk of overtraining. Equally,
for the recreational runner, ST may take up valuable leisure
time that could be spent running, therefore identifying the
optimal volume and frequency of ST to achieve an
improvement in performance would be desirable. A pre-
vious meta-analysis indicated that two or three sessions per
week provides a large effect on strength, but for the non-
strength trained individual, three sessions is superior to two
sessions per week [218]. More recently, a weak relation-
ship was established between improvement in RE and
weekly frequency of ST sessions in 311 endurance runners
[10]. This suggests that higher weekly volumes of ST
would not necessarily provide greater RE improvements,
therefore two sessions per week is likely to be sufﬁcient
[10].
Given the volume of endurance training participants
were exposed to and the duration of each study, it seems
likely that an attenuation of strength-related adaptation
would have occurred. To minimize this interference phe-
nomenon, it is therefore recommended that a recovery
period of[3 h is provided following high-intensity run-
ning training before ST takes place [146]. In many studies
running training and ST took place on different days [33,
36, 85, 88, 89], and several papers noted a gap of[3 h
between running and ST on the same day [32, 38, 72, 78,
79]. This feature of concurrent training prescription
Effects of Strength Training on Distance Running
1141
123
therefore appears important in ensuring sufﬁcient strength-
adaptations are realized but without compromising running
training. Although there is very little evidence that the
dosage of ST prescribed impaired any endurance-related
adaptations, recent work has highlighted that acute bouts of
RT may cause fatigue sufﬁcient to impair subsequent
running performance, which long term may result in sub-
optimal adaptation [219]. It is therefore recommended that
this potential fatigue is accounted for by allowing at least
24 h recovery between an ST session and an intensive
running session [33, 85, 88, 89].
The results provide compelling evidence that a rela-
tively short period (6 weeks) of ST can enhance physio-
logical qualities related to distance running performance.
Improvements in RE [57] and 10 km TT performance [88]
have also been shown in as little as 4 weeks. A relationship
between intervention duration and improvement in RE has
previously been reported [10], suggesting that longer
periods of ST provide a larger beneﬁt. The same may be
true for v _VO2max; however, more research using longer
periods of ST is required to establish if this is indeed the
case. The beneﬁts to performance also seem to be depen-
dent
on
study
duration
as
most
short
interventions
(6 weeks) tended to produce small TT improvements
(2.4–2.7%, ES: 0.13–0.4) [75, 87, 91], whereas longer
programs (8–11 weeks) resulted in moderate or large per-
formance effects (3.1–5.5%, ES: 0.67–1.50) [32, 73, 88]. It
would seem reasonable to assume that highly-trained dis-
tance runners would require a higher volume of ST to
achieve the same beneﬁt as less experienced runners;
however, this does not appear to be the case. Relatively
short (6–9 weeks) periods of ST improved RE and TT
performance to a similar extent in highly-trained individ-
uals [77, 87] and recreational runners [76, 86, 91]. It is
therefore recommended that future investigations use
periods of 10 weeks or longer to provide further insight
into how ST modalities may impact physiological param-
eters long-term in different types of distance runner.
The time of year or phase of training when the research
was conducted was not reported in the majority of studies.
Several papers indicated that the intervention formed part
of an off-season preparation period [73, 74, 78, 82, 86], but
others scheduled the intervention within the competition
period [32, 38, 87]. Based upon the literature reviewed, it is
currently not possible to provide speciﬁc recommendations
for ST in different phases of a runners training macrocycle,
as most studies found at least some physiological or per-
formance beneﬁts to concurrent training. Importantly
though, evidence suggests that choosing to exclude ST
following a successful intervention period results in a
detraining effect which causes improvements to return to
baseline
levels
within
6 weeks
[31].
The
40-week
intervention conducted by Beattie and colleagues [33]
provides evidence that reducing ST volume from two
sessions per week (both with a lower limb HRT emphasis)
during the preparatory phase to one weekly session (ERT
and PT emphasis) during the in-season racing period is
sufﬁcient to at least maintain previous strength and phys-
iological gains. This ﬁnding corroborates with a mainte-
nance effect observed in cyclists [220, 221] and soccer
players [222] showing one ST session per week is sufﬁcient
to preserve the strength qualities developed during a pre-
ceding phase of training. Therefore, runners can decrease
ST volume from 2–3 sessions per week (each with a lower
limb focus) in preparatory phases of training to a single
session each week during the competitive season without
fearing a loss of adaptation as a consequence of the
reduction in training density.
It is currently uncertain what volume and intensity of
running and ST are most likely to avoid the interference
effect associated with concurrent training practices. One
option to minimize attenuation of strength development is
to organize activities into periods that concentrate on
developing either strength or endurance adaptation [223].
This polarized approach to planning seems unnecessary
and counterintuitive for distance runners who generally
possess little ST experience, therefore require a minimal
stimulus to create an adaptation. Indeed, studies that
replaced running training with ST [73, 78, 88] found no
greater beneﬁt than those which included ST in a supple-
mentary manner.
4.10.4 Training Supervision
In most studies, the ST routine was supervised and tightly
monitored; however, similar controls were often absent for
the running training participants performed. It seems rea-
sonable to assume that any errors in participants training
logbooks would be similar across intervention and control
groups; however, validity of ﬁndings would be improved if
the running component of training had been more tightly
deﬁned. Where supervision of the ST exercises was not
included [76] or only included for the ﬁrst 2 weeks [36],
strength measures did not improve following the inter-
vention period. This indicates that a suitably qualiﬁed
coach is an important feature of an ST programme for a
distance runner who lacks ST experience.
4.11 Limitations
In addition to the limitations already highlighted in this
review,
there
are
other
weaknesses
that
should
be
acknowledged. For many of the studies reviewed, calcu-
lation of an ES was possible for the variables measured,
which provides insight into the meaningfulness and
1142
R. C. Blagrove et al.
123
substantiveness of results. However, despite the qualitative
nature of this review, interpretation of ﬁndings was pre-
dominantly based upon reported probability values, which
can be misleading due to low sample sizes and the
heterogeneity in the pool of participants studied. A rela-
tively large number of studies have been included in this
review; however, several parameters (e.g., v _VO2max and
BL) were measured in only a small number of studies,
which increases the possibility that false conclusions may
be drawn.
There was also a lack of detail concerning several
important confounding variables in studies, such as the
nature of running training prescription and participant’s
previous experience in ST. All but seven studies [31, 73,
74, 76, 84, 86, 90, 92] identiﬁed that participants had not
been engaged in a program of ST for at least 3 months prior
to the study commencing. Although it is perhaps unlikely
that participants in these seven studies were strength-
trained, this cannot be discounted and may therefore have
inﬂuenced ﬁndings in these investigations.
