Influence of menstrual cycle phases on anaerobic physical performance of healthy women

Influence of menstrual cycle phases on anaerobic physical performance of healthy women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of menstrual cycle phases on anaerobic physical performance of healthy women Júlia Araujo Pavan, Maraysa Spagnollo da Silva, Germano Marcolino Putti, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9215036/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Purpose Women remain underrepresented in research due to the menstrual cycle’s complexity. Most studies use outdated methods, limiting insights into menstrual cycle effects on performance. Therefore, there is a need to expand the body of knowledge using modern and robust methodologies. The aim of this study was to investigate the anaerobic performance of women (18–35y) across different ovarian cycle phases and their influence on performance. The protocol was conducted in three cycle phases: early follicular (EF), late follicular (LF), and mid-luteal (ML) phase. Methods Sixteen volunteers underwent anthropometric measurements, maximal isometric strength test with rate of force development (RFD), horizontal jump (HJ), countermovement jump (CMJ), squat jump (SJ) and the running-based anaerobic sprint test (RAST). Results RFD and resultant force showed better performance in LF compared to ML (p = 0.015, ΔRFD:12.61%; p = 0.030, ΔResultant Force:12,17%); CMJ and HJ performed better in ML compared to EF (p = 0.002, ΔCMJ:5.7%; p = 0.001, ΔHJ:3.2%). RAST and anthropometric measurements showed no differences between phases. Conclusion It is suggested that strength efforts tend to improve in the late follicular phase, while coordinative and speed-related tasks seem to improve during ML. Recognizing that neuromuscular demands respond differently to hormonal fluctuations may help reduce performance variability and optimize outcomes by leveraging specific menstrual phases. Future research should also investigate the role of subjective and qualitative symptoms and assess whether/how they impact performance, even in similar hormonal profiles. Menstrual cycle Athletic training Strength and conditioning Exercise physiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Women are participating in sports universe in record numbers [ 1 , 2 ]. The vast majority of high-quality studies on sports and exercise have been derived from studies involving male participants, which limits the applicability of the data to women due to physiological differences between the sexes, specifically in relation to reproductive endocrinology [ 3 ]. Women between approximately 13–50 years experience the so-called menstrual cycle, in which hormones fluctuate predictably for an average of 28 days, with individual variations between 21 and 35 days [ 4 ]. There are two main phases: follicular phase and luteal phase. Based on fluctuations of estrogen and progesterone, three different hormonal environments can be identified: early follicular phase (low levels of estrogen and progesterone; days 1 to 4–6 of the cycle), late follicular phase (high estrogen and low progesterone; approximately between days 4–6 to day 13 of the cycle), and mid-luteal phase (high levels of estrogen and progesterone; approximately between days 20 and 23 of the cycle) [ 5 ]. Estrogen and progesterone levels cause various physiological effects in several systems of the human body, including cardiovascular, respiratory, metabolic, and neuromuscular parameters, which may have subsequent implications for athletic performance [ 6 ]. The biphasic responses of estrogen and progesterone throughout the menstrual cycle therefore influence physiological responses, which contribute to adaptation to exercise in women [ 7 ]. There may be physiological variations in thermoregulation, vascular dynamics, ventilation, and energy metabolism during exercise in women with endogenous cycles (i.e. hormones produced by the body’s own metabolic pathways) [ 8 ]. The impact of the menstrual cycle phases on aerobic performance has been extensively studied [ 9 ]. However, according to a recent narrative review [ 4 ], there is no consensus regarding the relationship between the menstrual cycle and anaerobic performance, and this may be due to the lack of adequate experimental controls and the wide variation in the methods used to determine the phase of the menstrual cycle, as well as the timing of testing during the menstrual cycle. Female performance throughout the menstrual cycle still generates debate, as the evidence is conflicting [ 7 ]. Only 4%–13% of studies on motor performance include exclusively women [ 2 , 5 ]. Ignoring hormonal complexity widens the gap in understanding the effects of sex hormones. Thus, there is a low female representation in the field of sports and exercise research [ 7 ], making it essential to conduct more research using stronger and reliable methodologies. Therefore, there is a need for studies with methodologies based on published recommendations [ 3 , 5 ] including only women. Thus, the objective of the study was to evaluate the anaerobic performance (maximum strength, power and repeated sprints ability) of healthy, physically active women at different phases of the ovarian cycle. The study aimed to test the hypothesis that strength and power would be higher in the late follicular phase, when estrogen peaks, and lower in the luteal phase, as progesterone is increased [ 4 ]. 2. Methods Data collection was conducted at the ( BLINDED FOR REVIEW PROCESS ), with a controlled environment and temperature, and near the ( BLINDED FOR REVIEW PROCESS ). 2.1. Participants Sample size was calculated a priori based on a study published about effect of menstrual cycle phase on multiple performance test parameters [ 10 ], standardizing a statistical power (1-β) of 0.8 and an alpha (α) of 0.05. The calculation was performed using G*Power 3.1.9.7 software (Düsseldorf, Germany) using the dependent t-test. Four variables reported by a recent study [ 10 ] were used in order to find the appropriate sample size for this study. Average power (effect size = 1.5, sample size of 5 participants) and peak power (effect size = 0.91, sample size of 9 participants) were tested in the Wingate anaerobic test, peak torque in the isokinetic muscle strength test of the extensors (effect size = 0.75, sample size of 13 participants), and knee flexors (effect size = 1.01, sample size of 8 participants). Thus, predicting a 20% sample loss, the final sample size was 16 participants, similar to the sample size used in previous research in the field [ 10 , 11 ]. Inclusion criteria were: age 18–35 years, regular endogenous ovarian cycles, and ≥ 8 weeks of regular strength training. Participants also performed ≥ 150 minutes of physical activity per week, verified by the International Physical Activity Questionnaire (IPAQ) [ 12 ]. Women using hormonal contraceptives (oral, injectable, intradermal, transcutaneous, hormonal intrauterine devices) or women with diseases that could affect the menstrual cycle (such as polycystic ovary syndrome, thyroid disorders, and endometriosis) were excluded from the study. Twenty-one participants began the study. However, five participants were excluded due to schedule conflicts and irregular cycles (Fig. 1 ). 2.2. Study overview A familiarization session was conducted before testing (day 1), totaling four meetings (Fig. 2 ). During familiarization, participants completed all physical tests in the planned order. A digital thermometer and urinary LH kit were provided, with instructions for the remainder of the study. The other three meetings (~ 90 minutes each) were scheduled across the menstrual cycle: early follicular phase (day 2; EF), late follicular phase (day 3; LF), and mid-luteal phase (day 4; ML). During this period, participants measured their temperature daily. Days 2–4 were scheduled according to each participant’s menstrual cycle and included electrical bioimpedance, maximal voluntary isometric contraction (MVIC), horizontal and vertical jump tests, and running-based anaerobic sprint test (RAST). Participants were instructed to avoid vigorous physical activity for 48 hours before testing and to refrain from alcohol, food, and stimulants. Source: Adapted from methodological recommendations for menstrual cycle research published [ 5 ]. 2.3. Methods for verification of menstrual cycle phase The methods for verifying the phase of the menstrual cycle used in this study were based on a recent review [ 5 ]. All three methods were used to identify the menstrual cycle phases: a calendar to estimate the first day of the cycle and ovulation, basal body temperature using a sensitive thermometer (0.05°C scale; FAMIVITA, São Paulo, Brazil) to identify whether there was an increase (approximately 0,03°C) in temperature, and urinary LH measurement to predict ovulation. To estimate the ovulation day, an ovulation predictor kit (FAMIVITA, São Paulo, Brazil) was used [ 5 ]. 