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Vol.:(0123456789)
Sports Medicine (2020) 50:1813–1827
https://doi.org/10.1007/s40279-020-01319-3
SYSTEMATIC REVIEW
The Eects ofMenstrual Cycle Phase onExercise Performance
inEumenorrheic Women: ASystematic Review andMeta‑Analysis
KellyLeeMcNulty1 · KirstyJayneElliott‑Sale2 · EimearDolan3 · PaulAlanSwinton4 · PaulAnsdell1 ·
StuartGoodall1 · KevinThomas1 · KirstyMarieHicks1
Published online: 13 July 2020
© The Author(s) 2020
Abstract
Background Concentrations of endogenous sex hormones fluctuate across the menstrual cycle (MC), which could have
implications for exercise performance in women. At present, data are conflicting, with no consensus on whether exercise
performance is affected by MC phase.
Objective To determine the effects of the MC on exercise performance and provide evidence-based, practical, performance
recommendations to eumenorrheic women.
Methods This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide-
lines. Four databases were searched for published experimental studies that investigated the effects of the MC on exercise
performance, which included at least one outcome measure taken in two or more defined MC phases. All data were meta-
analysed using multilevel models grounded in Bayesian principles. The initial meta-analysis pooled pairwise effect sizes
comparing exercise performance during the early follicular phase with all other phases (late follicular, ovulation, early
luteal, mid-luteal and late luteal) amalgamated. A more comprehensive analysis was then conducted, comparing exercise
performance between all phases with direct and indirect pairwise effect sizes through a network meta-analysis. Results from
the network meta-analysis were summarised by calculating the Surface Under the Cumulative Ranking curve (SUCRA).
Study quality was assessed using a modified Downs and Black checklist and a strategy based on the recommendations of the
Grading of Recommendations Assessment Development and Evaluation (GRADE) working group.
Results Of the 78 included studies, data from 51 studies were eligible for inclusion in the initial pairwise meta-analysis.
The three-level hierarchical model indicated a trivial effect for both endurance- and strength-based outcomes, with reduced
exercise performance observed in the early follicular phase of the MC, based on the median pooled effect size (ES0.5 = − 0.06
[95% credible interval (CrI): −0.16 to 0.04]). Seventy-three studies had enough data to be included in the network meta-
analysis. The largest effect was identified between the early follicular and the late follicular phases of the MC (ES0.5 = − 0.14
[95% CrI: −0.26 to −0.03]). The lowest SUCRA value, which represents the likelihood that exercise performance is poor,
or among the poorest, relative to other MC phases, was obtained for the early follicular phase (30%), with values for all other
phases ranging between 53 and 55%. The quality of evidence for this review was classified as “low” (42%).
Conclusion The results from this systematic review and meta-analysis indicate that exercise performance might be trivially
reduced during the early follicular phase of the MC, compared to all other phases. Due to the trivial effect size, the large
between-study variation and the number of poor-quality studies included in this review, general guidelines on exercise per-
formance across the MC cannot be formed; rather, it is recommended that a personalised approach should be taken based
on each individual’s response to exercise performance across the MC.
Kelly Lee McNulty and Kirsty Jayne Elliott-Sale: Joint first
authors.
Electronic supplementary material The online version of this
article (https ://doi.org/10.1007/s4027 9-020-01319 -3) contains
supplementary material, which is available to authorized users.
Extended author information available on the last page of the article
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1814 K.L.McNulty et al.
Key Points
In women, exercise performance might be reduced by
a trivial amount during the early follicular phase of
the menstrual cycle when compared with other phases.
However, large between-study variance was identified,
indicating that research design, participant characteris-
tics and choice of outcome measure might influence any
group-level effect.
Practically, the current evidence does not warrant general
guidance on modulating exercise across the menstrual
cycle. As such, we recommend that a personalised
approach should be taken based on each individual’s
response to exercise performance across the menstrual
cycle.
The quality of evidence for this review was mostly
classified as “low” quality, which can be attributed to a
range of methodological issues. Future studies need to
improve methodological quality and limit confounders
to facilitate a deeper understanding of the effects of the
menstrual cycle on exercise performance.
1 Background
Over the last three decades, there has been a rise in the num-
ber of women participating in exercise, from physical activ-
ity to elite sport, attributable to the increasing development
of, and investment in, women’s professional sport [1–4].
Specifically, the percentage of women competing at the
Olympic Games has increased from 26% in Seoul in 1988 to
45% in Rio de Janeiro in 2016 [5]. Furthermore, Tokyo 2021
is set to be the most sex-balanced Games in history, with
the same number of medals available for men and women,
which is projected to see women participation in the Games
rise to 49% [5]. Performance-based research in women has
not kept pace with the exponential rise in participation [6,
7]. Indeed, it would be naive to assume that all research in
men can be directly applied to women, given the anatomical,
physiological and endocrinological differences between the
sexes [4, 8–10]. As such, sportswomen will benefit from sex-
specific research and guidelines, which consider the effects
of women’s physiology, such as the menstrual cycle (MC),
on performance [8, 11].