5 Conclusion and Future Research
This review is the most comprehensive to date surrounding
the potential impact of ST on the physiological determi-
nants of distance running. The research reviewed suggests
that supplementing the training of a distance runner with
ST is likely to provide improvements to RE, TT perfor-
mance and anaerobic parameters such as maximal sprint
speed. Improvements in RE in the absence of changes in
_VO2max, BL and body composition parameters suggests
that the underlying mechanisms predominantly relate to
alterations in intra-muscular co-ordination and increases in
tendon stiffness which contribute to optimizing force–
length-velocity properties of muscle. Nevertheless, it is
clear that the inclusion of ST does not adversely affect
_VO2max or BL markers. The addition of two to three
supervised ST sessions per week is likely to provide a
sufﬁcient stimulus to augment parameters within a 6- to
14-week period, and beneﬁts are likely to be larger for
interventions of a longer duration. A variety of ST
modalities can be used to achieve similar outcomes
assuming runners are of a non-strength trained status;
however, to maximize long-term adaptations, it is sug-
gested that a periodized approach is adopted with HRT
prioritized initially. Although changes in fat-free mass
were not observed in the majority of studies, a targeted RT
program, which aims to increase muscle mass speciﬁcally
around the proximal region of the lower limb may enhance
biomechanical and physiological factors which positively
inﬂuence RE.
A number of methodological issues are likely to have
contributed towards the discrepancies in results and should
be acknowledged in future research conducted in this area.
In particular, the measurement of RE should be quantiﬁed
as energy cost (rather than oxygen cost) and a variety of
speeds assessed which are relative to the maximum steady
state of each participant. Furthermore, when quantifying
RE and
_VO2max, differences in body size should be
accounted for by using scaling exponents which are
appropriate for the cohort under investigation. Although a
direct measure of v _VO2max has obvious validity, the dis-
crete increments utilized during a maximal test may not
provide the sensitivity required to detect changes which
exist in this parameter following a relatively short inter-
vention. Alternative strategies to quantifying v _VO2max may
provide a solution. It is therefore recommended that future
studies focus their time and efforts on investigating the
effects of ST on physiological variables other than _VO2max
and BL responses, such as RE, v _VO2max and parameters
associated with the critical power model. The nature of the
running training undertaken by participants and strength
training history potentially confounds the outcomes of
studies in this area, therefore attempts should also be made
to control these variables as much as possible.
Although the interference phenomenon is likely to have
blunted the strength adaptations observed, the extent to
which this occurs is currently uncertain due to the absence
of a strength-only training group in the studies reviewed.
For longer term interventions, where improvements inevi-
tably plateau, minimizing attenuation to strength outcomes
(and equally augmenting aerobic adaptation) potentially
becomes more important. Therefore the organization of ST
around running training provides a further avenue for
investigation. Similarly, it would be useful for practitioners
to understand the optimal sequencing of ST modalities
within a long-term program in order to optimize training
outcomes and facilitate a peaking response. Finally, very
few investigations have examined the effect of ST on
speciﬁc populations of runners such as young [78], female
[32, 38, 72], and masters’ age [86] competitors, therefore
future research should attempt to address this dearth in
literature.
Compliance with ethical standards
Conﬂict of interest Richard Blagrove, Glyn Howatson and Philip
Hayes declare that they have no conﬂict of interest. No funding was
provided to support the preparation of this manuscript.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
Effects of Strength Training on Distance Running
1143
123
link to the Creative Commons license, and indicate if changes were
made.
References
1. di Prampero PE, Atchou G, Bruckner JC, Moia C. The ener-
getics of endurance running. Euro J Appl Physiol Occ Physiol.
1986;55(3):259–66.
2. Joyner MJ. Modeling: optimal marathon performance on the
basis
of
physiological
factors.
J
Appl
Physiol.
1991;70(2):683–7.
3. McLaughlin JE, Howley ET, Bassett DR Jr, et al. Test of the
classic model for predicting endurance running performance.
Med Sci Sports Exerc. 2010;42(5):991–7.
4. Billat LV, Koralsztein JP. Signiﬁcance of the velocity at _VO2max
and
time
to
exhaustion
at
this
velocity.
Sports
Med.
1996;22(2):90–108.
5. Conley DL, Krahenbuhl GS. Running economy and distance
running performance of highly trained athletes. Med Sci Sports
Exerc. 1980;12(5):357–60.
6. Morgan DW, Craib M. Physiological aspects of running econ-
omy. Med Sci Sports Exerc. 1992;24(4):456–61.
7. Saunders PU, Pyne DB, Telford RD, Hawley JA. Factors
affecting running economy in trained distance runners. Sports
Med. 2004;34(7):465–85.
8. Svedenhag J, Sjodin B. Physiological characteristics of elite
male runners in and off-season. Can J Appl Sport Sci.
1985;10(3):127–33.
9. Jones AM, Carter H. The effect of endurance training on
parameters of aerobic ﬁtness. Sports Med. 2000;29(6):373–86.
10. Denadai BS, de Aguiar RA, de Lima LC, et al. Explosive
training and heavy weight training are effective for improving
running economy in endurance athletes: a systematic review and
meta-analysis. Sports Med. 2017;47(3):545–54.
11. Lacour JR, Padilla-Magunacelaya S, Barthelemy JC, Dormois
D. The energetics of middle-distance running. Euro J App
Physiol Occ Physiol. 1990;60(1):38–43.
12. Brandon LJ, Boileau RA. Inﬂuence of metabolic, mechanical
and physique variables on middle distance running. J Sports
Med Phys Fit. 1992;32(1):1–9.
13. Padilla S, Bourdin M, Barthelemy JC, Lacour JR. Physiological
correlates of middle-distance running performance. A compar-
ative study between men and women. Euro J App Physiol Occ
Physiol. 1992;65(6):561–6.
14. Brandon LJ. Physiological factors associated with middle dis-
tance running performance. Sports Med. 1995;19(4):268–77.
15. Abe D, Yanagawa K, Yamanobe K, Tamura K. Assessment of
middle-distance running performance in sub-elite young runners
using energy cost of running. Euro J App Physiol Occ Physiol.
1998;77(4):320–5.
16. Ingham SA, Whyte GP, Pedlar C, et al. Determinants of 800-m
and 1500-m running performance using allometric models. Med
Sci Sports Exerc. 2008;40(2):345–50.
17. Rabada´n M, Dı´az V, Caldero´n FJ, et al. Physiological deter-
minants of speciality of elite middle- and long-distance runners.
J Sports Sci. 2011;29(9):975–82.
18. Busso T, Chatagnon M. Modelling of aerobic and anaerobic
energy production in middle-distance running. Eur J App
Physiol. 2006;97(6):745–54.
19. Paavolainen L, Nummela A, Rusko H. Muscle power factors
_VO2maxand as determinants of horizontal and uphill running
performance. Scand J Med Sci Sports. 2000;10(5):286–91.