2.4. Assessment sessions Tests were conducted in the following order: bioimpedance, MVIC, horizontal jump, counter movement jump, squat jump and RAST. 2.4.1. Bioimpedance Body composition was assessed using a Tetrapolar Electrical Bioimpedance device (AF Sanny-BIA1011AF). Following the manufacturer's instructions, participants were instructed to fast for 4 hours prior to assessment. 2.4.2. Warm-up Warm-up was standardized and performed after body composition assessment and before physical testing. It consisted of 10 minutes on the treadmill (TRG Progress T6, TRG Fitness, Brazil), with intensity based on subjective perception of effort 3–4 (Borg CR10 scale) [ 13 ], which was presented to the participants during the familiarization session. 2.4.3. Maximum voluntary isometric contraction test The MVIC test was performed on the guided bar of the Smith Machine using the half squat exercise position, with the knee joint at 90º [ 14 ]. Participants stood on two force platforms (Force Plate SVB, Cefise®, Sorocaba, Brazil). The bar was loaded to prevent movement, and they were instructed to push against it by applying force to the floor with their feet. Before testing, they performed two sets of three 3-second submaximal contractions (10 s between repetitions; 1 min between sets). After a 5-minute passive rest, they completed three 5-second maximal voluntary isometric contractions, with 1-minute rest between trials. During testing, they were instructed to contract “as hard as possible” and apply maximal force against the bar [ 15 ]. Data were recorded at 600 Hz and exported using specific software provided by the equipment manufacturer (Vertical Jump Power, Cefise®, Sorocaba, Brazil). The force-time curve was subsequently analyzed to obtain peak isometric force (IFpeak) and the rate of force development (RFD) at 30, 50, 90, 100, 150, 200, and 250 milliseconds [ 15 ]. For RFD analysis, the minimum resultant force (RF) value in the 5-second test was subtracted from all values, so that the baseline value was set at 0 N. In addition, outliers with very high RF values were noted, so a filter was applied, excluding value of RF > 10,000 N. A RF graph was plotted as a function of time, and the test start point was visually determined to be the last value before a sharp and continuous increase in RF values (Fig. 3 a). Of a total of 172 evaluations, 27 (16%) were discarded because they presented behaviors incompatible with the test that had been performed, probably due to an error in the data acquisition platform (Fig. 3 b). After this initial filtering, two evaluators independently selected the starting point of force production (T0) from an interface using the “shiny” package [ 16 ] of the R environment [ 17 ]. The scripts used are available on GitHub. RFD was calculated using Eq. 1: $$\:RFD\:\left(t\right)=\frac{RF\:\left(t\right)-RF\:(time={T}_{0})}{t-{T}_{0}}$$ , with t assuming the time values of 30, 50, 90, 100, 150, 200, and 250 ms [ 15 ]. The maximum resultant force value (RF MAX ) was chosen from the maximum RF value at t > T0. Tests (n = 63) that showed an inter-evaluator difference greater than 10% in any of the calculated RFD values were reanalyzed by the same evaluators, this time determining the T 0 point together. For tests with a difference of less than 10%, a simple average was calculated between values. Graph plotted in the R environment. The red arrow indicates the starting point of the test (determined to be the last valley before a sharp increase in the resultant force values). Evaluation discarded due to behavior incompatible with the test performed, since the test predicts a rapid increase in strength without interruptions. 2.4.4. Horizontal Jump The test was performed on a flat surface. A starting line was marked on the floor, from where all participants were instructed to begin the jump, with their feet behind the line. The volunteers were instructed to keep their feet slightly apart sideways, to jump as far as possible in a horizontal distance, performing a triple extension of the hip, knee, and ankle, along with the movement of the upper limbs [ 18 ]. Distance from the starting line to the nearest heel contact was measured with a tape measure (Stanley 34–263, United States). Three attempts were performed, and the best result was recorded. 2.4.5. Vertical jump tests Vertical jump tests were performed using the Jump System Pro sensor mat (Cefise, Sorocaba, Brazil), connected to electronic circuits that measure the time during which the subject is not in contact with the device while performing the jump [ 19 ]. The mat connects to the computer, compiling the data using Vertical Jump Power®ฏ - version 1.0.3.1 software (Vertical Jump Power, Cefise®, Nova Odessa, Brazil). For the squat jump (SJ), participants jumped vertically from a static squat position, assessing lower limb power [ 20 ]. Participants were instructed to stand with their feet parallel and hip-width apart, starting in an upright posture. They were asked to keep their hands on their hips to eliminate arm swing and to leave the ground with their knees and ankles extended and land in the same position and location to minimize horizontal displacement and influence on flight time [ 20 ]. Three attempts were performed, with 10 seconds between repetitions, and the best one was selected. For the countermovement jump test (CMJ), participants stood on the mat with the same feet and hands position as the SJ. They were instructed to perform a triple flexion (hip, knee, and ankle) from a standing position, followed by a triple extension and jump [ 21 ]. Three attempts were made, and the best one was used. 2.4.6. Running-based Anaerobic Sprint Test Participants underwent six maximum sprints of 35 meters on flat ground, with a 10-second passive rest [ 22 ]. To record the time of each effort, an Apple®ฏ smartphone (iPhone 13) was used, configured to film in 1080 p resolution and at a frame rate of 240 fps. The videos were analyzed, and the times were determined using Kinovea®ฏ software. Time was measured using the software's stopwatch tool, and two lines marked on the floor at the start and end of the 35 m course served as references for the participants. To minimize parallax error, a cone was placed behind the starting line and another in front of the finish line so that, from the video perspective, the moment the participant’s hip crossed the start/finish line coincided with the cone being visually occluded. Power generated (P) in each effort was estimated with the following Eq. 1 [22]: $$\:P\left(W\right)=\frac{Body\:mass\:\left(kg\right)\:x\:{Distance\:traveled\:\left(m\right)}^{2}}{{Time\:\left(s\right)}^{3}}$$ Parameters analyzed were: maximum power (Pmax; highest power among the 6 efforts), average power (Pmean; average of the powers of the 6 efforts), minimum power (Pmin; lowest power among the 6 efforts), total time (TT; sum of the time of the 6 efforts), and fatigue index (FI; W.s-1), according to the Eq. 2 [ 22 ]: $$\:FI\left(W.{s}^{-1}\right)=\:\frac{Pmax-Pmin}{TT}$$ 2.5. Statistical Analysis After checking for outliers using boxplots and graphs of individual values vs. cycle phases, data were analyzed using generalized linear mixed models [ 23 ]. Assumption of normality of the residuals was verified using a histogram and QQplot, while the assumption of homoscedasticity was verified using a graph of residuals vs. predicted values. The gaussian and gamma distributions were tested, both with the identity link function, and if models with the two distributions met the assumptions, the model with the lowest Akaike information criterion was chosen. In all variables analyzed, cycle phase was used as a fixed effect (with the EF as the reference phase) and participants as a random effect. For the RFD and RF data at a given time (RF(t)) of the predetermined intervals (i.e., 30, 50, 90, 100, 150, 200, and 250 ms), the time value was transformed into a categorical variable and used as a fixed effect. For RFD data (cycle phase x interval), an interaction (slope) effect was also included in the model. Due to the discrepancy in strength levels among the participants, these variables (RFD and RF(t)) log-transformed (log 10 ) before adjustment in the model, and hypothesis tests were performed on the log 10 means. For presentation of the results, the values are presented on the original scale—i.e., mean values of these variables in log 10 are geometric mean values on the original scale [ 24 ]. To verify differences between measurements or slopes, hypothesis tests were performed with Bonferroni post hoc correction. The significance level adopted for rejecting the null hypothesis was p < 0.05. The following R environment packages were used to perform the statistical analysis: 1) “lme4” for model fitting [ 23 ]; 2) “ggResidpanel” [ 25 ], for residual analysis; and 3) “emmeans” [ 26 ], for generating estimated means and comparisons. 