Mating Effort Predicts Human Menstrual
, Hannah K. Bradshaw
, and Sarah E. Hill
The human menstrual cycle is characterized by substantial variability both within and between women. Here, we sought to
account for such variability by examining whether human menstrual cycle frequency varies as a function of the projected fitness
payoffs associated with investment in mating effort. We used structural equation modeling to test the prediction that women
whose environmental conditions or life histories favor heavier investment in mating effort would have shorter, more regular
cycles. Results supported our hypothesis, revealing that women who project more mating success and have faster life history
strategies exhibit greater mating effort and shorter, more regular menstrual cycles. An alternative model that specified cycle
frequency as a predictor of mating effort was a poor fit for the data, lending support for the hypothesized directionality of the path
between these variables. Together, these results provide some of the first empirical evidence that the length and regularity of the
human menstrual cycle may be calibrated to investment in mating effort.
life history theory, menstrual cycle, fecundity, mating effort, mating success
Date received: June 12, 2018; Accepted: October 15, 2018
Although the human menstrual cycle typically lasts between 21
and 35 days, there is considerable variability in cycle length
both between and within women (Chiazze, Brayer, Macisco,
Parker, & Duffy, 1968; Creinin, Keverline, & Meyn, 2004). For
example, one seminal study tracking 2,316 women over 30,655
total cycles found that healthy women’s menstrual cycles often
last anywhere between 15 and 45 days (Chiazze et al., 1968).
Others find sizable differences in cycle length even within
individual women, with most reporting inter-cycle differences
ranging between 7 and 14 days (Creinin et al., 2004).
Why is there so much variability in women’s cycles? Much
research addressing this issue has examined the role that lifestyle
and environmental factors each play in contributing to the
observed variability in women’s cycles (e.g., Fenster et al.,
1999; Harlow & Matanoski, 1991; Rowland et al., 2002). For
example, longer, more irregular cycles have been associated
with higher body mass index (BMI), stress, and disorders such
as diabetes (Matteo, 1987; Solomon et al., 2001). Shorter, more
regular cycles, on the other hand, have been linked to healthier
BMI, nulliparity, and earlier age of menarche (Kato et al., 1999).
Although such research has provided a useful first step in
identifying various contributors to menstrual cycle variability,
it has been done in isolation of a larger predictive framework
that could account for these findings and generate new
predictions about contextual effects on cycle length. Here,
we redress these gaps using insights from life history theory
(LHT), hypothesizing that variation in women’s cycle fre-
quency should reflect the projected fitness payoffs associated
with investment in mating effort. Specifically, we predicted
that women whose life histories or environmental conditions
favor heavier investment in mating effort would have shorter,
more regular cycles, whereas women whose life histories or
environmental conditions favor lesser investment in mating
effort would have longer, less regular cycles.
Factors Influencing the Human Menstrual
Cycle length and regularity are often closely related, such that
women with irregular cycles are more likely to have longer
cycles, while women with more regular cycles are more likely
Department of Psychology, Texas Christian University, Fort Worth, TX, USA
Jeffrey Gassen, Department of Psychology, Texas Christian University, 2955 S
University Drive, Fort Worth, TX 76129, USA.
October-December 2018: 1–10
ªThe Author(s) 2018
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to have shorter cycles (e.g., Gleeson et al., 2016; Jensen,
Scheike, Keiding, Schaumburg, & Grandjean, 1999; Kolstad
et al., 1999). To date, much of the research examining
variability in menstrual cycle length and regularity has uti-
lized an epidemiological perspective, focusing on the influ-
ence of chronic diseases and lifestyle factors (e.g., Rowland
et al., 2002; Saha et al., 2014; Solomon et al., 2001). This
research generally finds that disease states predict a shift
toward longer, more irregular menstrual cycles. For exam-
ple, longer cycle lengths and cycle irregularity have both
been found in women with chronic medical conditions, such
as irritable bowel disease, diabetes, and depression, among
other disorders (Rowland et al., 2002; Saha et al., 2014;
Solomon et al., 2001). Further, both long cycles (i.e., 36
days or more) and cycle irregularity are each associated
with a history of infertility (Rowland et al., 2002). Shorter,
more regular cycles, however, may also be related to certain
health issues in women, such as having an increased risk of
endometriosis (Cramer et al., 1986; Wei, Cheng, Bu, Zhao,
& Zhao, 2016), breast cancer (Den Tonkelaar & De Waard,
1996; Terry, Willet, Rich-Edwards, Hunter, & Michels,
2005), and anxiety disorders (Barron, Flick, Cook, Homan,
& Campbell, 2008).