20. Houmard JA, Costill DL, Mitchell JB, et al. The role of
anaerobic ability in middle distance running performance. Euro
J App Physiol Occ Physiol. 1991;62(1):40–3.
21. Billat V, Renoux JC, Pinoteau J, et al. Reproducibility of run-
ning time to exhaustion at _VO2max in subelite runners. Med Sci
Sports Exerc. 1994;26(2):254–7.
22. Reardon J. Optimal pacing for running 400-and 800-m track
races. Am J Phys. 2013;81(6):428–35.
23. Beattie K, Kenny IC, Lyons M, Carson BP. The effect of
strength training on performance in endurance athletes. Sports
Med. 2014;44(6):845–65.
24. Moore IS. Is there an economical running technique? A review
of modiﬁable biomechanical factors affecting running economy.
Sports Med. 2016;46(6):793–807.
25. Fletcher JR, MacIntosh BR. Running economy from a muscle
energetics perspective. Front Physiol. 2017;8:433.
26. Balsalobre-Fernandez
C,
Santos-Concejero
J,
Grivas
GV.
Effects of strength training on running economy in highly
trained runners: a systematic review with meta-analysis of
controlled trials. J Strength Cond Res. 2016;30(8):2361–8.
27. Cavanagh PR, Pollock ML, Landa J. A biomechanical com-
parison of elite and good distance runners. Ann NY Acad Sci.
1977;301:328–45.
28. Coetzer P, Noakes TD, Sanders B, et al. Superior fatigue
resistance of elite black South African distance runners. J Appl
Physiol. 1993;75(4):1822–7.
29. Schoenfeld BJ, Ogborn D, Krieger JW. Effects of resistance
training frequency on measures of muscle hypertrophy: a sys-
tematic
review
and
meta-analysis.
Sports
Med.
2016;46(11):1689–97.
30. Wilson JM, Marin PJ, Rhea MR, et al. Concurrent training: a
meta-analysis examining interference of aerobic and resistance
exercises. J Strength Cond Res. 2012;26(8):2293–307.
31. Karsten B, Stevens L, Colpus M, et al. The effects of sport-
speciﬁc maximal strength and conditioning training on critical
velocity, anaerobic running distance, and 5-km race perfor-
mance. Int J Sports Physiol Perf. 2016;11(1):80–5.
32. Vikmoen O, Raastad T, Seynnes O, et al. Effects of heavy
strength training on running performance and determinants of
running performance in female endurance athletes. PLoS One.
2016;11(3):e0150799.
33. Beattie K, Carson BP, Lyons M, et al. The effect of strength
training on performance indicators in distance runners. J
Strength Cond Res. 2017;31(1):9–23.
34. Clark AW, Goedeke MK, Cunningham SR, et al. Effects of
pelvic and core strength training on high school cross-country
race times. J Strength Cond Res. 2017;31(8):2289–95.
35. Denadai BS, Greco CC. Resistance training and exercise toler-
ance during high-intensity exercise: moving beyond just running
economy and muscle strength. J Appl Physiol. 2017. https://doi.
org/10.1152/japplphysiol.00800.2017 (jap 00800 2017).
36. Giovanelli N, Taboga P, Rejc E, Lazzer S. Effects of strength,
explosive and plyometric training on energy cost of running in
ultra-endurance athletes. Eur J Sport Sci. 2017;17(7):805–13.
37. Stohanzl M, Balas J, Draper N. Effects of minimal dose of
strength training on running performance in female recreational
runners. J Sports Med Phys Fit. 2017. https://doi.org/10.23736/
s0022-4707.17.07124-9.
38. Vikmoen O, Ronnestad BR, Ellefsen S, Raastad T. Heavy
strength training improves running and cycling performance
following prolonged submaximal work in well-trained female
athletes. Physiol Rep. 2017; 5(5). https://doi.org/10.14814/phy2.
13149.
39. Berryman N, Mujika I, Arvisais D, et al. Strength training for
middle- and long-distance performance: a meta-analysis. Int J
1144
R. C. Blagrove et al.
123
Sports Physiol Perf. 2017; pp 1–27. https://doi.org/10.1123/
ijspp.2017-0032
40. Tanaka H, Swensen T. Impact of resistance training on endur-
ance performance. A new form of cross-training? Sports Med.
1998;25(3):191–200.
41. Jung AP. The impact of resistance training on distance running
performance. Sports Med. 2003;33(7):539–52.
42. Yamamoto LM, Lopez RM, Klau JF, et al. The effects of
resistance training on endurance distance running performance
among highly trained runners: a systematic review. J Strength
Cond Res. 2008;22(6):2036–44.
43. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items
for systematic reviews and meta-analyses: the PRISMA state-
ment. Bri Med J. 2009;339:b2535. https://doi.org/10.1136/bmj.
b2535.
44. Mayhew TP, Rothstein JM, Finucane SD, Lamb RL. Muscular
adaptation to concentric and eccentric exercise at equal power
levels. Med Sci Sports Exerc. 1995;27(6):868–73.
45. Baroni BM, Rodrigues R, Franke RA, et al. Time course of
neuromuscular adaptations to knee extensor eccentric training.
Int J Sports Med. 2013;34(10):904–11.
46. Jones AM. Middle- and long-distance running. In: Winter EM,
Jones AM, Davidson RC, et al., editors. Sport and exercise
physiology testing guidelines: volume i–sport testing: The Bri-
tish Association of Sport and Exercise Sciences Guide. London,
UK: Routledge; 2006. p. 152.
47. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Ray-
yan-a web and mobile app for systematic reviews. Syst Rev.
2016;5(1):210.
48. Hickson RC. Interference of strength development by simulta-
neously training for strength and endurance. Euro J Appl Physiol
Occ Physiol. 1980;45(2–3):255–63.
49. Hickson RC, Dvorak BA, Gorostiaga EM, et al. Potential for
strength and endurance training to amplify endurance perfor-
mance. J Appl Physiol. 1988;65(5):2285–90.
50. Spurrs R, Murphy A, Watsford M. Plyometric training improves
distance running performance: a case study. J Sci Med Sport.
2002;5(4):41.
51. Glowacki SP, Martin SE, Maurer A, et al. Effects of resistance,
endurance, and concurrent exercise on training outcomes in
men. Med Sci Sports Exerc. 2004;36(12):2119–27.
52. Saunders PU, Pyne DB, Telford RD, et al. Nine weeks of ply-
ometric training improves running economy in highly trained
distance runners. Med Sci Sports Exerc. 2004;36(5):S254.
53. Chtara M, Chamari K, Chaouachi M, et al. Effects of intra-
session concurrent endurance and strength training sequence on
aerobic
performance
and
capacity.
Brit
J
Sports
Med.
2005;39(8):555–60.
54. Hamilton RJ, Paton CD, Hopkins WG. Effect of high-intensity
resistance training on performance of competitive distance
runners. Int J Sports Physiol Perf. 2006;1(1):40–9.