3. RESULTS 3.1. Participants Table 1 describes information of the 16 participants who completed the study. Table 1 Personal information (mean ± SD) of participants (n = 16). Age (years) Body mass (kg) Cycle length (days) Menstruation length (days) 25.6 ± 4.99 63.33 ± 11.20 28.18 ± 1.28 5.13 ± 0.89 Tetrapolar Bioelectrical Impedance Analysis (AF Sanny-BIA1011AF) results across cycle phases are shown in Table 2 , with no differences observed in body composition variables. IPAQ data are presented in Table 3 . Based on the questionnaire, the participants were classified as recreationally active (n = 11) and trained/developmental (n = 5) [ 27 ]. Table 2 Bioimpedance information (mean ± SD) measured at each phase of the cycle. Phase n BW (kg) TBW (L) FFM (kg) BF (%) SMM (kg) BMI (kg/M²) EF 15 63.33 ± 10.56 32.46 ± 4.18 43.34 ± 3.33 33.12 ± 9.84 21.97 ± 1.68 23.14 ± 3.54 LF 12 63.61 ± 9.45 32.96 ± 3.76 43.85 ± 3.03 32.49 ± 8.81 22.38 ± 1.54 23.24 ± 3.17 ML 16 63.21 ± 10.9 32.46 ± 4.32 43.22 ± 3.46 32.92 ± 10.15 21.92 ± 1.72 23.10 ± 3.66 BW: Body Weight; TBW: Total Body Water; FFM: Fat-Free Mass; BF:Body Fat; SMM: Skeletal Muscle Mass; BMI: Body Mass Index. Table 3 IPAQ information (mean ± SD; n = 16). Work/ day (hours) Sleep/ night (hours) Walking as part of work/study (min/day) Walking in free time (min/day) Moderate activity in free time (min/day) Vigorous activity in free time (min/day Sitting time (hours/day) 5.8 ± 3.12 6.57 ± 1.08 28.18 ± 1.28 31.11 ± 14.74 73.93 ± 25.13 75.71 ± 50.19 5.38 ± 2.06 Rate of force development Mean RFD value (i.e., mean between all 7 predetermined time intervals, Fig. 4 a) was 12.61% higher in LF compared to ML (p = 0.0159). Individually, none of the 7 predetermined intervals differed between phases. Resultant force (Fig. 4 b) was 12.17% higher in the late follicular phase when compared to the luteal phase (p = 0.0299). 3.2. Jump tests Jumping performance was 5.7% higher for CMJ and 3.2% higher for HJ in the ML compared to the EF. Means (± SD) are shown in Table 4 . Table 4 Mean (± SD) jump height by menstrual cycle phase. CMJ (cm) Early folicular Late folicular Mid-luteal p 27.96 ± 5.69 n = 15 28.64 ± 5.87 n = 16 29.58 ± 5.87* n = 16 0.001 SJ (cm) 29.42 ± 7.91 n = 15 29.44 ± 8.17 n = 16 30.10 ± 7.92 n = 15 0.114 HJ (cm) 171.73 ± 33.51 n = 16 173.65 ± 33.52 n = 16 177.34 ± 33.54* n = 16 0.001 *p < 0.05 compared to early follicular phase. CMJ: Countermovement Jump; SJ: Squat Jump; HJ: Horizontal Jump. 3.4. Running-based Anaerobic Sprint Test No differences were observed between menstrual cycle phases for RAST variables (Fig. 5 ). 4. DISCUSSION Following best practice methodologies to establish menstrual cycle phases, the objective of the study was to evaluate the anaerobic performance of healthy, active women at different phases of the ovarian cycle. It was observed that physical performance differed throughout the cycle according to the test. The ability to perform repeated sprints did not change during menstrual cycle. Furthermore, RFD and RF performed better in LF compared to ML, and jumping performance (CMJ and HJ) was higher in ML compared to EF. The findings partly support the idea that strength and power should have better performance in the LF and lower performance in the ML. Considering that mean RFD value was 12.61% and RF was 12.17% higher in LF compared to ML, it is possible to conclude that strength was improved in LF. Furthermore, the ability to produce force rapidly depends predominantly on increased muscle activation at the onset of contraction [ 28 ], which is linked to RFD performance, assessed in the MVIC test. No difference was observed in the SJ test between the LF and the EF and ML phases. However, CMJ and HJ improved in ML compared to the EF. A study with male college weightlifters found no positive correlation between peak RFD and SJ test performance [ 29 ]. According to a review [ 30 ], there is no consensus on the influence of parameters such as peak RFD and time to peak RFD on sports performance. Furthermore, the ability to produce a higher RFD in initial movement intervals (0-100ms) is related to performance in various sports movements [ 30 ], however, no differences were found in this interval individually, only in average RFD. The RFD parameters studied here appear to have no influence on the participants' jumping performances, as RFD improves in LF compared to ML, while CMJ and HJ perform better in ML compared to EF. Unlike that was found in this manuscript, a recent study [ 31 ] found no differences during the menstrual cycle for CMJ height in elite Australian football athletes. The difference in participant characteristics may have influenced the result, since the study was conducted with high-level athletes, while the participants in the present study were physically active. One hypothesis for this difference is that the jumping tasks require greater inter and intramuscular coordination compared to the MVIC test. Since the participants were not necessarily trained for this task, the increase in RFD did not translate into jumping performance, masking a possible muscle recruitment effect. Furthermore, they [ 31 ] studied only two phases of the cycle: follicular phase (not subdivided into early and late) and luteal phase, using the calendar method and urine samples to check pregnanediol glucuronide (PdG) levels. It is possible that differences between studies are justified by the way cycle phases were divided (into 2 or 3 phases) and identified (calendar, urine (PdG), urinary LH measurement and basal temperature). Nevertheless, another study [ 32 ] also found better CMJ performance in the luteal phase: the mean relative concentric power was 16.8% higher in the luteal phase compared to the EF. However, the best SJ performance was in the EF compared to the luteal phase [ 32 ]. In this study, as in another recent ones, in sub-elite female soccer players [ 33 ] and with loaded jumps and in elite soccer athletes [ 34 ], no differences were observed between menstrual cycle phases for SJ. Studies involving horizontal jumps are a minority in recent literature. Based on the findings of a single study comparing the three menstrual cycle phases [ 35 ], the cycle does not appear to influence performance in the horizontal jump test. Another study [ 36 ] found a difference for the vertical jump in sedentary women. They had a better performance in the LF compared to the late luteal phase (days 26–28 of the cycle). Therefore, the phase of the cycle that demonstrated the peak of jumping performance (both horizontal and vertical jumps) was inconsistent among different studies [ 4 , 36 ]. One hypothesis for the increased jumping performance in the present study is that estradiol and progesterone have a role in augmenting and attenuating neuromuscular function, respectively [ 37 ]. A study that analyzed female rugby athletes (national level) [ 32 ] and found that an increase in estradiol concentration was associated with increased impulse at 200 ms during the CMJ, while elevated progesterone was associated with an increase in contraction time during SJ. Data from the present study suggest that coordinative tasks, involving strength and speed together, the luteal phase may be favored, when both estradiol and progesterone are elevated. Based on performance in MVIC test, results obtained in this study suggest that there may be an improvement in the performance of strength tasks in LF when these tasks are decontextualized from time. Tasks decontextualized from time were defined as those in which time remained constant (e.g., 5 s in the MVIC test), unlike a jump test, in which the speed of movement may influence performance. Previous studies suggest there is no influence of cycle phases on sprint performance. A study [ 38 ] found no difference between EF and ML in a test of three 30-meter sprints and another one [ 32 ] also found no difference in the 2x20m sprint test between phases, even when analyzing EF, LF, and ML. In addition, a study [ 39 ] conducted with men and women in the 20-second maximum bicycle test also concluded that hormonal changes during menstrual cycle do not influence anaerobic all-out performance in women. These results are similar to those found in the RAST performed in this study, in which no differences were found between phases for maximum and minimum values, or values