Others find that healthy women’s cycles are also subject
to variability based on lifestyle factors and transient changes
in health and well-being. For example, being overweight,
experiencing rapid changes in body composition (Harlow
& Matanoski, 1991), and engaging in both high and low
levels of physical activity (Gudmundsdottir, Flanders, &
Augestad, 2011; Hahn et al., 2013) are associated with lon-
ger cycle lengths. Further, aspects of one’s occupation such
as working night shifts (Lawson et al., 2011) and experien-
cing stress in the workplace are each predictive of cycle
irregularity (Matteo, 1987). Shorter, more regular cycles,
on the other hand, are linked to smoking, alcohol consump-
tion, and an earlier age of menarche (Hahn et al., 2013;
Jukic et al., 2007; Kato et al., 1999; Liu, Gold, Lasley, &
Although the epidemiological perspective has offered a
number of important insights into the relationship between
disease states, health, and menstrual cycle characteristics,
this perspective has key limitations. First, the epidemiologi-
cal perspective fails to account for normal menstrual cycle
variability that exists in healthy women (i.e., in the absence
of disease states, acute stress, or illness). For example, sub-
stantial variability in menstrual cycle length remains even
when adjusting for factors related to health outcomes (see,
e.g., Fehring, Schneider, & Raviele, 2006). Further, research
using the epidemiological perspective lacks a larger predic-
tive framework that explains why certain health and life-
style factors appear to reliably increase or decrease cycle
frequency. Here, we use the theoretical framework of LHT
to account for these findings and generate new predictions
about some of the specific contexts that should influence
cycle length and regularity.
Contextual Variables Predicting Mating
LHT is a framework that yields predictions about how and
when organisms will allocate resources toward accomplishing
the various tasks necessary for survival and reproduction,
including mating, offspring care, foraging, growth, and somatic
defense (Kaplan & Gangestad, 2005; Roff, 1992; Stearns,
1992). According to this perspective, the resource allocation
strategy “selected” by an organism varies as a function of the
expected fitness payoffs associated with investment in each of
these domains relative to the others (Ellis, Figueredo, Brum-
bach, & Schlomer, 2009; Kaplan & Gangestad, 2005; Roff,
1992; Stearns, 1992). When the expected fitness benefits asso-
ciated with investing effort in one domain—such as mating—is
relatively high, energetic investment in that domain is expected
to increase. Conversely, when the fitness benefits associated
with investing effort in a particular domain are relatively low,
energetic investment is expected to decrease.
Much research supports this view. For example, for men, the
expected fitness payoffs associated with increased mating
effort are higher than they are for women because their initial
investment in offspring production is so low (Bateman, 1948;
Courtiol, Pettay, Jokela, Rotkirch, & Lummaa, 2012; Symons,
1979; Trivers, 1972). Consistent with predictions from LHT,
research finds that men invest more effort in mating than do
women (e.g., desire for more sexual variety, Schmitt, 2003;
greater frequency of sexual fantasies, Ellis & Symons, 1990;
desire for greater number of partners, Buss & Schmitt, 1993).
Research also finds support for this general hypothesis in
women based on internal cyclic fertility cues. Several studies
have found, for example, that women invest more in mating
effort (e.g., ornamentation, Durante, Li, & Haselton, 2008;
Haselton, Mortezaie, Pillsworth, Bleske-Rechek, & Frederick,
greater sexual desire, Jones et al., 2018; Pillsworth,
Haselton, & Buss, 2004) when the probability of conception
is high (i.e., near ovulation), particularly for those from envir-
onments favoring faster life history strategies (Kim, Bradshaw,
Durante, & Hill, 2018). These findings suggest that when the
fitness payoffs associated with allocating resources toward
mating effort are high, greater investment in this domain
Guided by these insights, we sought to examine whether
conditions that favor investment in mating effort predict
shorter, more regular cycles. Total fecundity varies, in large
part, by the length and frequency of women’s cycles, with
shorter, more regular cycles corresponding to increased
fecundity (Jensen et al., 1999; Wesselink et al., 2016; Zhang
et al., 2017). Such a relationship might exist because greater
cycle frequency provides more opportunities for conception.