55. Esteve-Lanao J, Rhea MR, Fleck SJ, Lucia A. Running-speciﬁc,
periodized strength training attenuates loss of stride length
during intense endurance running. J Strength Cond Res.
2008;22(4):1176–83.
56. Kelly CM, Burnett AF, Newton MJ. The effect of strength
training on three-kilometer performance in recreational women
endurance runners. J Strength Cond Res. 2008;22(2):396–403.
57. Guglielmo LG, Greco CC, Denadai BS. Effects of strength
training
on
running
economy.
Int
J
Sports
Med.
2009;30(1):27–32.
58. Sato K, Mokha M. Does core strength training inﬂuence running
kinetics, lower-extremity stability, and 5000-m performance in
runners? J Strength Cond Res. 2009;23(1):133–40.
59. Taipale RS, Mikkola J, Nummela A, et al. Strength training in
endurance runners. Int J Sports Med. 2010;31(7):468–76.
60. Childs D, Ryan M, Reneau P. The effects of core strength
training on maximal running performance in middle distance
running. Med Sci Sports Exerc. 2011;43(5):775.
61. Hasegawa H, Yamauchi T, Kawasaki T, et al. Effects of plyo-
metric training using a portable self-coaching system on running
performance and biomechanical variables in jump exercises. J
Strength Cond Res. 2011;25:S110–1.
62. Mikkola J, Vesterinen V, Taipale R, et al. Effect of resistance
training regimens on treadmill running and neuromuscular per-
formance in recreational endurance runners. J Sports Sci.
2011;29(13):1359–71.
63. Barnes KR, Hopkins WG, McGuigan MR, et al. Effects of
resistance training on running economy and cross-country per-
formance. Med Sci Sports Exerc. 2013;45(12):2322–31.
64. Sedano S, Marin PJ, Cuadrado G, Redondo JC. Concurrent
training in elite male runners: the inﬂuence of strength versus
muscular endurance training on performance outcomes. J
Strength Cond Res. 2013;27(9):2433–43.
65. Taipale RS, Mikkola J, Vesterinen V, et al. Neuromuscular
adaptations during combined strength and endurance training in
endurance runners: maximal versus explosive strength training
or a mix of both. Euro J Appl Physiol. 2013;113(2):325–35.
66. Mac´kała K, Stodo´łka J. Effects of explosive type strength
training on selected physical and technical performance char-
acteristics in middle distance running-a case report. Pol J Sport
Tourism. 2014;21(4):228–33.
67. Taipale RS, Mikkola J, Salo T, et al. Mixed maximal and
explosive strength training in recreational endurance runners. J
Strength Cond Res. 2014;28(3):689–99.
68. Bluett KA, De Ste Croix MB, Lloyd RS. A preliminary inves-
tigation into concurrent aerobic and resistance training in youth
runners. Isokinet Exerc Sci. 2015;23(2):77–85.
69. Roschel H, Barroso R, Tricoli V, et al. Effects of strength
training associated with whole-body vibration training on run-
ning economy and vertical stiffness. J Strength Cond Res.
2015;29(8):2215–20.
70. Tong TK, McConnell AK, Lin H, et al. Functional inspiratory
and core muscle training enhances running performance and
economy. J Strength Cond Res. 2016;30(10):2942–51.
71. Vorup J, Tybirk J, Gunnarsson TP, et al. Effect of speed
endurance and strength training on performance, running econ-
omy and muscular adaptations in endurance-trained runners.
Euro J Appl Physiol. 2016;116(7):1331–41.
72. Johnston RE, Quinn TJ, Kertzer R, Vroman NB. Strength
training in female distance runners: impact on running economy.
J Strength Cond Res. 1997;11(4):224–9.
73. Paavolainen L, Ha¨kkinen K, Ha¨ma¨la¨inen I, et al. Explosive-
strength training improves 5-km running time by improving
running
economy
and
muscle
power.
J
Appl
Phys.
1999;86(5):1527–33.
74. Millet GP, Jaouen B, Borrani F, Candau R. Effects of concurrent
endurance and strength training on running economy and VO(2)
kinetics. Med Sci Sports Exerc. 2002;34(8):1351–9.
75. Spurrs RW, Murphy AJ, Watsford ML. The effect of plyometric
training on distance running performance. Euro J Appl Physiol.
2003;89(1):1–7.
76. Turner AM, Owings M, Schwane JA. Improvement in running
economy after 6 weeks of plyometric training. J Strength Cond
Res. 2003;17(1):60–7.
77. Saunders PU, Telford RD, Pyne DB, et al. Short-term plyo-
metric training improves running economy in highly trained
middle and long distance runners. J Strength Cond Res.
2006;20(4):947–54.
78. Mikkola J, Rusko H, Nummela A, et al. Concurrent endurance
and explosive type strength training improves neuromuscular
Effects of Strength Training on Distance Running
1145
123
and anaerobic characteristics in young distance runners. Int J
Sports Med. 2007;28(7):602–11.
79. Storen O, Helgerud J, Stoa EM, Hoff J. Maximal strength
training improves running economy in distance runners. Med
Sci Sports Exerc. 2008;40(6):1087–92.
80. Berryman N, Maurel DB, Bosquet L. Effect of plyometric vs.
dynamic weight training on the energy cost of running. J
Strength Cond Res. 2010;24(7):1818–25.
81. Ferrauti A, Bergermann M, Fernandez-Fernandez J. Effects of a
concurrent strength and endurance training on running perfor-
mance and running economy in recreational marathon runners. J
Strength Cond Res. 2010;24(10):2770–8.
82. Fletcher JR, Esau SP, MacIntosh BR. Changes in tendon stiff-
ness and running economy in highly trained distance runners.
Euro J Appl Physiol. 2010;110(5):1037–46.
83. Bonacci J, Green D, Saunders PU, et al. Plyometric training as
an intervention to correct altered neuromotor control during
running after cycling in triathletes: a preliminary randomised
controlled trial. Phys Ther Sport. 2011;12(1):15–21.
84. Albracht K, Arampatzis A. Exercise-induced changes in triceps
surae tendon stiffness and muscle strength affect running
economy
in
humans.
Euro
J
Appl
Physiol.
2013;113(6):1605–15.
85. Bertuzzi R, Pasqua LA, Bueno S, et al. Strength-training with
whole-body vibration in long-distance runners: a randomized
trial. Int J Sports Med. 2013;34(10):917–23.
86. Piacentini MF, De Ioannon G, Comotto S, et al. Concurrent
strength and endurance training effects on running economy in
master
endurance
runners.
J
Strength
Cond
Res.
2013;27(8):2295–303.
87. Ramirez-Campillo R, Alvarez C, Henriquez-Olguin C, et al.
Effects of plyometric training on endurance and explosive
strength performance in competitive middle- and long-distance
runners. J Strength Cond Res. 2014;28(1):97–104.