related to the test in general (average power, total time, and fatigue index). The participants' performance in RAST is consistent with the results obtained in previous studies [ 32 , 34 , 38 , 39 ], suggesting that hormonal fluctuations do not influence the performance of repeated sprints in women. Among the limitations of this study is the lack of information regarding symptoms that may be related to the menstrual cycle—such as mood, pain (headache, lower back pain, etc.), fatigue, menstrual flow, and subjective perception of effort—which can affect physical performance and therefore need to be considered when related to athletic performance [ 40 ]. A study conducted with professional rugby players found that 93% of female athletes experienced negative symptoms related to their menstrual cycle [ 41 ]. A study that evaluated performance in two phases of the cycle (EF and ML) through time to exhaustion noted that women with high menstrual flow had a shorter time to exhaustion [ 42 ], which shows how menstrual symptoms can contribute to a change in performance that is not necessarily directly associated with hormone levels, but rather with perceived discomfort. Although studying three phases of the cycle, rather than just two, the present study did not verify the serum concentration of hormones related to the menstrual cycle. Despite this, the combined methods used in this study to identify menstrual cycle phases represent some of the least invasive and most cost-effective approaches currently reported in the literature [ 5 ]. Another study limitation was the heterogeneity in the participants' training levels (recreationally active and trained/developmental), evidenced especially by the differences in the maximum isometric strength test. Although one of the inclusion criteria was to have a regular strength training routine of at least 8 weeks, some tests involving a higher level of coordination, such as sprints or jumps, may be affected by the level of physical fitness, regardless of hormonal fluctuations. Nevertheless, the heterogeneity may not be as relevant in the context of the phenomenon, as the participants largely shared familiarity with the analysis tools. Future studies should continue to evaluate menstrual cycle phases in a subdivided manner, level the participants' training level, and investigate the role of qualitative/subjective symptoms. 5. CONCLUSION Anaerobic performance in different menstrual cycle phases did not change uniformly, showing differences depending on the aspect of strength analyzed. Actions related to maximum strength, such as rate of force development and resultant force (MVIC test), tend to be better during the late follicular phase. On the other hand, actions related to rapid strength, such as CMJ and HJ, performed better during the mid-luteal phase, while the ability to perform repeated sprints did not change throughout the cycle. Therefore, based on the results obtained in this study, it is suggested that strength efforts decontextualized from time seem to perform better in the follicular phase, while coordinative and fast tasks seem to be improved in the luteal phase in healthy and physically active women. Further studies are needed to identify the influence of the menstrual cycle on the physical performance of healthy women. Declarations Conflict of interest Authors declare they have no competing financial or non-financial interests. Ethical approval The study was conducted according to the guidelines of the Declaration of Helsinki and approved by Research Ethics Committee of the [BLINDED FOR REVIEW PROCESS]. Informed consent Written informed consent was obtained from all individual participants prior to participation. Funding This research was financed by [BLINDED FOR REVIEW PROCESS]. Author Contribution Conceptualization: E.F.P., J.A.P.; Data curation: M.S.S., N.A.O.M., G.M.P., J.A.P.; Formal analysis: G.M.P., J.A.P.; Funding acquisition: E.F.P., J.A.P.; Investigation: N.A.O.M., M.S.S., J.A.P.; Methodology: E.F.P., J.A.P.; Project administration: E.F.P., J.A.P.; Resources: E.F.P., J.A.P.; Software: G.M.P.; Supervision: E.F.P.; Validation: E.F.P., G.M.P., J.A.P.; Visualization: E.F.P., G.M.P., J.A.P.; Writing – original draft: J.A.P.; Writing – review & editing: E.F.P, M.S.S., G.M.P., J.A.P. Data Availability The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. References Hulteen RM, Smith JJ, Morgan PJ, Barnett LM, Hallal PC, Colyvas K, et al. Global participation in sport and leisure-time physical activities: A systematic review and meta-analysis. Preventive Medicine. Elsevier Inc; 2017;95:14–25. https://doi.org/10.1016/j.ypmed.2016.11.027 Janse de Jonge X, Minahan C. Physiology and Performance Research in Female Athletes: Bridging the Gap Between Opportunity and Evidence-Based Support. IJSPP. 2025;20:181–2. https://doi.org/10.1123/ijspp.2024-0540 Elliott-Sale KJ, Minahan CL, de Jonge XAKJ, Ackerman KE, Sipilä S, Constantini NW, et al. Methodological Considerations for Studies in Sport and Exercise Science with Women as Participants: A Working Guide for Standards of Practice for Research on Women. Sports Medicine. Springer International Publishing; 2021;51:843–61. https://doi.org/10.1007/s40279-021-01435-8 Carmichael MA, Thomson RL, Moran LJ, Wycherley TP. The impact of menstrual cycle phase on athletes’ performance: a narrative review. International Journal of Environmental Research and Public Health. 2021a;18:1–24. https://doi.org/10.3390/ijerph18041667 Janse de Jonge X, Thompson B, Han A. Methodological Recommendations for Menstrual Cycle Research in Sports and Exercise. Medicine and science in sports and exercise. 2019;51:2610–7. https://doi.org/10.1249/MSS.0000000000002073 McNulty KL, Elliott-Sale KJ, Dolan E, Swinton PA, Ansdell P, Goodall S, et al. The Effects of Menstrual Cycle Phase on Exercise Performance in Eumenorrheic Women: A Systematic Review and Meta – Analysis. Sports Medicine. Springer International Publishing; 2020; https://doi.org/10.1007/s40279-020-01319-3 Sims ST, Heather AK. Myths and Methodologies: Reducing scientific design ambiguity in studies comparing sexes and/or menstrual cycle phases. Experimental Physiology. 2018;103:1309–17. https://doi.org/10.1113/EP086797 Lebrun CM. Effects of the Menstrual Cycle and Oral Contraceptives on Sports Performance. Women in Sport. 1993;16:37–61. https://doi.org/10.1002/9780470757093.ch3 Redman LM, Weatherby RP. Measuring Performance during the Menstrual Cycle: A Model Using Oral Contraceptives. Medicine & Science in Sports & Exercise. 2004;130–6. https://doi.org/10.1249/01.MSS.0000106181.52102.99 Oğul A, Ercan S, Ergan M, İnce Parpucu T, Çetin C. The effect of menstrual cycle phase on multiple performance test parameters. Turkish Journal of Sports Medicine. 2021;56:159–65. https://doi.org/10.47447/tjsm.0552 Yapici-Oksuzoglu A, Egesoy H. The effect of menstrual cycle on anaerobic power and jumping performance. Pedagogy of Physical Culture and Sports. 2021;25:367–72. https://doi.org/10.15561/26649837.2021.0605 Vespasiano BDS, Dias R, Correa DA. a utilização do Questionário Internacional de Atividade Física (IPAQ) como ferramenta diagnóstica do nível de aptidão física: uma revisão no Brasil. Saúde em Revista. 2012;12:49–54. Borg GAV. Psychophysical bases of perceived exertion. Medicine and Science on Sports and Exercise. 1982;14:377–81. Escamilla RF. Knee biomechanics of the dynamic squat exercise. Medicine and science in sports and exercise. 2001;33:127–41. https://doi.org/10.1097/00005768-200101000-00020 Haff GG, Ruben RP, Lider J, Twine C, Cormie P. A comparison of methods for determining the rate of force development during isometric midthigh clean pulls. Journal of Strength and Conditioning Research. 2015;29:386–95. Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, et al. _shiny: Web Application Framework for R_ [Internet]. 2024. https://CRAN.R-project.org/package=shiny R Core Team. A Language and Environment for Statistical Computing [Internet]. Vienna, Áustria: R Foundation for Statistical Computing; 2024. https://www.R-project.org/ Moreira A, Oliveira PR De, Okano AH, Souza M De. A dinâmica de alteração das medidas de força e o efeito posterior duradouro de treinamento em basquetebolistas submetidos ao sistema de treinamento em bloco. Revista Brasileira de Medicina do Esporte. 2004;10:243–50. Bosco C, Belli A, Astrua M, Tihanyi J, Pozzo R, Kellis S, et al. A dynamometer for evaluation of dynamic muscle work. Eur J Appl Physiol Occup Physiol. 1995;70:379–86. https://doi.org/10.1007/BF00618487 Meylan C, Malatesta D. Effects of In-season Plyometric Training Within Soccer Practice on Explosive Actions of Young Players. Journal of Strength and Conditioning Research. 