The MC is an important biological rhythm, whereby
large cyclic fluctuations in endogenous sex hormones, such
as oestrogen and progesterone, are observed [12–14]. The
fairly predictable (and measurable) fluctuations in oestrogen
and progesterone across the MC create significantly differ-
ent transient hormonal profiles, which are used to differ-
entiate between MC phases [15, 16]. As such, the MC is
commonly divided into three phases, (1) the early follicular
phase, characterised by low oestrogen and progesterone, (2)
the ovulatory phase, characterised by high oestrogen and
low progesterone, and (3) the mid-luteal phase, character-
ised by high oestrogen and progesterone [17]. Although the
primary function of these hormones is to support reproduc-
tion, research has highlighted that the changing concentra-
tions of oestrogen and progesterone across the MC also
exert a myriad of diverse and complex effects on multiple
physiological systems, including cardiovascular, respiratory,
metabolic and neuromuscular parameters [12, 18, 19], which
could have subsequent implications for exercise performance
[15, 20–23].
There are a range of suggested mechanisms by which the
cyclical fluctuations in oestrogen and progesterone across
the MC might affect performance. Specifically, oestrogen is
thought to have an anabolic effect on skeletal muscle [24,
25] and has been shown to play a role in substrate metabo-
lism changes through increased muscle glycogen storage
and increased fat utilisation [26]. Additionally, progesterone
is thought to have anti-oestrogenic effects [21]. As such,
it is plausible that changes in exercise performance might
be observed due to the different hormonal profiles across
the MC [15, 20–23]. To date, the effects of fluctuations in
oestrogen and progesterone across the MC on exercise per-
formance are conflicting, with studies reporting improved
performance outcomes during the early follicular [27–29],
ovulatory [30] and mid-luteal [31, 32] phases; whereas, oth-
ers have shown no changes in exercise performance between
MC phases [33–39]. Therefore, it is evident that a consensus
is yet to be reached regarding the effects of the MC on exer-
cise performance. Subsequently, no evidence-based guide-
lines for managing exercise performance across the MC cur-
rently exist for either exercising women, nor for practitioners
working with elite sportswomen.
Given the recent increase in the number of women par-
ticipating in exercise and the lack of consensus regarding
the effects of the MC on exercise performance, there is a
growing need to determine the effects of the fluctuations in
oestrogen and progesterone across the MC on exercise per-
formance. To our knowledge, this is the first meta-analysis
to critically examine existing studies investigating changes
in exercise performance across the MC, in eumenorrheic
women. Additionally, this review is the first of its kind to
appraise the quality of previous studies using robust assur-
ance tools. The information provided by this meta-analysis
can be used to inform practical recommendations for ath-
letes, practitioners and researchers interested in managing
exercise performance across the MC.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1815
Menstrual Cycle Phase and Exercise Performance
2 Methods
This review conforms to the PRISMA statement guidelines
(see Electronic Supplementary Material Appendix S1) [40].
2.1 Study Inclusion andExclusion Criteria
Consideration of Population, Intervention, Comparator, Out-
comes and Study design (PICOS) was used to determine the
parameters within which the review was conducted:
2.1.1 Population
Participants included healthy women who were (a) between
the ages of 18 and 40years; (b) eumenorrheic; (c) not taking
any hormonal contraceptives or medication known to affect
the hypothalamic–pituitary–ovarian (HPO) axis; (d) free
from any menstrual-related dysfunctions (such as, amenor-
rhea) or any other conditions (e.g., pregnancy, eating disor-
ders or disordered eating) known to affect the HPO axis; and
(e) free from any injury that would affect participation. No
restrictions were placed on activity level or training status.
2.1.2 Intervention
No specific intervention was investigated, but all participants
were required to have a normal MC, defined as having a
minimum of nine cycles per calendar year and a MC that
ranged between 21 and 35days in length.
2.1.3 Comparator
Comparisons were made between the early follicular phase
(acting as a ‘control’ phase) of the MC and all other MC
phases, in line with the following predetermined MC phase
classification as shown in Fig.1: early follicular (days 1–5),
late follicular (days 6–12), ovulation (days 13–15), early
luteal (days 16–19), mid-luteal (days 20– 23) and late luteal
(days 24-28).
2.1.4 Outcomes
The primary outcome was exercise test performance. For
the purposes of this review, exercise test performance was
defined as total work done, time to completion, time to
exhaustion, mean, peak and ratio outputs, rate of force pro-
duction and decline, and indices of fatigue. Although maxi-
mum oxygen uptake (maximal [
̇
V
O2max] or peak [
̇
V
O2peak])
is not a performance test, this physiology-based outcome
was included as it can be used as an indicator of perfor-
mance. A full list of considered outcomes can be found in
Electronic Supplementary Material Appendix S2. Perfor-
mance outcome data were allocated into broad categories
to allow for subgroup analysis; namely endurance (power
and capacity) and strength (maximal expression of force
and rate of force development). All exercise outcomes were
extracted, and effect size duplication of multiple outcomes
from the same test accounted for within the statistical analy-
sis, as described below.
2.1.5 Study Design
Experimental studies were considered for analysis if they
met the following inclusion criteria: (a) published, in full,
Fig. 1 Schematic displaying the hormonal fluctuations across an idealised 28-day menstrual cycle, with ovulation occurring on day 14 Adapted
from Pitchers and Elliott-Sale [8]
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1816 K.L.McNulty et al.
in a peer-reviewed journal, (b) had the primary or second-
ary objective of assessing changes in exercise performance
across the MC, (c) included within-group comparisons and
(d) outcome measure(s) were taken in two or more defined
MC phases. As such, case studies, review articles, study pro-
tocol papers and conference abstracts were excluded. Moreo-
ver, only full texts that were published in English or had an
existing translation were retrieved and examined. There was
no limit on date of publication.