Accordingly, contexts favoring heavier investment in mating
effort should calibrate functioning of the hypothalamic–
pituitary–gonadal (HPG) axis, leading to shorter, more reg-
ular cycles, which would increase potential conceptive
opportunities (Belsky, Steinberg, & Draper, 1991; Wood
& Weinstein, 1988).
Support for this hypothesis is found in research examining
the link between sexual activity and menstrual cycle length
and variability. For example, women who are regularly sexu-
ally active have less cycle variability than women who are
celibate or report only sporadic sexual activity (Burleson,
Gregory, & Trevathan, 1991; Cutler, Garcia, & Krieger,
1980). Others find that women who spend more time with
men have a significantly higher rate of ovulation than those
spending less time around men (Veith, Buck, Getzlaf, Van
Dalfsen, & Slade, 1983).
Additional support for our hypothesis is found in research
conducted in nonhuman animals. For example, research finds
that exposure to desirable males (or their mating calls)
advances timing of estrous in a female deer (both the red deer,
Cervus elaphus, Komers, Birgersson, & Ekvall, 1999; as well
as the fallow deer, Dama dama, McComb, 1987). Similarly,
menstrual cycles of the female mandrill, Mandrillus sphinx
(Setchell & Wickings, 2004) and chacma baboon, Papio ursi-
nus (Howard-Tripp & Bielert, 1978) become shorter and more
regular in contexts that favor increased mating effort (higher
dominance rank and contact with male conspecifics, respec-
tively). Research such as this suggests that females’ HPG axis
may be tuned to environmental- and individual-based cues that
influence the expected payoff associated with increased mating
effort, and females respond to these cues by adjusting the fre-
quency of their menstrual cycles.
In humans, one important factor that influences the expected
fitness payoffs associated with investment in mating effort is
one’s life history strategy. Individuals with “faster” life history
strategies (which emerge in the context of early life stress,
father absence, and ecological unpredictability) tend to favor
heavier investment in mating effort than those with “slower”
life history strategies (Belsky et al., 1991; Chua, Lukaszewski,
Grant, & Sng, 2016; Ellis et al., 2009; Kaplan & Gangestad,
2005; Szepsenwol et al., 2017). Accordingly, we should find
that women with faster life history strategies—which are char-
acterized by relatively greater investment in mating effort—
have shorter, more regular cycles than women with slower life
A second factor that plays an important role in modulating
humans’ mating effort is one’s ability to successfully compete
for desirable mates in their current environment (Clark, 2004;
Perilloux, Cloud, & Buss, 2013; Weeden & Sabini, 2007).
Because availability of partners in the future is not guaranteed,
the expected fitness payoff of investment in mating effort
increases with one’s projected mating success in the current
environment. That is, mating effort should increase in contexts
in which one expects a high likelihood of achieving a desired
mating outcome. Consistent with this idea, research finds that
both men and women report greater mating effort in contexts in
which they perceive themselves as being highly desirable to
potential mates (Clark, 2004; Perilloux et al., 2013; Weeden &
Sabini, 2007). Others find that mating effort increases when
individuals are led to believe that mating opportunities are
abundant (e.g., Moss & Maner, 2016). Together, this research
demonstrates that factors such as individual differences in life
history strategies and access to mating opportunities influence
investment in mating effort.
The Current Research
In the current research, we tested the impact of two concep-
tually distinct predictors of increased mating effort in
humans—faster life history strategies (Belsky, Schlomer, &
Ellis, 2012; Del Giudice, Gangestad, & Kaplan, 2015; Ellis
et al., 2009) and one’s ability to achieve a successful mating
outcome in their current environment (Durante & Li, 2009;
Perilloux et al., 2013)—on women’s cycle length and regular-
ity. We predicted that women whose life histories favor
increased mating effort (those with relatively faster life history
strategies) and women who perceive themselves as being most
able to achieve a successful mating outcome in their current
environment would exhibit greater mating effort and shorter,
more regular cycles. Because cycle frequency over time is
determined by both the length of each individual cycle, as well
as the regularity of cycle occurrence, we predicted that both
variables would be influenced by investment in mating effort.
Finally, we tested an alternative to our hypothesized model in
which cycle length and regularity were specified as predictors
of mating effort to test the proposed directionality of the path
between these variables.