2009;23:2605–13. Smirniotou A, Katsikas C, Paradisis G, Argeitaki P, Zacharogiannis E, Tziortzis S. Strength-power parameters as predictors of sprinting performance.pdf. The Journal of Sports Medicine and Physical Fitness. 2008;48:447. Roseguini AZ, Silva ASR da, Gobatto CA. Determinações e Relações dos Parâmetros Anaeróbios do RAST, do Limiar Anaeróbio e da Resposta Lactacidemica Obtida no Início, no Intervalo e ao Final de uma Partida Oficial de Handebol. Revista Brasileira de Medicina do Esporte. 2008;14:46–50. Bates D, Maechler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67:1–48. https://doi.org/10.18637/jss.v067.i01 Olivier J, Johnson WD, Marshall GD. The logarithmic transformation and the geometric mean in reporting experimental IgE results: what are they and when and why to use them? Annals of Allergy, Asthma & Immunology. 2008;100:333–7. https://doi.org/10.1016/S1081-1206(10)60595-9 Goode K, Rey K. _ggResidpanel: Panels and Interactive Versions of Diagnostic Plots using ’ggplot2’_ [Internet]. 2019. https://CRAN.R-project.org/package=ggResidpanel Lenth R. _emmeans: Estimated Marginal Means, aka Least-Squares Means_ [Internet]. 2024. %3Chttps://CRAN.R-project.org/package=emmeans%3E McKay AKA, Stellingwerff T, Smith ES, Martin DT, Mujika I, Goosey-Tolfrey V, et al. Defining Training and Performance Caliber: A Participant Classification Framework. nternational Journal of Sports Physiology and Performance. 2022;17:317–31. https://doi.org/10.1123/ijspp.2021-0451 Maffiuletti NA, Aagaard P, Blazevich AJ, Folland J, Tillin N, Duchateau J. Rate of force development: physiological and methodological considerations. Eur J Appl Physiol. 2016;116:1091–116. https://doi.org/10.1007/s00421-016-3346-6 Kawamori N, Rossi SJ, Justice BD, Haff EE, Pistilli EE, O’Bryant HS, et al. Peak force and rate of force development during isometric and dynamic mid-thigh clean pulls performed at various intensities. Journal of Strength and Conditioning Research. 2006;20:483–91. https://doi.org/10.1519/18025.1 Hernández-Davó J, Sabido R. Rate of force development: reliability, improvements and influence on performance. A review. European Journal of Human Movement. 2014;33:46–69. Carmichael MA, Thomson RL, Moran LJ, Dunstan JR, Nelson MJ, Mathai ML, et al. A pilot study on the impact of menstrual cycle phase on elite australian football athletes. International Journal of Environmental Research and Public Health. 2021b;18. https://doi.org/10.3390/ijerph18189591 Smith ES, Weakley J, McKay AKA, McCormick R, Tee N, Kuikman MA, et al. Minimal influence of the menstrual cycle or hormonal contraceptives on performance in female rugby league athletes. European Journal of Sport Science. 2024;24:1067–78. https://doi.org/10.1002/ejsc.12151 Igonin PH, Cognasse F, Gonzalo P, Philippot P, Rogowski I, Sabot T, et al. Monitoring of sprint and change of direction velocity, vertical jump height, and repeated sprint ability in sub-elite female football players throughout their menstrual cycle. Science and Medicine in Football. Routledge; 2024;8:418–26. https://doi.org/10.1080/24733938.2024.2328674 Bouvier J, Igonin P-H, Boithias M, Fouré A, Belli A, Boisseau N, et al. The squat jump and sprint force–velocity profiles of elite female football players are not influenced by the menstrual cycle phases and oral contraceptive use. Eur J Appl Physiol [Internet]. 2025 [cited 2025 Feb 24]; https://doi.org/10.1007/s00421-025-05723-3 Davies BN, Elford JC, Jamieson KF. Variations in performance in simple muscle tests at different phases of the menstrual cycle. J Sports Med Phys Fitness. 1991;31:532–7. Tasmektepligil MY, Agaoglu SA, Türkmen L, Türkmen M. The motor performance and some physical characteristics of the sportswomen and sedentary lifestyle women during menstrual cycle. Archives of Budo. 2010;6:195–203. Pallavi LC, D Souza UJ, Shivaprakash G. Assessment of Musculoskeletal Strength and Levels of Fatigue during Different Phases of Menstrual Cycle in Young Adults. Journal of Clinical and Diagnostic Research. 2017;11:11–3. https://doi.org/10.7860/JCDR/2017/24316.9408 Julian R, Hecksteden A, Fullagar HHK, Meyer T. The effects of menstrual cycle phase on physical performance in female soccer players. PLoS One. 2017;12:e0173951. https://doi.org/10.1371/journal.pone.0173951 Wiecek M, Szymura J, Maciejczyk M, Cempla J, Szygula Z. Effect of sex and menstrual cycle in women on starting speed, anaerobic endurance and muscle power. Physiology International. 2016;103:127–32. https://doi.org/10.1556/036.103.2016.1.13 Minahan C. Menstrual cycles and macrocycles: Science, not socials, is doing the heavy lifting. The Journal of Physiology. 2025;603:1023–4. https://doi.org/10.1113/JP288096 Findlay RJ, Macrae EHR, Whyte IY, Easton C, Forrest Née Whyte LJ. How the menstrual cycle and menstruation affect sporting performance: experiences and perceptions of elite female rugby players. Br J Sports Med. 2020;54:1108–13. https://doi.org/10.1136/bjsports-2019-101486 de Carvalho G, Papoti M, Rodrigues MCD, Foresti YF, De Oliveira Guirro EC, De Jesus Guirro RR. Interaction predictors of self-perception menstrual symptoms and influence of the menstrual cycle on physical performance of physically active women. Eur J Appl Physiol [Internet]. Springer Science and Business Media LLC; 2023 [cited 2025 July 30]; https://doi.org/10.1007/s00421-022-05086-z Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 24 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9215036","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615321661,"identity":"b7510737-b78b-4bef-98cc-2454319ae468","order_by":0,"name":"Júlia Araujo Pavan","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Júlia","middleName":"Araujo","lastName":"Pavan","suffix":""},{"id":615321667,"identity":"8535787a-3d81-492b-81dd-e092d7c29504","order_by":1,"name":"Maraysa Spagnollo da Silva","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Maraysa","middleName":"Spagnollo da","lastName":"Silva","suffix":""},{"id":615321669,"identity":"91bf2824-ff96-418b-be41-e3a6d15a3dde","order_by":2,"name":"Germano Marcolino Putti","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Germano","middleName":"Marcolino","lastName":"Putti","suffix":""},{"id":615321671,"identity":"024598a5-9918-4c4f-adf5-079426d5f7d2","order_by":3,"name":"Nick Anderson Okuyama Marchi","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Nick","middleName":"Anderson Okuyama","lastName":"Marchi","suffix":""},{"id":615321672,"identity":"c2374cad-7ce1-4283-9e21-db1a758a8fdb","order_by":4,"name":"Enrico Fuini Puggina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnklEQVRIiWNgGAWjYJCCAwwMNiCajSQtaSRqAYLDJGjhl+59eOBHxfnE+bMb2B5XEKNFcs5xg4M9Z24nbrhzgN3wDDFaDG6kMRxmbANqkUhgk2wgRos9RMu5xPkziNViIAHWciCx4QaxWiSAtgD9kmy84c7BdkOitPDPSGP+8KPCTnb+7OZjD4nSgmQfI4kagFpI1TAKRsEoGAUjBgAAKik0nSFexPcAAAAASUVORK5CYII=","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":true,"prefix":"","firstName":"Enrico","middleName":"Fuini","lastName":"Puggina","suffix":""}],"badges":[],"createdAt":"2026-03-24 17:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9215036/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9215036/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106101400,"identity":"00271ce9-716e-4d6b-8e28-c9f88e932058","added_by":"auto","created_at":"2026-04-03 12:48:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":206214,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of participants through each stage of the study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9215036/v1/744da2c2e483319fca3ec360.png"},{"id":106101401,"identity":"457c1b8d-30c1-4c47-b46b-81019538c940","added_by":"auto","created_at":"2026-04-03 12:48:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":242057,"visible":true,"origin":"","legend":"\u003cp\u003eScheme of methodological steps to verify menstrual cycle phases and meetings during the evaluation period (familiarization day and other 3 meetings).\u003c/p\u003e\n\u003cp\u003eSource: Adapted from methodological recommendations for menstrual cycle research published [5].\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9215036/v1/2dea1ae91d274292ef2d322b.png"},{"id":106401868,"identity":"7203dd0d-5d51-4fe5-8b2d-34bbf3308d49","added_by":"auto","created_at":"2026-04-08 09:10:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107080,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of a RF vs. time graph evaluated (3a) and a discarded evaluation (3b).\u003cbr\u003e\nGraph plotted in the R environment. The red arrow indicates the starting point of the test (determined to be the last valley before a sharp increase in the resultant force values). Evaluation discarded due to behavior incompatible with the test performed, since the test predicts a rapid increase in strength without interruptions.