2.2 Search Strategy forIdentication ofStudies
A systematic electronic literature search was conducted
by KLM to identify all relevant articles using four online
databases (PubMed, CENTRAL, SPORTDiscus and Pro-
Quest). The searches were performed using medical subject
headings terms, free-text and thesaurus terms, as well as,
keywords from existing relevant papers [15, 20–23]. The
following search terms and their combinations were used:
(‘menstrual cycle’, OR ‘menstrual phase’, OR ‘follicular
phase’, OR ‘luteal phase’) AND (‘strength’, OR ‘power’,
OR ‘torque’, OR ‘force’, OR ‘neuromuscular’, OR ‘max*
voluntary contraction’, OR ‘isometric’, OR ‘isokinetic’, OR
‘skeletal muscle’ OR ‘muscular performance’, OR ‘aero-
bic’, OR ‘aerobic power’, OR ‘aerobic capacity’, OR ‘endur-
ance’, OR ‘endurance power’, OR ‘endurance capacity’, OR
‘anaerobic’, OR ‘anaerobic power’, OR ‘anaerobic capacity’,
OR ‘athletic performance’, OR ‘sports performance’). An
example of a full electronic search for one database (Pub-
Med: 14/01/2019) is presented in Electronic Supplementary
Material Appendix S3. Databases were searched from incep-
tion until February 2019. The reference lists of obtained
relevant articles and review articles were hand-searched to
identify any further studies and were added in manually.
Following the same search criteria and strategy, an updated
electronic and manual hand-search for relevant literature was
subsequently conducted in April 2020 to identify any further
articles published between February 2019 and April 2020.
2.3 Data Selection, Extraction andStudy Quality
Assessment
2.3.1 Selection ofStudies
Three reviewers (KLM, KMH and KES) independently
reviewed the titles, abstracts and full-text paper of the iden-
tified articles for inclusion and any duplicates were removed,
using Covidence systematic review software (v1251, Veritas
Health Innovation, Australia). All searches followed a two-
phase screening strategy. Phase one assessed the eligibility
of the title and abstract of every manuscript generated from
the electronic searches and hand-searching against the prede-
termined inclusion and exclusion criteria. Studies that either
clearly did not meet the inclusion criteria or met at least one
exclusion criterion were excluded at this phase. In phase
two, the full-text paper was retrieved for the articles identi-
fied in stage one and assessed against the predetermined
inclusion and exclusion criteria. Any conflicts between the
reviewers relating to study eligibility were resolved in con-
sensus meetings (KLM, KMH and KES).
2.3.2 Data Extraction andManagement
Data extraction was conducted by one reviewer (KLM),
using a pre-piloted data extraction form, and independently
verified by two members of the review team (KMH and
KES). Any discrepancies were resolved by reviewing the
original article and consensus achieved by discussion during
consensus meetings (KLM, KMH and KES), or, if needed,
in consultation with a fourth reviewer (ED). When data were
presented in graphical and not in numerical format, Digi-
tizeIt software (v2.3, DigitizeIt, Germany) was used to con-
vert the relevant data. Further, where data were incomplete,
authors were contacted to obtain the relevant information.
Authors were given 4weeks to respond; if the authors failed
to respond after this date, the papers were excluded if no
relevant data could be extracted from the published version
of the paper.
2.3.3 Quality Assessment ofIncluded Studies
Study quality was assessed by one reviewer (KLM) and
independently verified by two members of the review team
(KMH and KES), using a strategy based on the recommen-
dations of the Grading of Recommendations Assessment
Development and Evaluation (GRADE) working group [41].
This strategy considers quality of evidence for any one out-
come based on five domains, namely risk of bias, indirect-
ness, inconsistency, imprecision or evidence of publication
bias. Both risk of bias and indirectness were initially con-
ducted at the individual study level, with mode ratings used
to describe whole outcomes. The initial appraisal tool used
was based on the Downs and Black checklist for measuring
study quality [42] and was specifically modified for use in
this review (see Electronic Supplementary Material Appen-
dix S4). The modified Downs and Black checklist comprised
15 outcomes, from five domains: (1) reporting; (2) external
validity; (3) internal validity—bias; (4) internal validity—
confounding; and (5) power. A maximum attainable score
of 16 could be awarded, whereby study quality was catego-
rised as follows: “high” (14–16); “moderate” (10–13); “low”
(6–9); or “very low” (0–5). The results of the Downs and
Black assessment were used to assign an a priori quality rat-
ing to each study. This a priori rating was then either main-
tained, or downgraded a level, based on the response to two
questions that were considered key to the directness of these
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1817
Menstrual Cycle Phase and Exercise Performance
research studies: Q.1) was the MC phase confirmed using
blood samples? If the authors reported using blood samples
to confirm MC phase, the a priori rating was maintained
and if not, the study was downgraded a level (e.g., a study
that started out as “high” in quality, but did not confirm MC
phase using a blood sample, drops to “moderate” in qual-
ity); and Q.2) was the MC phase confirmed using urinary
ovulation detection kits? If the authors reported the use of
an urinary ovulation detection kit to identify MC phase, the
Q.1 rating was maintained; if not, the study was downgraded
a level (as such, the maximum rating for any study that does
not use serum analysis or urinary ovulation detection kits
to identify and verify MC phase is “low”). The inclusion of
these specific questions was based on the methodological
conclusions made in previous studies [10, 17]. Consistency
was ascertained using the meta-analysis results and was
based on visual inspection of effect size estimates, whether
or not confidence intervals overlapped, and on statistical
tests for heterogeneity. Precision was judged based on the
number of outcomes available (with outcomes based on < 5
data points downgraded) and on visual analysis of the width
of the confidence intervals. Publication bias was assessed
using Egger’s test along with visual inspection of funnel
plots. Overall, this procedure allowed the final quality of evi-
dence for each outcome to be categorised as either “high”,
“moderate”, “low” or “very low” in quality. This quality
appraisal was not used to exclude any study, although a sen-
sitivity analysis was conducted using only those individual
studies deemed to be of “high” or “moderate” quality, based
on the risk of bias and directness assessments. Any differ-
ences between the reviewers were resolved by discussion
during consensus meetings (KLM, KMH and KES), or, if
needed, in consultation with a fourth reviewer (ED).