Participants were 176 female undergraduate students ranging
from 17 to 30 years of age (M
participated in exchange for partial course credit. All women
who participated were (a) nulliparous, (b) naturally cycling
(i.e., had not been on hormonal birth control for at least 3
months prior to participating), (c) without chronic health
problems, including hormonal disorders of any kind, and
Materials and Procedure
After determining eligibility using a presurvey, women who
indicated that they wished to participate in the study were
contacted by the researchers via e-mail and were provided a
link to a survey containing the target measures. After providing
informed consent, participants completed the survey, were
thanked, debriefed, and awarded credit.
Life history strategy. Participants’ life history strategies were
measured using the short-form Arizona Life History Battery
(the Mini-K; Figueredo et al., 2006). We chose the Mini-K
because it is one of the most reliable predictors of life history
strategies (Dunkel & Decker, 2010). The Mini-KScale consists
of 20 statements assessing behaviors (e.g., “I am often in social
contact with my friends”) and attitudes (e.g., “I can often tell
how things will turn out”) related to one’s life history strategy.
Ratings are made on 7-point scales (3¼strongly disagree,
Gassen et al. 3
3¼strongly agree) with higher scores indicating slower stra-
tegies. Together, the items were formed into a mean composite
Projected mating success. Projected mating success was mea-
sured using the Self-Perceived Mating Success Scale (Landolt,
Lalumie`re, & Quinsey, 1995). This 8-item scale assesses an
individual’s perceived likelihood of achieving a successful
mating outcome in their environment (e.g., “Members of the
opposite-sex are attracted to me” and “I can have as many
sexual partners as I choose”). Ratings were made on 7-point
scales (1 ¼completely disagree,7¼completely agree), and all
items were formed into a mean composite with higher scores
indicating more expected mating success (a¼.90).
Mating effort. Mating effort was measured using the revised
Sociosexual Orientation Inventory (SOI-R; Penke &
Asendorpf, 2008). The SOI-R consists of three subscales that
capture unique facets of mating effort: Mating Behavior (e.g.,
“With how many different partners have you had sex within the
past 12 months?”), Sexual Attitudes (e.g., “Sex without love is
OK”), and Sexual Desire (“How often do you have fantasies
about having sex with someone you are not in a committed
romantic relationship with?”). Participants provided answers
using 9-point scales with higher scores indicating higher mat-
ing effort. Each subscale was formed into a mean composite
(behaviors: a¼.89, attitudes: a¼.87, desire: a¼.84). We
chose the SOI-R as a proxy measure of current mating effort as
this scale has been used in previous research as part of a latent
factor of the mating effort construct (e.g., Fernandes, Menie,
Hutz, Kruger, & Figueredo, 2016), it is predictive of one’s
lifetime number of sexual partners (Ostovich & Sabini,
2004), and it is associated with investment in other mating-
related activities (e.g., ornamentation; Kruger, 2017).
Average menstrual cycle length and regularity. Participants pro-
vided a whole number representing their average cycle length
in response to the question: “How many days long are your
menstrual cycles (for most women, the range is between 25 and
35 days)?” We informed participants that menstrual cycle
length refers to the number of days between the start of one
menstrual period and the start of the next menstrual period.
Cycle regularity was assessed with the question: “How well
can you predict the date on which you will have another period;
that is, how regular is your cycle?” Participants provided
answers on a 9-point scale (1 ¼not at all,9¼completely),
with a higher score indicating more regular cycles.
Alternative explanations. In order to statistically control for the
effects of internal and environmental factors previously shown
to influence cycle characteristics or mating effort, we collected
measures of age, relationship status, BMI, and weekly exercise
frequency (Edelstein, Chopik, & Kean, 2011; Harlow & Mata-
noski, 1991; Mesko´, La´ng, & Kocsor, 2014). Participants indi-
cated their current relationship status by answering the
question: “What is your current relationship status?” using the
options single,casually dating someone,in a committed
relationship,engaged,ormarried. Participants also self-
reported height and weight from which BMI was calculated
). Finally, participants indicated how frequently they
exercised by providing an answer to the question: “How
many hours of exercise do you do in a typical week?” using
a whole number.