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9215036/v1/7bbc6d78100b174e840ffd34.png"},{"id":106101402,"identity":"34b48463-3579-4c62-94c3-7d42fd0c12fe","added_by":"auto","created_at":"2026-04-03 12:48:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":158049,"visible":true,"origin":"","legend":"\u003cp\u003eMean rate of force development (4a) and resultant force (4b) during menstrual cycle phases. Each grey dot represents an individual participant during a menstrual cycle phase, indicated by X axis phase. Each black dot represents the mean variable value of that specific phase. EF: early follicular phase; LF: late follicular phase; ML: mid-luteal phase; RFD: rate of force development; *: p\u0026lt;0,05 (difference to mid-luteal phase).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9215036/v1/2efa8e04770e5894e96216ca.png"},{"id":106101404,"identity":"7f5a2f60-e119-42c4-961a-bf2124328446","added_by":"auto","created_at":"2026-04-03 12:48:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":364029,"visible":true,"origin":"","legend":"\u003cp\u003eFatigue index, total time, and maximum, minimum, and average power during the RAST throughout the menstrual cycle. EF: early follicular phase; LF: late follicular phase; ML: mid-luteal phase.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9215036/v1/3497d09fb7bfbad7a29578f4.png"},{"id":106405515,"identity":"90acc369-20d9-4866-a9c6-8d13e30dde3b","added_by":"auto","created_at":"2026-04-08 09:26:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1966393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9215036/v1/3dabf1a0-12f1-42e2-951a-de6e76fa11ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of menstrual cycle phases on anaerobic physical performance of healthy women","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWomen are participating in sports universe in record numbers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The vast majority of high-quality studies on sports and exercise have been derived from studies involving male participants, which limits the applicability of the data to women due to physiological differences between the sexes, specifically in relation to reproductive endocrinology [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWomen between approximately 13\u0026ndash;50 years experience the so-called menstrual cycle, in which hormones fluctuate predictably for an average of 28 days, with individual variations between 21 and 35 days [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. There are two main phases: follicular phase and luteal phase. Based on fluctuations of estrogen and progesterone, three different hormonal environments can be identified: early follicular phase (low levels of estrogen and progesterone; days 1 to 4\u0026ndash;6 of the cycle), late follicular phase (high estrogen and low progesterone; approximately between days 4\u0026ndash;6 to day 13 of the cycle), and mid-luteal phase (high levels of estrogen and progesterone; approximately between days 20 and 23 of the cycle) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEstrogen and progesterone levels cause various physiological effects in several systems of the human body, including cardiovascular, respiratory, metabolic, and neuromuscular parameters, which may have subsequent implications for athletic performance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The biphasic responses of estrogen and progesterone throughout the menstrual cycle therefore influence physiological responses, which contribute to adaptation to exercise in women [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. There may be physiological variations in thermoregulation, vascular dynamics, ventilation, and energy metabolism during exercise in women with endogenous cycles (i.e. hormones produced by the body\u0026rsquo;s own metabolic pathways) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe impact of the menstrual cycle phases on aerobic performance has been extensively studied [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, according to a recent narrative review [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], there is no consensus regarding the relationship between the menstrual cycle and anaerobic performance, and this may be due to the lack of adequate experimental controls and the wide variation in the methods used to determine the phase of the menstrual cycle, as well as the timing of testing during the menstrual cycle.\u003c/p\u003e \u003cp\u003eFemale performance throughout the menstrual cycle still generates debate, as the evidence is conflicting [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Only 4%\u0026ndash;13% of studies on motor performance include exclusively women [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Ignoring hormonal complexity widens the gap in understanding the effects of sex hormones. Thus, there is a low female representation in the field of sports and exercise research [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], making it essential to conduct more research using stronger and reliable methodologies.\u003c/p\u003e \u003cp\u003eTherefore, there is a need for studies with methodologies based on published recommendations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] including only women. Thus, the objective of the study was to evaluate the anaerobic performance (maximum strength, power and repeated sprints ability) of healthy, physically active women at different phases of the ovarian cycle. The study aimed to test the hypothesis that strength and power would be higher in the late follicular phase, when estrogen peaks, and lower in the luteal phase, as progesterone is increased [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eData collection was conducted at the (\u003cb\u003eBLINDED FOR REVIEW PROCESS\u003c/b\u003e), with a controlled environment and temperature, and near the (\u003cb\u003eBLINDED FOR REVIEW PROCESS\u003c/b\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eSample size was calculated a priori based on a study published about effect of menstrual cycle phase on multiple performance test parameters [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], standardizing a statistical power (1-β) of 0.8 and an alpha (α) of 0.05. The calculation was performed using G*Power 3.1.9.7 software (D\u0026uuml;sseldorf, Germany) using the dependent t-test. Four variables reported by a recent study [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] were used in order to find the appropriate sample size for this study. Average power (effect size\u0026thinsp;=\u0026thinsp;1.5, sample size of 5 participants) and peak power (effect size\u0026thinsp;=\u0026thinsp;0.91, sample size of 9 participants) were tested in the Wingate anaerobic test, peak torque in the isokinetic muscle strength test of the extensors (effect size\u0026thinsp;=\u0026thinsp;0.75, sample size of 13 participants), and knee flexors (effect size\u0026thinsp;=\u0026thinsp;1.01, sample size of 8 participants). Thus, predicting a 20% sample loss, the final sample size was 16 participants, similar to the sample size used in previous research in the field [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInclusion criteria were: age 18\u0026ndash;35 years, regular endogenous ovarian cycles, and \u0026ge;\u0026thinsp;8 weeks of regular strength training. Participants also performed\u0026thinsp;\u0026ge;\u0026thinsp;150 minutes of physical activity per week, verified by the International Physical Activity Questionnaire (IPAQ) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Women using hormonal contraceptives (oral, injectable, intradermal, transcutaneous, hormonal intrauterine devices) or women with diseases that could affect the menstrual cycle (such as polycystic ovary syndrome, thyroid disorders, and endometriosis) were excluded from the study.