2.4 Data Synthesis
Data were extracted from studies comprising both between-
and within-group designs. Pairwise effect sizes were cal-
culated by dividing mean differences by pooled standard
deviations. At the study level, variance of effect sizes was
calculated according to standard distributional assumptions
[43]. All meta-analyses were conducted within a Bayesian
framework enabling the results to be interpreted more intui-
tively compared to a standard frequentist approach through use
of subjective probabilities [44]. With a Bayesian framework,
dichotomous interpretations of the results of a meta-analysis
with regards to the presence or absence of an effect (e.g. with
p values) can be avoided, and greater emphasis placed on
describing the most likely values for the average effect and
addressing practical questions such as, the probability the aver-
age effect is beyond a certain threshold [44]. The Bayesian
framework is also particularly suited to hierarchical models
and sharing information within and across studies to improve
estimates [44]. In the present meta-analysis, three-level hier-
archical models were conducted to account for covariance
in multiple outcomes presented in the same study [45]. For
the initial analysis, individual effect sizes were calculated by
comparing exercise performance in the early follicular phase
(acting as a ‘control’ phase) with all other phases of the MC
(late follicular, ovulation, early luteal, mid-luteal and late
luteal). Meta-regression was performed to assess whether the
pooled effect size estimate was influenced by testing category
(endurance or strength outcomes). Where no evidence of a
difference was identified, the model was re-run combining
both categories of outcomes to increase data to better esti-
mate model parameters. Given the expectation of relatively
small effect sizes, an a priori threshold of ± 2 was identified
for outliers. Primary analyses were completed with outliers
removed, but results were also presented from the full comple-
ment of studies as sensitivity analyses. A sensitivity analysis
was also conducted on data obtained from studies categorised
as “high” or “moderate” in quality. Assessment of publication
bias was made using a multilevel extension of Egger’s test with
effect sizes regressed on the inverse of standard errors [46].
Inferences from all analyses were performed on posterior sam-
ples generated by Markov Chain Monte Carlo with Bayesian
95% credible intervals (CrIs) constructed to enable probabil-
istic interpretations of parameter values. Interpretations were
based on visual inspection of the posterior sample, the median
value (ES0.5: 0.5 quantile) and 95% CrIs. Cohen’s [47] standard
threshold value of 0.8 was used to describe effect size as large,
values between 0.5 and 0.8 as medium, values between 0.2 and
0.5 as small, and values between 0 and 0.2 as trivial.
Subsequent to this initial analysis, a network meta-anal-
ysis approach was used to compare exercise performance
measured across all MC phases (early follicular, late folli-
cular, ovulation, early luteal, mid-luteal and late luteal) with
each other. Network meta-analyses are becoming increas-
ingly common in evidence synthesis and are most commonly
used to compare multiple experimental treatments where
individual studies are unlikely to directly compare all rel-
evant treatments [48]. The technique calculates pairwise
effect sizes from studies comparing two treatments (direct
evidence), and generates indirect evidence comparing other
treatments through a common comparator [48]. The tech-
nique was adopted in the present review to supplement the
initial pairwise meta-analysis and synthesise additional data
comparing exercise performance using different combina-
tions of MC phases. Study-specific treatment effects were
drawn from multivariate normal distributions with up to five
arms included. To test the consistency assumption of the
network meta-analysis, the fit of the base-case model was
compared to that of the inconsistency model. To summa-
rise potential differences in exercise performance outcomes
across all MC phases, results from the network meta-analysis
were used to calculate the Surface Under the Cumulative
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1818 K.L.McNulty et al.
Ranking curve (SUCRA; [49]). For each MC phase, a
SUCRA value expressed as a percentage was calculated
representing the likelihood that exercise performance was
maximised or near maximised relative to other MC phases.
More formally, the SUCRA value can be interpreted as the
average proportion of phases where exercise performance
is lower than the phase considered, with the mean SUCRA
value equal to 50% [50]. Analyses were performed using the
R packages R2WinBUGS [51] and brms [52]. Convergence
of parameter estimates was checked with Gelman–Rubin
R-hat values [53].
3 Results
3.1 Literature Search
The literature search and selection of studies are presented
in Fig.2.
3.2 Study Characteristics
In total, 78 studies [19, 27–39, 54–117] with a total of 1193
participants were included in the review. Details of the
included studies are shown in Electronic Supplementary
Material Appendix S5.