88. Skovgaard C, Christensen PM, Larsen S, et al. Concurrent speed
endurance and resistance training improves performance, run-
ning economy, and muscle NHE1 in moderately trained runners.
J Appl Physiol. 2014;117(10):1097–109.
89. Damasceno MV, Lima-Silva AE, Pasqua LA, et al. Effects of
resistance training on neuromuscular characteristics and pacing
during 10-km running time trial. Euro J Appl Physiol.
2015;115(7):1513–22.
90. Schumann M, Mykkanen OP, Doma K, et al. Effects of endur-
ance training only versus same-session combined endurance and
strength training on physical performance and serum hormone
concentrations in recreational endurance runners. Appl Physiol
Nutr Metab. 2015;40(1):28–36.
91. Pellegrino J, Ruby BC, Dumke CL. Effect of plyometrics on the
energy cost of running and MHC and titin isoforms. Med Sci
Sports Exerc. 2016;48(1):49–56.
92. Schumann M, Pelttari P, Doma K, et al. Neuromuscular adap-
tations to same-session combined endurance and strength
training in recreational endurance runners. Int J Sports Med.
2016;37(14):1136–43.
93. Maher CG, Sherrington C, Herbert RD, et al. Reliability of the
PEDro scale for rating quality of randomized controlled trials.
Phys Ther. 2003;83(8):713–21.
94. Hoogkamer W, Kipp S, Spiering BA, Kram R. Altered running
economy directly translates to altered distance-running perfor-
mance. Med Sci Sports Exerc. 2016;48(11):2175–80.
95. Frederick EC, Daniels JT, Hayes JW. The effect of shoe weight
on the aerobic demands of running. In: Bachl N, Prokop L,
Suckert R, editors. Curr top sports med. Vienna: Urban &
Schwarzenberg; 1984. p. 616–25.
96. Morgan DW, Martin PE, Krahenbuhl GS, Baldini FD. Vari-
ability in running economy and mechanics among trained male
runners. Med Sci Sports Exerc. 1991;23(3):378–83.
97. Pereira MA, Freedson PS. Intraindividual variation of running
economy in highly trained and moderately trained males. Int J
Sports Med. 1997;18(2):118–24.
98. Saunders PU, Pyne DB, Telford RD, Hawley JA. Reliability and
variability of running economy in elite distance runners. Med
Sci Sports Exerc. 2004;36(11):1972–6.
99. Blagrove RC, Howatson G, Hayes PR. Test-retest reliability of
physiological parameters in elite junior distance runners fol-
lowing allometric scaling. Eur J Sport Sci. 2017; pp 1–10.
https://doi.org/10.1080/17461391.2017.1364301.
100. Shaw AJ, Ingham SA, Fudge BW, Folland JP. The reliability of
running economy expressed as oxygen cost and energy cost in
trained
distance
runners.
Appl
Physiol
Nutr
Metab.
2013;38(12):1268–72.
101. Rhea MR, Alderman BL. A meta-analysis of periodized versus
nonperiodized strength and power training programs. Res Q
Exerc Sport. 2004;75(4):413–22.
102. McCaw ST, Friday JJ. A comparison of muscle activity between
a free weight and a machine bench press. J Strength Cond Res.
1994;8:259–64.
103. Schwanbeck S, Chilibeck PD, Binsted G. A comparison of free
weight squat to Smith machine squat using electromyography. J
Strength Cond Res. 2009;23(9):2588–91.
104. Young WB. Transfer of strength and power training to sports
performance. Int J Sports Physiol Perf. 2006;1(2):74–83.
105. Cavagna GA, Kaneko M. Mechanical work and efﬁciency in
level walking and running. J Physiol. 1977;268(2):467–81.
106. Barnes KR, Kilding AE. Running economy: measurement,
norms,
and
determining
factors.
Sports
Med
Open.
2015;1(1):8–15.
107. Bergh U, Sjodin B, Forsberg A, Svedenhag J. The relationship
between body mass and oxygen uptake during running in
humans. Med Sci Sports Exerc. 1991;23(2):205–11.
108. Batterham AM, Jackson AS. Validity of the allometric cascade
model at submaximal and maximal metabolic rates in exercising
men. Respir Physiol Neurobiol. 2003;135(1):103–6.
109. Markovic G, Vucetic V, Nevill AM. Scaling behaviour of
_VO2max in athletes and untrained individuals. Ann Hum Biol.
2007;34(3):315–28.
110. Helgerud J. Maximal oxygen uptake, anaerobic threshold and
running economy in women and men with similar performances
level
in
marathons.
Eur
J
Appl
Physiol
Occ
Physiol.
1994;68(2):155–61.
111. Curran-Everett D. Explorations in statistics: the analysis of
ratios
and
normalized
data.
Adv
Physiol
Educ.
2013;37(3):213–9.
112. Fletcher JR, Esau SP, Macintosh BR. Economy of running:
beyond the measurement of oxygen uptake. J Appl Physiol.
2009;107(6):1918–22.
113. Shaw AJ, Ingham SA, Folland JP. The valid measurement of
running
economy
in
runners.
Med
Sci
Sports
Exerc.
2014;46(10):1968–73.
114. Bassett DR, Howley ET. Limiting factors for maximum oxygen
uptake and determinants of endurance performance. Med Sci
Sports Exerc. 2000;32(1):70–84.
115. Pate RR, Branch JD. Training for endurance sport. Med Sci
Sports Exerc. 1992;24(9 Suppl):S340–3.
116. Tesch PA, Komi PV, Hakkinen K. Enzymatic adaptations con-
sequent to long-term strength training. Int J Sports Med.
1987;8(1 Suppl):66–9.
117. Dudley GA. Metabolic consequences of resistive-type exercise.
Med Sci Sports Exerc. 1988;20(5 Suppl):S158–61.
1146
R. C. Blagrove et al.
123
118. Kraemer WJ, Fleck SJ, Evans WJ. Strength and power training:
physiological mechanisms of adaptation. Exerc Sport Sci Rev.
1996;24:363–97.
119. Hurley BF, Seals DR, Ehsani AA, et al. Effects of high-intensity
strength training on cardiovascular function. Med Sci Sports
Exerc. 1984;16(5):483–8.
120. Wenger HA, Bell GJ. The interactions of intensity, frequency
and duration of exercise training in altering cardiorespiratory
ﬁtness. Sports Med. 1986;3(5):346–56.
121. Martin DE, Vroon DH, May DF, Pilbeam SP. Physiological
changes in elite male distance runners training for Olympic
competition. Phys Sportsmed. 1986;14(1):152–206.
122. Morgan DW, Baldini FD, Martin PE, Kohrt WM. Ten kilometer
performance and predicted velocity
_VO2max at among well-
trained male runners. Med Sci Sports Exerc. 1989;21(1):78–83.