\u003c/p\u003e \u003cp\u003eTwenty-one participants began the study. However, five participants were excluded due to schedule conflicts and irregular cycles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study overview\u003c/h2\u003e \u003cp\u003eA familiarization session was conducted before testing (day 1), totaling four meetings (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During familiarization, participants completed all physical tests in the planned order. A digital thermometer and urinary LH kit were provided, with instructions for the remainder of the study. The other three meetings (~\u0026thinsp;90 minutes each) were scheduled across the menstrual cycle: early follicular phase (day 2; EF), late follicular phase (day 3; LF), and mid-luteal phase (day 4; ML). During this period, participants measured their temperature daily. Days 2\u0026ndash;4 were scheduled according to each participant\u0026rsquo;s menstrual cycle and included electrical bioimpedance, maximal voluntary isometric contraction (MVIC), horizontal and vertical jump tests, and running-based anaerobic sprint test (RAST). Participants were instructed to avoid vigorous physical activity for 48 hours before testing and to refrain from alcohol, food, and stimulants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Adapted from methodological recommendations for menstrual cycle research published [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Methods for verification of menstrual cycle phase\u003c/h2\u003e \u003cp\u003eThe methods for verifying the phase of the menstrual cycle used in this study were based on a recent review [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. All three methods were used to identify the menstrual cycle phases: a calendar to estimate the first day of the cycle and ovulation, basal body temperature using a sensitive thermometer (0.05\u0026deg;C scale; FAMIVITA, S\u0026atilde;o Paulo, Brazil) to identify whether there was an increase (approximately 0,03\u0026deg;C) in temperature, and urinary LH measurement to predict ovulation. To estimate the ovulation day, an ovulation predictor kit (FAMIVITA, S\u0026atilde;o Paulo, Brazil) was used [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Assessment sessions\u003c/h2\u003e \u003cp\u003eTests were conducted in the following order: bioimpedance, MVIC, horizontal jump, counter movement jump, squat jump and RAST.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Bioimpedance\u003c/h2\u003e \u003cp\u003eBody composition was assessed using a Tetrapolar Electrical Bioimpedance device (AF Sanny-BIA1011AF). Following the manufacturer's instructions, participants were instructed to fast for 4 hours prior to assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Warm-up\u003c/h2\u003e \u003cp\u003eWarm-up was standardized and performed after body composition assessment and before physical testing. It consisted of 10 minutes on the treadmill (TRG Progress T6, TRG Fitness, Brazil), with intensity based on subjective perception of effort 3\u0026ndash;4 (Borg CR10 scale) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which was presented to the participants during the familiarization session.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Maximum voluntary isometric contraction test\u003c/h2\u003e \u003cp\u003eThe MVIC test was performed on the guided bar of the Smith Machine using the half squat exercise position, with the knee joint at 90\u0026ordm; [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Participants stood on two force platforms (Force Plate SVB, Cefise\u0026reg;, Sorocaba, Brazil). The bar was loaded to prevent movement, and they were instructed to push against it by applying force to the floor with their feet. Before testing, they performed two sets of three 3-second submaximal contractions (10 s between repetitions; 1 min between sets). After a 5-minute passive rest, they completed three 5-second maximal voluntary isometric contractions, with 1-minute rest between trials. During testing, they were instructed to contract \u0026ldquo;as hard as possible\u0026rdquo; and apply maximal force against the bar [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Data were recorded at 600 Hz and exported using specific software provided by the equipment manufacturer (Vertical Jump Power, Cefise\u0026reg;, Sorocaba, Brazil). The force-time curve was subsequently analyzed to obtain peak isometric force (IFpeak) and the rate of force development (RFD) at 30, 50, 90, 100, 150, 200, and 250 milliseconds [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor RFD analysis, the minimum resultant force (RF) value in the 5-second test was subtracted from all values, so that the baseline value was set at 0 N. In addition, outliers with very high RF values were noted, so a filter was applied, excluding value of RF\u0026thinsp;\u0026gt;\u0026thinsp;10,000 N. A RF graph was plotted as a function of time, and the test start point was visually determined to be the last value before a sharp and continuous increase in RF values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Of a total of 172 evaluations, 27 (16%) were discarded because they presented behaviors incompatible with the test that had been performed, probably due to an error in the data acquisition platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). After this initial filtering, two evaluators independently selected the starting point of force production (T0) from an interface using the \u0026ldquo;shiny\u0026rdquo; package [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] of the R environment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The scripts used are available on GitHub. RFD was calculated using Eq.\u0026nbsp;1:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:RFD\\:\\left(t\\right)=\\frac{RF\\:\\left(t\\right)-RF\\:(time={T}_{0})}{t-{T}_{0}}$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewith t assuming the time values of 30, 50, 90, 100, 150, 200, and 250 ms [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The maximum resultant force value (RF\u003csub\u003eMAX\u003c/sub\u003e) was chosen from the maximum RF value at t\u0026thinsp;\u0026gt;\u0026thinsp;T0.\u003c/p\u003e \u003cp\u003eTests (n\u0026thinsp;=\u0026thinsp;63) that showed an inter-evaluator difference greater than 10% in any of the calculated RFD values were reanalyzed by the same evaluators, this time determining the T\u003csub\u003e0\u003c/sub\u003e point together. For tests with a difference of less than 10%, a simple average was calculated between values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGraph plotted in the R environment. The red arrow indicates the starting point of the test (determined to be the last valley before a sharp increase in the resultant force values). Evaluation discarded due to behavior incompatible with the test performed, since the test predicts a rapid increase in strength without interruptions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Horizontal Jump\u003c/h2\u003e \u003cp\u003eThe test was performed on a flat surface. A starting line was marked on the floor, from where all participants were instructed to begin the jump, with their feet behind the line. The volunteers were instructed to keep their feet slightly apart sideways, to jump as far as possible in a horizontal distance, performing a triple extension of the hip, knee, and ankle, along with the movement of the upper limbs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Distance from the starting line to the nearest heel contact was measured with a tape measure (Stanley 34\u0026ndash;263, United States). Three attempts were performed, and the best result was recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5. Vertical jump tests\u003c/h2\u003e \u003cp\u003eVertical jump tests were performed using the Jump System Pro sensor mat (Cefise, Sorocaba, Brazil), connected to electronic circuits that measure the time during which the subject is not in contact with the device while performing the jump [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The mat connects to the computer, compiling the data using Vertical Jump Power\u0026reg;ฏ - version 1.0.3.1 software (Vertical Jump Power, Cefise\u0026reg;, Nova Odessa, Brazil).\u003c/p\u003e \u003cp\u003eFor the squat jump (SJ), participants jumped vertically from a static squat position, assessing lower limb power [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Participants were instructed to stand with their feet parallel and hip-width apart, starting in an upright posture. They were asked to keep their hands on their hips to eliminate arm swing and to leave the ground with their knees and ankles extended and land in the same position and location to minimize horizontal displacement and influence on flight time [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Three attempts were performed, with 10 seconds between repetitions, and the best one was selected.