3.3 Methodological Quality
3.3.1 Quality Assessment ofIncluded Studies
All quality classifications are presented in Fig.3. Analysis
of quality based on the entire evidence base (n = 78) was
ascertained at the individual study level, and according to
the Downs and Black checklist, as well as the additional
questions regarding MC phase confirmation. The quality of
the evidence from the 78 studies included in this review was
primarily classified as “low” in quality (8% “high”; 24%
“moderate”; 42% “low”; 26% “very low”; Fig.3) such that,
“our confidence in the effect estimate is limited: the true
effect might be substantially different from the estimate of
the effect” [118]. In particular, 71% of studies were initially
allocated an a priori rating of “moderate” quality; how-
ever, following the application of questions pertaining to
MC phase identification and verification, only 24% of these
studies were allocated a final rating of “moderate” quality.
Fig. 2 Preferred Reporting
Items for Systematic Reviews
and Meta-Analyses (PRISMA)
guidelines flow chart for litera-
ture search and study selection
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1819
Menstrual Cycle Phase and Exercise Performance
3.3.2 Menstrual Cycle Phase Identication andVerication
In the 78 included studies, an array of methods was used
to identify MC phase: (1) a combination of methods (e.g.
counting of days, basal body temperature [BBT], assess-
ment of menstrual symptoms, MC history and serial fol-
licular scanning] without urinary ovulation detection kits
(45%); (2) a combination of methods (e.g. counting of days,
BBT, MC history, assessment of menstrual symptoms and
urine ovulation detection kits) with urinary ovulation detec-
tion kits (31%); (3) counting of days (10%); (4) MC history
(4%); (5) BBT (4%); and (vi) urinary ovulation detection
kits (1%). In addition, some studies (5%) did not provide
any information on how MC phases were identified. In rela-
tion to MC phase verification, out of the 78 studies included
in the review, the majority of studies (59%) retrospectively
verified MC phase using serum oestrogen and progesterone,
a small number of studies retrospectively verified MC phase
using saliva (4%) or urine (2%) oestrogen and progesterone,
and the remaining studies provided no information on how
they verified the identified MC phase (35%).
3.4 Outcomes
3.4.1 Analysis 1: Pairwise Meta‑Analysis
The initial meta-analysis comprised pooling of pairwise
effect sizes comparing exercise performance during the early
follicular phase of the MC with all other MC phases (late
follicular, ovulation, early luteal, mid-luteal and late luteal).
From the 78 studies that were eligible for the systematic
review, 51 studies [19, 27–29, 31, 34–37, 54–60, 62–67,
70–72, 74, 75, 77, 78, 81, 84–86, 89–94, 96, 99, 101–103,
105–107, 109, 114–116] included assessment of exercise
performance during the early follicular phase of the MC
and included all other data required for calculations. The
51 studies (mode quality rating = “low”; 8% “high”; 24%
“moderate”; 37% “low”; 31% “very low”) generated 362
pairwise effect sizes (240 strength and 122 endurance) with
an average of four outcomes per study and a range from 1
to 12 outcomes. Data were obtained from 709 participants
with studies comprising a mean participant size of 14 (range
n = 5–100). A total of nine outliers were identified (seven
studies with effect sizes less than −2 [favoring the “other
MC phases”] and two studies with effect sizes greater than
+2 [favoring the early follicular phase]) and subsequently
removed from the analysis. The three-level hierarchical
model indicated a trivial effect with reduced performance
obtained in the early follicular phase of the MC, based on the
median pooled effect size (ES0.5 = − 0.06 [95% CrI: −0.16 to
0.04]; Fig.4). Large between-study variance was identified
(
𝜏
0.5 = 0.26 [0.18–0.38]) and interclass correlation coefficient
estimates close to zero indicated little within-study correla-
tion between outcomes. Pooling of strength and endurance
outcomes was conducted as no evidence was obtained that
indicated a differential effect between these performance cat-
egories (ES0.5/Endurance-Strength = − 0.01 [95% CrI: −0.18 to
0.16]). Posterior estimates of the pooled effect size indicated
close to zero probability of a small effect either in favour of
the early follicular phase or all other MC phases (d ≥ 0.2;
p ≤ 0.001). Egger’s regression test provided no evidence
of publication bias (Egger0.5 = –0.01 [95% CrI: −0.09 to
0.08]). Inclusion of outliers within the model had minimal
influence on the average effect size (ES0.5 = − 0.08 [95% CrI:
−0.21 to 0.05]) and between-study variance (
𝜏
0.5 = 0.30
Fig. 3 Quality rating of outcomes from all included studies (n = 78).
Each bar represents the proportion of studies assigned a “high,”
“moderate,” “low,” or “very low” quality rating. The x-axis repre-
sents the different stages of the quality appraisal process, with ques-
tion one (Q. 1) and question two (Q. 2) indicating the questions asked
to determine menstrual cycle phase identification and verification in
each study, with the final bar representing the proportion of studies
assigned to each quality rating category
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1820 K.L.McNulty et al.
[95% CrI: 0.23–0.39]). A sensitivity analysis was completed
with data obtained from studies classified as either “high” or
“moderate” in quality (16 studies compromising 38 strength
effect sizes and 12 endurance effect sizes from 169 partici-
pants [19, 27, 31, 35, 37, 54, 57, 58, 67, 71, 75, 85, 90, 99,
106, 115]). Compared to the primary analysis, the reduced
data set resulted in a relatively symmetric credible interval
around the zero value (ES0.5 = − 0.01 [95% CrI: −0.11 to
0.08]).