Data Analytic Plan
We first examined the data to determine whether all assump-
tions for accurate estimation using maximum likelihood were
met (MPlus Version 7.4 statistical software, Muth´en &
Muth´en, 1998–2012, Los Angeles, CA; Kline, 2016). Three
participants reported average cycle lengths greater than 3 stan-
dard deviations (SDs) above the mean (45 [þ3.67 SDs], 46
[þ3.91 SDs], and 54 [þ5.81 SDs] days). Because each of these
values was within the range of cycles recorded in prior research
(Mumford et al., 2012; Wilcox, Dunson, & Baird, 2000), it was
determined prior to analyses that these outliers would only be
removed if doing so significantly improved model fit. As aver-
age cycle length and the SOI-R scales were positively skewed,
we used the robust maximum likelihood estimator which is
robust to nonnormality in observed variables. We assessed
model fit using four fit indices: w
test of model fit, the com-
parative fit index (CFI), the root mean square error of approx-
imation (RMSEA), and the standardized root mean square
residual (SRMR). Adequate model fit was indicated by a non-
value (p> .05), a CFI value > .95, an RMSEA
value < .05, and an SRMR statistic < .05.
Confirmatory Factor Analysis (CFA)
Descriptive statistics for all observed variables are shown in
Table 1. Before we estimated the full hypothesized model (see
Figure 1 for model), we first conducted a CFA to test the
validity of the proposed model factor structure. We tested a
two-factor model with each SOI-R subscale indicating a latent
factor of mating effort and both cycle length and regularity
indicating a latent factor of cycle frequency. Due to a high
Table 1. Descriptive Statistics for Observed Variables.
Variable Range MSD
Estimated cycle length 22–54 29.57 4.20
Estimated cycle regularity 1–9 5.54 2.16
SOI behaviors 1–6.67 1.55 1.05
SOI attitudes 1–9 3.31 2.13
SOI desire 1–8.67 2.67 1.60
SPMV 1–7 4.42 1.20
Mini-K0.21–2.53 1.57 0.52
Age 17–30 19.55 2.15
BMI 18.36–31.70 22.68 2.63
Exercise frequency 0–11 4.10 2.52
Note. SOI ¼sociosexual orientation inventory; SPMV ¼self-perceived mate
value; BMI ¼body mass index.
correlation between the Attitude and Desire subscales of the
SOI-R, specifying a correlation between the errors of these
scales was necessary for model convergence and adequate
model fit (see Table 2 for model fit statistics). Removing cycle
length outliers did not improve model fit; these values were
thus included in subsequent analyses (see Table 2). All factor
loadings were significant (see Table 3).
Test of the Hypothesized Structural Model
All fit statistics indicated adequate model fit (see Table 2). As
shown in Figure 1, greater mating effort was independently
SE ¼.09, t¼3.86, p< .001, and faster life history strategies,
b¼.23, SE ¼.09, t¼2.49, p¼.01. Further, greater mating
effort predicted shorter, more regular cycles, b¼.31, SE ¼.09,
t¼3.29, p¼.001. Indirect effects on cycle frequency via
increased mating effort were significant for each expected mat-
ing success, b¼.11, SE ¼.05, t¼2.34, p¼.02, and life
history strategy, b¼.07, SE ¼.04, t¼1.98, p¼.047,
indicating that each uniquely predicts cycle length/regularity
through increased mating effort. Together, the model
accounted for 9.4%of the variance in cycle frequency.
Hypothesized Model With Covariates Included
The results of the hypothesized model remained significant
when controlling for age, BMI, relationship status, and exer-
cise level. All latent construct indicators were regressed on
each of these factors, and the test of the hypothesized model
was repeated. Results revealed that direct effects on mating
effort remained significant for both life history strategy,
b¼.25, SE ¼.09, t¼2.65, p¼.008, and projected
mating success, b¼.30, SE ¼.09, t¼3.22, p¼.001. Spe-
cifically, both a faster life history strategy and a higher pro-
jected ability to secure mating opportunities predicted greater
mating effort. The direct effect of mating effort on
cycle frequency also remained significant, b¼.31, SE ¼
.10, t¼3.07, p¼.002, indicating that greater mating effort
predicted shorter, more regular cycles. No covariates signifi-
cantly predicted cycle characteristics (ps > .17).
Figure 1. Final model with standardized estimates. *p< .05. **p< .01. ***p < .001.