123. Midgley AW, McNaughton LR, Wilkinson M. Is there an
optimal training intensity for enhancing the maximal oxygen
uptake of distance runners?: empirical research ﬁndings, current
opinions, physiological rationale and practical recommenda-
tions. Sports Med. 2006;36(2):117–32.
124. Paavolainen LM, Nummela AT, Rusko HK. Neuromuscular
characteristics and muscle power as determinants of 5-km run-
ning performance. Med Sci Sports Exerc. 1999;31(1):124–30.
125. Noakes TD, Myburgh KH, Schall R. Peak treadmill running
velocity during the _VO2max test predicts running performance. J
Sports Sci. 1990;8(1):35–45.
126. Stratton E, O’Brien BJ, Harvey J, et al. Treadmill velocity best
predicts
5000-m
run
performance.
Int
J
Sports
Med.
2009;30(1):40–5.
127. Noakes TD. Implications of exercise testing for prediction of
athletic performance: a contemporary perspective. Med Sci
Sports Exerc. 1988;20(4):319–30.
128. Daniels JT, Scardina N, Hayes J, et al. Elite and subelite female
middle-and long-distance runners. In: Landers DM, editor. Sport
and elite performers. Champaign: Human Kinetics; 1984. p. 57–72.
129. Denadai BS, Greco CC. Can the critical power model explain
the increased peak velocity/power during incremental test after
concurrent strength and endurance training? J Strength Cond
Res. 2017;31(8):2319–23.
130. Jones AM, Vanhatalo A, Burnley M, et al. Critical power:
implications for determination of and exercise tolerance. Med
Sci Sports Exerc. 2010;42(10):1876–90.
131. Bosquet L, Duchene A, Lecot F, et al. Vmax estimate from
three-parameter critical velocity models: validity and impact on
800 m running performance prediction. Euro J Appl Physiol.
2006;97(1):34–42.
132. Bishop D, Jenkins DG. The inﬂuence of resistance training on
the critical power function and time to fatigue at critical power.
Aust J Sci Med Sport. 1996;28(4):101–5.
133. Sawyer BJ, Stokes DG, Womack CJ, et al. Strength training
increases endurance time to exhaustion during high-intensity
exercise despite no change in critical power. J Strength Cond
Res. 2014;28(3):601–9.
134. Farrell PA, Wilmore JH, Coyle EF, et al. Plasma lactate accu-
mulation and distance running performance. Med Sci Sports.
1979;11(4):338–44.
135. Fay L, Londeree BR, LaFontaine TP, Volek MR. Physiological
parameters related to distance running performance in female
athletes. Med Sci Sports Exerc. 1989;21(3):319–24.
136. Yoshida T, Udo M, Iwai K, Yamaguchi T. Physiological char-
acteristics related to endurance running performance in female
distance runners. J Sports Sci. 1993;11(1):57–62.
137. Tanaka K, Matsuura Y, Matsuzaka A, et al. A longitudinal
assessment of anaerobic threshold and distance-running perfor-
mance. Med Sci Sports Exerc. 1984;16(3):278–82.
138. Carter H, Jones AM, Doust JH. Effect of 6 weeks of endurance
training
on
the
lactate
minimum
speed.
J
Sports
Sci.
1999;17(12):957–67.
139. Billat V, Sirvent P, Lepretre PM, Koralsztein JP. Training effect
on performance, substrate balance and blood lactate concentra-
tion at maximal lactate steady state in master endurance-runners.
Pﬂugers Arch. 2004;447(6):875–83.
140. Londeree BR. Effect of training on lactate/ventilatory thresh-
olds:
a
meta-analysis.
Med
Sci
Sports
Exerc.
1997;29(6):837–43.
141. Jacobs I, Esbjornsson M, Sylven C, et al. Sprint training effects
on muscle myoglobin, enzymes, ﬁber types, and blood lactate.
Med Sci Sports Exerc. 1987;19(4):368–74.
142. Harmer AR, McKenna MJ, Sutton JR, et al. Skeletal muscle
metabolic and ionic adaptations during intense exercise fol-
lowing
sprint
training
in
humans.
J
Appl
Physiol.
2000;89(5):1793–803.
143. Burgomaster KA, Heigenhauser GJ, Gibala MJ. Effect of short-
term sprint interval training on human skeletal muscle carbo-
hydrate metabolism during exercise and time-trial performance.
J Appl Physiol. 2006;100(6):2041–7.
144. Marcinik EJ, Potts J, Schlabach G, et al. Effects of strength
training on lactate threshold and endurance performance. Med
Sci Sports Exerc. 1991;23(6):739–43.
145. Hayes PR, Bowen SJ, Davies EJ. The relationships between
local muscular endurance and kinematic changes during a run to
exhaustion
at
v
_VO2max.
J
Strength
Cond
Res.
2004;18(4):898–903.
146. Baar K. Using molecular biology to maximize concurrent
training. Sports Med. 2014;44(Suppl 2):S117–25.
147. Coffey VG, Pilegaard H, Garnham AP, et al. Consecutive bouts
of diverse contractile activity alter acute responses in human
skeletal muscle. J Appl Physiol. 2009;106(4):1187–97.
148. Wang L, Mascher H, Psilander N, et al. Resistance exercise
enhances the molecular signaling of mitochondrial biogenesis
induced by endurance exercise in human skeletal muscle. J Appl
Physiol. 2011;111(5):1335–44.
149. Green HJ, Patla AE. Maximal aerobic power: neuromuscular
and
metabolic
considerations.
Med
Sci
Sports
Exerc.
1992;24(1):38–46.
150. Bulbulian R, Wilcox AR, Darabos BL. Anaerobic contribution
to distance running performance of trained cross-country ath-
letes. Med Sci Sports Exerc. 1986;18(1):107–13.
151. Blazevich AJ, Jenkins DG. Effect of the movement speed of
resistance training exercises on sprint and strength performance
in concurrently training elite junior sprinters. J Sports Sci.
2002;20(12):981–90.
152. Satkunskiene D, Rauktys D. Stanislovaitis A The effect of
power training on sprint running kinematics. Educ Physical
Train Sport. 2009;72:116–22.
153. Kamandulis S, Skurvydas A, Brazaitis M, et al. Effect of a
periodized power training program on the functional perfor-
mances and contractile properties of the quadriceps in sprinters.
Res Q Exerc Sport. 2012;83(4):540–5.
154. Tucker R, Lambert MI, Noakes TD. An analysis of pacing
strategies during men’s world-record performances in track
athletics. Int J Sports Physiol Perf. 2006;1(3):233–45.
155. Hanley B. Senior men’s pacing proﬁles at the IAAF World
Cross
Country
Championships.
J
Sports
Sci.
2014;32(11):1060–5.