3.4.2 Analysis 2: Network Meta‑Analysis
Figure5 shows a network diagram illustrating the pairwise
effect sizes calculated across the six MC phases (early fol-
licular, late follicular, ovulation, early luteal, mid-luteal
and late luteal). Seventy-three studies (mode quality rat-
ing = “low”; 7% “high”; 26% “moderate”; 42% “low”; 25%
“very low”) included enough data to be included in the net-
work meta-analysis [19, 27–29, 31, 33–39, 54–68, 70–72,
74, 75, 77–117]. A total of 220 performance outcomes were
included across 954 participants, with the number of com-
parisons across MC phases equal to: comparison between
two phases = 87; comparison between three phases = 93;
comparison between four phases = 27; comparison between
five phases = 10; and comparison between six phases = 3. The
Fig. 4 Bayesian Forest Plot of multilevel meta-analysis comparing
performance measured during the early follicular phase with all other
menstrual cycle phases. The study-specific intervals represent indi-
vidual effect size estimates and sampling error. The circle represents
the pooled estimate generated with Bayesian inference along with
the 95% credible interval (95% CrI). Negative values favour all other
menstrual cycle phases (late follicular, ovulation, early luteal, mid-
luteal and late luteal) compared to the early follicular phase. High and
moderate quality studies are indicated with an asterisk (*)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1821
Menstrual Cycle Phase and Exercise Performance
most frequent comparisons made were between the early fol-
licular and mid-luteal phase of the MC (21% of comparisons),
followed by the late follicular and mid-luteal phases of the
MC (18% of comparisons). Pairwise estimates including the
early follicular phase as a reference are presented in Table1.
with negative median pooled effect sizes (“other MC phases”)
obtained for all comparisons and the largest effect identified
between the early follicular and the late follicular phase of the
MC (ES0.5 = − 0.14 [95% CrI: −0.26 to −0.03]). The low-
est SUCRA value was obtained for the early follicular phase
(30%) with all other MC phase values ranging between 53
and 55%.
4 Discussion
The aim of this review was to examine if MC phase affects
exercise performance in eumenorrheic women. The results
indicate that on average, exercise performance might be
trivially reduced during the early follicular phase of the
MC when compared with all other MC phases. Perfor-
mance was consistent between all other MC phases. In
addition to the estimated trivial average effect, results from
the meta-analysis models showed relatively large between-
study variance indicating that research design, participant
characteristics and type of performance measured might
influence any effect. Furthermore, most studies that were
included in this meta-analysis were classified as “low”
in quality, and as such, the confidence in the evidence
reported in this meta-analysis is also low, and should be
interpreted with caution. Due to the trivial effect size, the
large between-study variation and the number of poor-
quality studies included in this review, general guidelines
on exercise performance across the MC cannot be formed;
rather, it is recommended that a personalised approach
should be taken based on each individual’s response to
exercise performance across the MC.
There are a range of suggested mechanisms by which
the lower levels of oestrogen and progesterone seen in the
early follicular phase of the MC might negatively affect
the exercise performance. Although a detailed mechanistic
review is beyond the scope of this review, the following
points can be noted. First, oestrogen is known for its ana-
bolic effects [24, 25], as well as its role in regulating sub-
strate metabolism through increasing glycogen uptake and
sparing glycogen stores. Additionally, it has been shown
to have antioxidant and membrane stabiliser properties,
which might offer protection against exercise-induced
muscle damage and reduce inflammatory responses [26].
Further, oestrogen is thought to have neuroexcitatory
effects, whereby it reduces inhibition and increases vol-
untary activation [19]. Therefore, when oestrogen rises
during the late follicular and ovulatory phases and remains
elevated in the mid-luteal phase, it is plausible that this
might affect muscular performance [24, 25] or maximal
and submaximal intensity exercise performance [26].
Moreover, progesterone is thought to have anti-oestrogenic
effects [21]; therefore, it could be speculated that the ben-
eficial performance effects of oestrogen are likely to be
greater in the late follicular and ovulatory phases when
Fig. 5 Network diagram illustrating the pairwise effect sizes calcu-
lated across the six menstrual cycle phases (early follicular, late folli-
cular, ovulation, early luteal, mid-luteal and late luteal). The analysis
included direct and indirect pairwise effect sizes from 73 studies. The
relative size of nodes and relative thickness of connecting lines illus-
trate the frequency of outcomes measured in a given menstrual cycle
phase and the number of direct comparisons between two phases,
respectively
Table 1 Summary of network meta-analysis results from 73 studies
using the early follicular phase as a reference
Negative values for effect sizes favour all other menstrual cycle
phases (late follicular, ovulation, early luteal, mid-luteal and late
luteal) compared to the early follicular phase
SUCRA the surface under the cumulative ranking curve, CrI credible
intervals
Comparison to early
follicular phase
Effect size [95% CrI] SUCRA (%)
Early follicular − 30
Late follicular −0.14 [− 0.26 to −0.03] 54
Ovulation −0.07 [− 0.15 to 0.07] 55
Early luteal −0.07 [− 0.19 to 0.16] 54
Mid-luteal −0.04 [− 0.11 to 0.08] 55
Late luteal −0.01 [− 0.18 to 0.17] 53
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1822 K.L.McNulty et al.