Table 2. Summary of Model Fit Indices.
(df) CFI RMSEA SRMR
CFA model 3.61(3) 0.99 .03 [.00, .14] .03
CFA excluding outliers 5.92(3) 0.97 .07 [.00, .16] .04
Hypothesized model 9.18(11) 1.00 .00 [.00, .07] .04
Covariate model 7.56(11) 1.00 .00 [.00, .06] .02
Alternative model 22.42(11)* 0.88 .08 [.03, .12] .06
Note. CFA ¼confirmatory factor analysis; CFI ¼comparative fit index; RMSEA
¼root mean square error of approximation; SRMR ¼standardized root mean
*p< .05. **p< .01. ***p< .001.
Table 3. Estimates for Confirmatory Factor Analysis (CFA).
Variable Estimate SE p R
SOI attitudes .60 .18 .001 .36
SOI behaviors .65 .20 .001 .42
SOI desire .26 .12 .027 .07
Cycle regularity .61 .18 .001 .37
Cycle length .68 .20 <.001 .46
Note. Standardized estimated for CFA model, along with standard errors,
pvalues, and R
. SOI ¼sociosexual orientation inventory.
Gassen et al. 5
Test of an Alternative Model
We next conducted a test of the fit of an alternative model for
which the order of the mediator (mating effort) and outcome
variables (cycle frequency) was switched (see Figure 2). This
allowed us to statistically test whether cycle frequency is better
represented as a mediator of mating effort (rather than as an
outcome of mating effort, as our hypothesis specifies). Results
revealed poor model fit (see Table 2), supporting the proposed
directionality of the path between these variables.
Past research demonstrates considerable variability exists in
the length and regularity of the human menstrual cycle
(Chiazze et al., 1968; Creinin et al., 2004). Explanations for
this have mostly focused on the proximate role of disease states
and health issues, neglecting to consider adaptive reasons for
why such variability exists in healthy women. Here, we pro-
posed that variability in the human menstrual cycle may arise
as a function of the projected payoffs associated with invest-
ment in mating effort. Accordingly, we predicted that women
whose life history strategies or environments favored greater
mating effort would report shorter, more regular cycles. We
hypothesized that both cycle length and regularity would be
influenced by mating effort, as these characteristics each deter-
mine cycle frequency—and the number of total conception
The results of the current research supported our hypothesis.
Women with faster life history strategies and those expecting
more favorable mating outcomes reported investing more
effort in mating. Increased mating effort, in turn, predicted
shorter, more regular cycles. Importantly, these results were
robust to controlling for covariates that have previously been
found to impact cycle length and regularity, such as age, BMI,
and exercise. Using model fit as a guide, we also statistically
explored the alternative possibility that cycle frequency better
represented a predictor, rather than an outcome, of mating
effort. This alternative model was a poor fit to the current data,
lending support for the hypothesis that cycle frequency is cali-
brated to mating effort and not vice versa. In sum, our results
provide some of the first empirical evidence that human men-
strual cycle length and regularity may vary as a function of
Prior research investigating the factors that influence human
menstrual cycle characteristics has found that longer, more
irregular cycles are often associated with predictors of poor
health: rapid weight change, stress, and disorders such as dia-
betes (Kato et al., 1999; Matteo, 1987; Solomon et al., 2001).
Although not previously considered in this light, research such
as this provides additional support for the hypothesis that the
human menstrual cycle changes in response to cues that
increase or decrease the projected payoff from investing ener-
getic resources in mating effort. When energetic resources are
constrained or the costs of investing in mating are high and the
benefits low (e.g., when one is ill), longer, more irregular
cycles should result. Conversely, when the costs are relatively
low and the benefits high, shorter, more regular cycles should
occur. Exceptions to this pattern of health problems predicting
longer, more irregular cycles are the incidence of relatively
shorter cycles found in women with endometriosis, as well as
those with a history of psychiatric disorders, particularly anxi-
ety (Barron et al., 2008; Rowland et al., 2002). These excep-
tions, however, are not inconsistent with our predictive
framework. Endometriosis, for example, is associated with
greater exposure to estrogens, which play a critical role in
women’s sexual desire (Cappelletti & Wallen, 2016). Addition-
ally, research has identified anxiety as a psychological charac-
teristic of faster life history strategies (e.g., Chua et al., 2016),
Figure 2. Alternative model with standardized estimates. Nonsignificant paths denoted by dotted lines. *p< .05. **p< .01. ***p< .001.
lending further support for the hypothesis that life history char-
acteristics influence cycle frequency.