156. Hanley B. Pacing proﬁles and pack running at the IAAF World
Half
Marathon
Championships.
J
Sports
Sci.
2015;33(11):1189–95.
157. Sandford GN, Pearson S, Allen SV, et al. Tactical behaviours in
men’s 800 m Olympic and World Championship medallists: a
Effects of Strength Training on Distance Running
1147
123
changing of the guard. Int J Sports Physiol Perform. 2017; pp
1–13. https://doi.org/10.1123/ijspp.2016-0780.
158. Turnes T, Salvador AF, Lisboa FD, et al. A fast-start pacing
strategy speeds pulmonary oxygen uptake kinetics and improves
supramaximal
running
performance.
PLoS
One.
2014;9(10):e111621.
159. Bundle MW, Hoyt RW, Weyand PG. High-speed running per-
formance: a new approach to assessment and prediction. J Appl
Physiol. 2003;95(5):1955–62.
160. Vanhatalo A, Jones AM, Burnley M. Application of critical
power in sport. Int J Sports Physiol Perform. 2011;6(1):128–36.
161. Jenkins DG, Quigley BM. Endurance training enhances critical
power. Med Sci Sports Exerc. 1992;24(11):1283–9.
162. Vanhatalo A, Doust JH, Burnley M. A 3-min all-out cycling test
is sensitive to a change in critical power. Med Sci Sports Exerc.
2008;40(9):1693–9.
163. Rusko HK. Measurement of maximal and submaximal anaero-
bic power: an introduction. Int J Sports Med. 1996;17(Suppl
2):S89–90.
164. Folland JP, Williams AG. The adaptations to strength training:
morphological and neurological contributions to increased
strength. Sports Med. 2007;37(2):145–68.
165. Sale DG. Neural adaptation to resistance training. Med Sci
Sports Exerc. 1988;20(5 Suppl):S135–45.
166. Sale D, MacDougall J, Jacobs I, Garner S. Interaction between
concurrent strength and endurance training. J App Physiol.
1990;68(1):260–70.
167. Kraemer WJ, Patton JF, Gordon SE, et al. Compatibility of high-
intensity strength and endurance training on hormonal and
skeletal muscle adaptations. J Appl Physiol. 1995;78(3):976–89.
168. McCarthy JP, Agre JC, Graf BK, et al. Compatibility of adaptive
responses with combining strength and endurance training. Med
Sci Sports Exerc. 1995;27(3):429–36.
169. McCarthy JP, Pozniak MA, Agre JC. Neuromuscular adapta-
tions to concurrent strength and endurance training. Med Sci
Sports Exerc. 2002;34(3):511–9.
170. Rønnestad BR, Hansen EA, Raastad T. High volume of endur-
ance training impairs adaptations to 12 weeks of strength
training in well-trained endurance athletes. Eur J Appl Physiol.
2012;112(4):1457–66.
171. Lundberg TR, Fernandez-Gonzalo R, Gustafsson T, Tesch PA.
Aerobic exercise does not compromise muscle hypertrophy
response to short-term resistance training. J Appl Physiol.
2013;114(1):81–9.
172. Hennessy LC, Watson AW. The interference effects of training
for strength and endurance simultaneously. J Strength Cond Res.
1994;8(1):12–9.
173. Chtara M, Chaouachi A, Levin GT, et al. Effect of concurrent
endurance and circuit resistance training sequence on muscular
strength
and
power
development.
J
Strength
Cond
Res.
2008;22(4):1037–45.
174. Hakkinen K. Neuromuscular and hormonal adaptations during
strength and power training. A review. J Sports Med Phys Fit.
1989;29(1):9–26.
175. Yang Y, Creer A, Jemiolo B, Trappe S. Time course of myo-
genic and metabolic gene expression in response to acute
exercise
in
human
skeletal
muscle.
J
Appl
Physiol.
2005;98(5):1745–52.
176. Spiering BA, Kraemer WJ, Anderson JM, et al. Resistance
exercise biology: manipulation of resistance exercise pro-
gramme variables determines the responses of cellular and
molecular signalling pathways. Sports Med. 2008;38(7):527–40.
177. Glass DJ. Skeletal muscle hypertrophy and atrophy signaling
pathways. Int J Biochem Cell Biol. 2005;37(10):1974–84.
178. Song Y-H, Godard M, Li Y, et al. Insulin-like growth factor
I-mediated skeletal muscle hypertrophy is characterized by
increased mTOR-p70S6 K signaling without increased Akt
phosphorylation. J Investig Med. 2005;53(3):135–42.
179. Vary TC. IGF-I stimulates protein synthesis in skeletal muscle
through multiple signaling pathways during sepsis. Am J Physiol
Regul Integr Comp Physiol. 2006;290(2):R313–21.
180. Bodine SC. mTOR signaling and the molecular adaptation to
resistance exercise. Med Sci Sports Exerc. 2006;38(11):1950–7.
181. Irrcher I, Adhihetty PJ, Joseph AM, et al. Regulation of mito-
chondrial biogenesis in muscle by endurance exercise. Sports
Med. 2003;33(11):783–93.
182. Hardie DG, Sakamoto K. AMPK: a key sensor of fuel and
energy status in skeletal muscle. Physiol. 2006;21:48–60.
183. Horman S, Browne G, Krause U, et al. Activation of AMP-
activated protein kinase leads to the phosphorylation of elon-
gation factor 2 and an inhibition of protein synthesis. Curr Biol.
2002;12(16):1419–23.
184. Rose AJ, Hargreaves M. Exercise increases Ca2 ? -calmodulin-
dependent protein kinase II activity in human skeletal muscle. J
Physiol. 2003;553(Pt 1):303–9.
185. Baar K. Training for endurance and strength: lessons from cell
signaling. Med Sci Sports Exerc. 2006;38(11):1939–44.
186. Nader GA. Concurrent strength and endurance training: from
molecules to man. Med Sci Sports Exerc. 2006;38(11):1965–70.
187. Dalleau G, Belli A, Bourdin M, Lacour JR. The spring-mass
model and the energy cost of treadmill running. Eur J Appl
Physiol Occup Physiol. 1998;77(3):257–63.
188. Arampatzis A, De Monte G, Karamanidis K, et al. Inﬂuence of
the muscle-tendon unit’s mechanical and morphological prop-
erties
on
running
economy.
J
Exp
Biol.
2006;209(Pt
17):3345–57.
189. Dumke CL, Pfaffenroth CM, McBride JM, McCauley GO.
Relationship between muscle strength, power and stiffness and
running economy in trained male runners. Int J Sports Physiol
Perform. 2010;5(2):249–61.
190. Kubo K, Kanehisa H, Fukunaga T. Effects of resistance and
stretching training programmes on the viscoelastic properties of
human tendon structures in vivo. J Physiol. 2002;538(Pt
1):219–26.