oestrogen is high without the interference of progesterone,
compared to the mid-luteal phase when both oestrogen
and progesterone are high. This speculation is supported
by the finding presented here that the biggest difference
in performance was between the early follicular and late
follicular phases of the MC. However, the average effect
calculated was trivial and there was considerable overlap
between each of the pairwise comparisons with the early
follicular phase. Whilst the current meta-analysis can-
not identify the mechanisms responsible, it does indicate
that, on average, exercise performance might be reduced
by a trivial amount in the early follicular phase of the MC
compared with all other phases. Interestingly, our sister
meta-analysis, on the effects of oral contraceptives (OCs)
on exercise performance, showed that, compared with
eumenorrheic women, OC users have on average slightly
inferior exercise performance [119]. Oral contraceptive
use results in significantly downregulated concentrations
of endogenous oestrogen and progesterone when compared
with the ovulatory and mid-luteal phases of the MC [71].
Indeed, the endogenous hormonal profile of OC users is
comparable to the profile seen during the early follicular
phase of the MC [71]. Both meta-analyses show slightly
impaired, group-level, exercise performance when both
oestrogen and progesterone are at their lowest, therefore
collectively suggesting that exercise performance might
be mediated by the concentration of endogenous ovarian
hormones in some exercising women.
Within the literature to date, the most common compari-
son used when investigating the effects of the MC on per-
formance was between the early follicular and mid-luteal
phase. This is not surprising, as the difference in the hormo-
nal milieu is typically at its greatest between these phases
(early follicular when both oestrogen and progesterone are
low, and mid-luteal when both oestrogen and progesterone
are high) [17]. As such, if performance was altered by syn-
ergistic fluctuations in oestrogen and progesterone levels, the
comparison between these two phases would maximise the
chance of observing an effect. This bi-phasic comparison,
however, ignores the late follicular and ovulatory phases of
the MC, when oestrogen is high, and progesterone is low.
The network analysis indicated that the largest difference in
performance might be expected between the early follicular
and the late follicular phases of the MC, when both oes-
trogen and progesterone are low and when oestrogen rises
without a concurrent increase in progesterone. Therefore,
the effects of oestrogen, without the interference of proges-
terone, might be overlooked if the late follicular or ovula-
tory phases are not included within the phase comparisons.
Future studies should, therefore, consider multiple phase
comparisons so that the effects of different ratios of oestro-
gen and progesterone can be explored. It should be noted,
however, that the inclusion of multiple phase comparisons
will result in more variability, and as such, more participants
will be needed to conclude any potential effects.
Although this systematic review included 78 studies and
1193 women (range n = 5–100), there were very few studies
classified as “moderate” or “high” in quality, which implies
that the confidence in the evidence used in this meta-analysis
should be low. Specifically, only 24% of studies were allo-
cated a quality rating of “moderate”, and only 8% of studies
were allocated a quality rating of “high”. Our quality assess-
ment approach included consideration of the methods used
to identify and verify the MC phase in the included studies,
which is considered to be key to the trustworthiness of the
results obtained (i.e. Q1. was the MC phase confirmed using
blood samples; Q2. was the MC phase confirmed using uri-
nary ovulation detection kits?). Across the included studies
there was large variability in the methods used to identify
and then verify MC phase, namely calendar-based counting,
BBT, MC history questionnaires, urinary ovulation detection
kits, and salivary, urinary and serum measurement of both
oestrogen and progesterone. Calendar-based counting is an
indirect method to identify MC phase, whereby the self-
reported onset of menses is set as day one, and the phases are
then established by counting days from this point [17]. This
method, however, assumes that all participants with regular
menstruation experience ovulatory cycles with a mid-cycle
peak in oestrogen, which is not always the case [120, 121].
As such, the use of calendar-based counting methods in iso-
lation is not recommended when accurate identification of
MC phase is required [122]. Similarly, BBT is a widely used
method for identifying ovulation, and the length of the fol-
licular and luteal phases [17], but this method does not pro-
vide information regarding actual hormone concentrations
[123], and temperature readings might also be influenced by
a range of factors such as illness, stress, sleep patterns and
medication [124]; hence BBT in isolation is not considered
a reliable method for MC phase verification [17]. Studies
using these aforementioned methods were downgraded on
this basis. Indeed, very few studies used a combination of
the recommended methods by Cable and Elliott [10] and
Janse de Jonge etal. [17], which include the use of the cal-
endar-based counting method in conjunction with urinary
ovulation detection kits to assist in setting the timing of test-
ing throughout the MC and to confirm the presence of an
ovulatory cycle, followed by serum measurement of both
oestrogen and progesterone levels to subsequently verify the
phases of the MC. Given that the rationale for exploring the
effect of the MC on performance is underpinned by changes
in oestrogen and progesterone, it is essential that studies
should accurately verify the acute changes in endogenous
hormones during each phase of the MC to ensure that the
intended phase is being examined. Overall, without blood
analysis, it is unclear which hormone milieu is being inves-
tigated, thus making it difficult to draw accurate conclusions
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1823
Menstrual Cycle Phase and Exercise Performance
regarding changes in performance across the MC and to
make direct comparisons between studies. These recom-
mendations echo recent publications in the area of women’s
physiology [10, 17], demonstrating an increasing awareness
for the nuances of this type of research, and collectively
provide researchers with ample tools to make methodologi-
cal decisions for future investigations. To limit the influence
of low quality papers on the analyses, a sensitivity analysis
was conducted with data obtained from studies that were
classified as either “moderate” or “high” in quality [19, 27,
31, 35, 37, 54, 57, 58, 67, 71, 75, 85, 90, 99, 106, 115]. Due
to the limited amount of data available, only the pairwise
meta-analysis comparing exercise performance during the
early follicular phase of the MC with all other MC phases
was conducted. The sensitivity analysis provided no evi-
dence of any effect, with a relatively symmetric credible
interval centred close to zero. Whilst studies that were allo-
cated a higher quality rating were better able to identify and
verify the MC phase, there was no association between study
quality and average sample size. Given the reduced amount
of data included within the sensitivity analysis and the low
sample sizes, the result is consistent with the primary analy-
ses and conclusion that if an average effect exists, it is likely
to be trivial in magnitude.