Limitations and Future Directions
The current research has several limitations. Although the
results of our alternative model supported the proposed direc-
tionality of our hypothesized model, the conclusions drawn
from the current study are limited by the nature of our cross-
sectional data. Future research would benefit from tracking
women’s cycles over time, as longitudinal designs are neces-
sary to confirm the causal chain from investment in mating
effort to adjustments in cycle frequency. Such studies exam-
ining the relationship between these factors might also
include measurements of health and somatic effort to examine
whether increases in mating effort are accompanied by trade-
offs in other domains of energetic investment. It should be
noted that all women in our study were nulliparous. Given that
nulliparous women are found to have shorter, more regular
cycles than women with children (Kato et al., 1999), the pres-
ent findings may not extend to women who have been preg-
nant. Longitudinal studies would also be able to examine
whether pregnancy influences relationships between mating
effort and cycle characteristics.
Next, our study was limited by our use of only one measure
of mating effort (i.e., sociosexual orientation). More research is
needed to examine relationships between cycle frequency and
mating effort across a broader range of variables representative
of one’s energetic investment in mating. Although sociosexual
orientation has been shown in previous research to be closely
related to important facets of mating effort, including one’s
lifetime number of sexual partners (Ostovich & Sabini, 2004)
and others (Kruger, 2017), future studies should assess whether
cycle frequency is also predicted by other aspects of mating
effort, such as sex steroid production, reproductive timing, or
costly mate attraction displays (e.g., luxury brand signaling,
conspicuous consumption; Griskevicius et al., 2007; Sundie
et al., 2011).
We only collected one measure of participants’ life history
strategies, the Mini-K. While some research has found conver-
gent validity between the Mini-Kand other life history mea-
sures (e.g., Dunkel & Decker, 2010), it should be noted that
others have recently suggested that this scale incompletely
captures the life history strategy construct (e.g., Richardson
et al., 2017). The results of the current research provide some
evidence for the validity of the Mini-K, given that it signifi-
cantly predicted mating effort—another indicator of one’s life
history strategy (Del Giudice et al., 2015). Nonetheless, cri-
tiques of the Mini-Kshould be considered when interpreting
the results of the current research.
Additional research is also needed to explore the hormonal
shifts underlying changes in mating effort and cycle character-
istics. Identifying the hormonal mechanisms involved in these
effects would make an important contribution to both research-
ers and clinicians interested in the biological correlates of sex-
ual behavior, fertility, and overall reproductive health.
Ovulation status, in particular, might be of particular interest
to future work given that longer cycle lengths are associated
with infertility (Rowland et al., 2002).
Finally, it should be noted that we asked women to self-
report the length and regularity of their cycles. Although mul-
tiple studies have successfully used self-reporting to measure
estimated cycle length (e.g., Barron et al., 2008; Rowland et al.,
2002; Wesselink et al., 2016), others find that such methods are
less accurate for those with very long or very short cycle
lengths, as well as those reporting high variability in cycle
lengths (Small, Manatunga, & Marcus, 2007). Future research
would benefit from validating self-reported cycle characteris-
tics with more objective measures, such as period tracking
phone applications or hormone analysis. These studies might
also collect a broader range of measures regarding factors that
potentially impact cycle length and regularity. Although we
accounted for many of these factors in our analyses and recruit-
ment (i.e., health, smoking, BMI, relationship status, age, and
exercise), there are others that may influence relationships
between mating effort and cycle characteristics (e.g., stress).
Despite these limitations, the current research provides an
important first step in establishing a general evolutionary the-
ory that accounts for cycle length and variability observed
between and within human females. These findings represent
some of the first empirical evidence that human menstrual
cycle characteristics are influenced by investment in mating
effort. Future work applying this predictive framework may
advance our understanding of the human menstrual cycle and
its relation to overall health.
The data associated with this research are promptly available from
corresponding author upon request. This research was approved
as ethical by the institutional review board at Texas Christian
We thank Randi P. Proffitt Leyva, Marjorie L. Prokosch, and Maggie
Kleiser for assistance with participant recruitment.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This work
was supported by National Science Foundation (NSF 1551201).
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