191. Foure A, Nordez A, Cornu C. Plyometric training effects on
Achilles tendon stiffness and dissipative properties. J Appl
Physiol. 2010;109(3):849–54.
192. Kyrolainen H, Belli A, Komi PV. Biomechanical factors
affecting
running
economy.
Med
Sci
Sports
Exerc.
2001;33(8):1330–7.
193. Fletcher JR, Groves EM, Pﬁster TR, Macintosh BR. Can muscle
shortening alone, explain the energy cost of muscle contraction
in vivo? Eur J Appl Physiol. 2013;113(9):2313–22.
194. Fletcher JR, MacIntosh BR. Achilles tendon strain energy in
distance running: consider the muscle energy cost. J Appl
Physiol. 2015;118(2):193–9.
195. Staron RS, Malicky ES, Leonardi MJ, et al. Muscle hypertrophy
and fast ﬁber type conversions in heavy resistance-trained
women. Eur J Appl Physiol Occup Physiol. 1990;60(1):71–9.
196. Staron RS, Karapondo DL, Kraemer WJ, et al. Skeletal muscle
adaptations during early phase of heavy-resistance training in
men and women. J Appl Physiol. 1994;76(3):1247–55.
197. Kyrolainen H, Kivela R, Koskinen S, et al. Interrelationships
between muscle structure, muscle strength, and running econ-
omy. Med Sci Sports Exerc. 2003;35(1):45–9.
198. Hunter GR, McCarthy JP, Carter SJ, et al. Muscle ﬁber type,
Achilles tendon length, potentiation, and running economy. J
Strength Cond Res. 2015;29(5):1302–9.
199. Iaia FM, Hellsten Y, Nielsen JJ, et al. Four weeks of speed
endurance training reduces energy expenditure during exercise
and maintains muscle oxidative capacity despite a reduction in
training volume. J Appl Physiol. 2009;106(1):73–80.
1148
R. C. Blagrove et al.
123
200. Williams KR, Cavanagh PR. Relationship between distance
running mechanics, running economy, and performance. J Appl
Physiol. 1987;63(3):1236–45.
201. Folland JP, Allen SJ, Black MI, et al. Running technique is an
important component of running economy and performance.
Med Sci Sports Exerc. 2017;49(7):1412–23.
202. Snyder KR, Earl JE, O’Connor KM, Ebersole KT. Resistance
training is accompanied by increases in hip strength and changes
in lower extremity biomechanics during running. Clin Biomech.
2009;24(1):26–34.
203. Chapman RF, Laymon AS, Wilhite DP, et al. Ground contact
time as an indicator of metabolic cost in elite distance runners.
Med Sci Sports Exerc. 2012;44(5):917–25.
204. Di Michele R, Merni F. The concurrent effects of strike pattern
and ground-contact time on running economy. J Sci Med Sport.
2014;17(4):414–8.
205. Crewther B, Cronin J, Keogh J. Possible stimuli for strength and
power adaptation: acute mechanical responses. Sports Med.
2005;35(11):967–89.
206. Markovic G, Mikulic P. Neuro-musculoskeletal and perfor-
mance adaptations to lower-extremity plyometric training.
Sports Med. 2010;40(10):859–95.
207. Cormie P, McGuigan MR, Newton RU. Adaptations in athletic
performance after ballistic power versus strength training. Med
Sci Sports Exerc. 2010;42(8):1582–98.
208. Lauersen JB, Bertelsen DM, Andersen LB. The effectiveness of
exercise interventions to prevent sports injuries: a systematic
review and meta-analysis of randomised controlled trials. Br J
Sports Med. 2014;48(11):871–7.
209. Cormie P, McGuigan MR, Newton RU. Inﬂuence of strength on
magnitude and mechanisms of adaptation to power training.
Med Sci Sports Exerc. 2010;42(8):1566–81.
210. Gamble P. Implications and applications of training speciﬁcity
for coaches and athletes. Strength Cond J. 2006;28(3):54.
211. Hayes P, Caplan N. Foot strike patterns and ground contact
times during high-calibre middle-distance races. J Sports Sci.
2012;30(12):1275–83.
212. Hasegawa H, Yamauchi T, Kraemer WJ. Foot strike patterns of
runners at the 15-km point during an elite-level half marathon. J
Strength Cond Res. 2007;21(3):888.
213. Kraemer WJ, Ratamess NA. Fundamentals of resistance train-
ing: progression and exercise prescription. Med Sci Sports
Exerc. 2004;36(4):674–88.
214. Izquierdo M, Ibanez J, Gonzalez-Badillo JJ, et al. Differential
effects of strength training leading to failure versus not to failure
on hormonal responses, strength, and muscle power gains. J
Appl Physiol. 2006;100(5):1647–56.
215. Morton RW, Oikawa SY, Wavell CG, et al. Neither load nor
systemic
hormones
determine
resistance
training-mediated
hypertrophy or strength gains in resistance-trained young men. J
Appl Physiol. 2016;121(1):129–38.
216. Davies T, Orr R, Halaki M, Hackett D. Effect of training leading
to repetition failure on muscular strength: a systematic review
and meta-analysis. Sports Med. 2016;46(4):487–502.
217. Pareja-Blanco F, Rodriguez-Rosell D, Sanchez-Medina L, et al.
Effect of movement velocity during resistance training on neu-
romuscular performance. Int J Sports Med. 2014;35(11):916–24.
218. Rhea MR, Alvar BA, Burkett LN, Ball SD. A meta-analysis to
determine the dose response for strength development. Med Sci
Sports Exerc. 2003;35(3):456–64.
219. Doma K, Deakin GB, Bentley DJ. Implications of impaired
endurance performance following single bouts of resistance
training: an alternate concurrent training perspective. Sports
Med. 2017. https://doi.org/10.1007/s40279-017-0758-3.
220. Rønnestad BR, Hansen EA, Raastad T. In-season strength
maintenance training increases well-trained cyclists’ perfor-
mance. Eur J Appl Physiol. 2010;110(6):1269–82.
221. Rønnestad BR, Hansen J, Hollan I, Ellefsen S. Strength training
improves performance and pedaling characteristics in elite
cyclists. Scand J Med Sci Sports. 2015;25(1):e89–98.
222. Rønnestad BR, Nymark BS, Raastad T. Effects of in-season
strength maintenance training frequency in professional soccer
players. J Strength Cond Res. 2011;25(10):2653–60.
223. Garcia-Pallares J, Izquierdo M. Strategies to optimize concur-
rent training of strength and aerobic ﬁtness for rowing and
canoeing. Sports Med. 2011;41(4):329–43.
Effects of Strength Training on Distance Running
1149
123

Effects of Strength Training on the Physiological Determinants of Middle- and Long-Distance Running Performance: A Systematic Review.
Blagrove, Richard C,Howatson, Glyn,Hayes, Philip R