The results from the meta-analysis models consistently
showed large between-study variance, which might be attrib-
utable to several factors: (a) inconsistent research design, as
shown by the network analysis that highlights the discrep-
ancy in the number of phase comparisons made between
studies; (b) poor methodological practices, as emphasised
by the quality assessment, whereby the majority of studies
included in the meta-analysis were classified as “low” (42%)
in quality primarily due to inadequate MC phase identifica-
tion and verification in many studies; (c) non-homogenous
participant groups, as shown in Electronic Supplementary
Material Appendix S5 participants in this meta-analysis
ranged from sedentary, to healthy, to physically active to
elite athletes; and (d) large variation in the type of perfor-
mance outcome measured, as detailed in Electronic Sup-
plementary Material Appendix S2. As such, the breadth of
this research area, without the corresponding depth, makes it
difficult to apply a meaningful, yet generalisable, interpreta-
tion of the current data.
5 Conclusion
This is the first systematic review with meta-analysis to
examine the effect of MC phase on exercise performance
in eumenorrheic women. These data provide new informa-
tion that exercise performance might on average be reduced
by a trivial amount during the early follicular phase of
the MC, compared with all other MC phases. The current
meta-analysis also identified large between-study variance
in the effect of the MC on exercise performance. This might
have been influenced by a range of methodological factors
and small participant numbers (average n = 14) as well as
associated high sampling variance. Participant characteris-
tics, such as training history, might also have contributed to
the large between-study variance observed. From a practical
perspective, as the effects tended to be trivial and variable
between studies, the implications of these findings are likely
to be so small as to be meaningless for most of the popu-
lation. These trivial effects might, however, be of greater
relevance to elite athletes, where the difference between win-
ning and losing is marginal. Specifically, we recommend
that practitioners working with elite sportswomen need to
consider the MC and be aware of the potential times across
the cycle whereby exercise performance might be reduced
(early follicular phase) or enhanced (all other MC phases),
but this approach should be tailored to, and informed by,
the individual athlete. In the future, it would be interest-
ing to identify which factors might cause some women to
experience reduced performance during the early follicular
phase of the MC when compared with all other MC phases,
and identify strategies to monitor these effects. Therefore,
future studies need to improve methodological quality (e.g.,
appropriate biochemical outcomes to confirm MC phase)
and limit confounders to facilitate a deeper understanding of
the effects of the MC on exercise performance in individuals.
Author Contributions KLM, KES, KMH, PA, SG and KT designed the
research. KLM conducted the searches and screening and KLM, KES
and KMH completed the three-phase screening process. KLM extracted
the data, which were verified by KES and KMH. PAS performed all
the statistical analysis. PAS, KLM, KMH, KES and ED interpreted the
data analysis. KLM and KES wrote the manuscript with critical input
from KMH, ED, PAS, PA, SG and KT. All authors read and approved
the final manuscript.
Availability of Data and Material Please contact the corresponding
author for data requests.
Compliance with Ethical Standards
Funding No sources of funding were used to assist in the prepara-
tion of this article. Eimear Dolan is supported by a research grant
(2019/05616-6) from the Fundação de Amparo à Pesquisa do Estado
de São Paulo (FAPESP).
Conflicts of interest Kelly Lee McNulty, Kirsty Jayne Elliott-Sale,
Eimear Dolan, Paul Alan Swinton, Paul Ansdell, Stuart Goodall,
Kevin Thomas and Kirsty Marie Hicks declare that they have no po-
tential conflicts of interest with the content of this article.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1824 K.L.McNulty et al.
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
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Aliations
KellyLeeMcNulty1 · KirstyJayneElliott‑Sale2 · EimearDolan3 · PaulAlanSwinton4 · PaulAnsdell1 ·
StuartGoodall1 · KevinThomas1 · KirstyMarieHicks1
* Kirsty Jayne Elliott-Sale
kirsty.elliottsale@ntu.ac.uk
1 Department ofSport, Exercise andRehabilitation, Faculty
ofHealth andLife Sciences, Northumbria University,
Newcastle-upon-Tyne, UK
2 Department ofSport Science, Sport, Health andPerformance
Enhancement (SHAPE) Research Centre, Nottingham Trent
University, Nottingham, UK
3 Applied Physiology andNutrition Research Group, Escola de
Educação Física e Esporte, Faculdade de Medicina FMUSP,
Universidade de São Paulo, SãoPaulo, Brazil
4 School ofHealth Sciences, Robert Gordon University,
Aberdeen, Scotland,UK
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
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