\u003c/p\u003e \u003cp\u003eFor the countermovement jump test (CMJ), participants stood on the mat with the same feet and hands position as the SJ. They were instructed to perform a triple flexion (hip, knee, and ankle) from a standing position, followed by a triple extension and jump [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Three attempts were made, and the best one was used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.6. Running-based Anaerobic Sprint Test\u003c/h2\u003e \u003cp\u003eParticipants underwent six maximum sprints of 35 meters on flat ground, with a 10-second passive rest [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To record the time of each effort, an Apple\u0026reg;ฏ smartphone (iPhone 13) was used, configured to film in 1080 p resolution and at a frame rate of 240 fps. The videos were analyzed, and the times were determined using Kinovea\u0026reg;ฏ software. Time was measured using the software's stopwatch tool, and two lines marked on the floor at the start and end of the 35 m course served as references for the participants. To minimize parallax error, a cone was placed behind the starting line and another in front of the finish line so that, from the video perspective, the moment the participant\u0026rsquo;s hip crossed the start/finish line coincided with the cone being visually occluded. Power generated (P) in each effort was estimated with the following Eq.\u0026nbsp;1 [22]:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:P\\left(W\\right)=\\frac{Body\\:mass\\:\\left(kg\\right)\\:x\\:{Distance\\:traveled\\:\\left(m\\right)}^{2}}{{Time\\:\\left(s\\right)}^{3}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eParameters analyzed were: maximum power (Pmax; highest power among the 6 efforts), average power (Pmean; average of the powers of the 6 efforts), minimum power (Pmin; lowest power among the 6 efforts), total time (TT; sum of the time of the 6 efforts), and fatigue index (FI; W.s-1), according to the Eq.\u0026nbsp;2 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:FI\\left(W.{s}^{-1}\\right)=\\:\\frac{Pmax-Pmin}{TT}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAfter checking for outliers using boxplots and graphs of individual values vs. cycle phases, data were analyzed using generalized linear mixed models [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Assumption of normality of the residuals was verified using a histogram and QQplot, while the assumption of homoscedasticity was verified using a graph of residuals vs. predicted values. The gaussian and gamma distributions were tested, both with the identity link function, and if models with the two distributions met the assumptions, the model with the lowest Akaike information criterion was chosen.\u003c/p\u003e \u003cp\u003eIn all variables analyzed, cycle phase was used as a fixed effect (with the EF as the reference phase) and participants as a random effect. For the RFD and RF data at a given time (RF(t)) of the predetermined intervals (i.e., 30, 50, 90, 100, 150, 200, and 250 ms), the time value was transformed into a categorical variable and used as a fixed effect. For RFD data (cycle phase x interval), an interaction (slope) effect was also included in the model. Due to the discrepancy in strength levels among the participants, these variables (RFD and RF(t)) log-transformed (log\u003csub\u003e10\u003c/sub\u003e) before adjustment in the model, and hypothesis tests were performed on the log\u003csub\u003e10\u003c/sub\u003e means. For presentation of the results, the values are presented on the original scale\u0026mdash;i.e., mean values of these variables in log\u003csub\u003e10\u003c/sub\u003e are geometric mean values on the original scale [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To verify differences between measurements or slopes, hypothesis tests were performed with Bonferroni post hoc correction. The significance level adopted for rejecting the null hypothesis was p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The following R environment packages were used to perform the statistical analysis: 1) \u0026ldquo;lme4\u0026rdquo; for model fitting [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; 2) \u0026ldquo;ggResidpanel\u0026rdquo; [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], for residual analysis; and 3) \u0026ldquo;emmeans\u0026rdquo; [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], for generating estimated means and comparisons.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Participants\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes information of the 16 participants who completed the study.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePersonal information (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) of participants (n\u0026thinsp;=\u0026thinsp;16).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCycle length (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMenstruation length (days)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTetrapolar Bioelectrical Impedance Analysis (AF Sanny-BIA1011AF) results across cycle phases are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with no differences observed in body composition variables.\u003c/p\u003e \u003cp\u003eIPAQ data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Based on the questionnaire, the participants were classified as recreationally active (n\u0026thinsp;=\u0026thinsp;11) and trained/developmental (n\u0026thinsp;=\u0026thinsp;5) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBioimpedance information (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) measured at each phase of the cycle.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTBW (L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFFM (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBF (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMM (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBMI (kg/M\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e63.33\u0026thinsp;\u0026plusmn;\u0026thinsp;10.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e32.46\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e43.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e33.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e21.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e23.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e63.61\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e32.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e43.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e32.49\u0026thinsp;\u0026plusmn;\u0026thinsp;8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e22.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e23.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e63.21\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e32.46\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e43.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e32.92\u0026thinsp;\u0026plusmn;\u0026thinsp;10.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e21.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e23.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBW: Body Weight; TBW: Total Body Water; FFM: Fat-Free Mass; BF:Body Fat; SMM: Skeletal Muscle Mass; BMI: Body Mass Index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIPAQ information (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; n\u0026thinsp;=\u0026thinsp;16).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork/ day (hours)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep/ night (hours)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWalking as part of work/study (min/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWalking in free time (min/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate activity in free time (min/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVigorous activity in free time (min/day\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSitting time (hours/day)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.11\u0026thinsp;\u0026plusmn;\u0026thinsp;14.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.93\u0026thinsp;\u0026plusmn;\u0026thinsp;25.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.71\u0026thinsp;\u0026plusmn;\u0026thinsp;50.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRate of force development\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMean RFD value (i.e., mean between all 7 predetermined time intervals, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) was 12.61% higher in LF compared to ML (p\u0026thinsp;=\u0026thinsp;0.0159). Individually, none of the 7 predetermined intervals differed between phases. Resultant force (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) was 12.17% higher in the late follicular phase when compared to the luteal phase (p\u0026thinsp;=\u0026thinsp;0.0299).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Jump tests\u003c/h2\u003e \u003cp\u003eJumping performance was 5.7% higher for CMJ and 3.2% higher for HJ in the ML compared to the EF. Means (\u0026plusmn;\u0026thinsp;SD) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD) jump height by menstrual cycle phase.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCMJ (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEarly folicular\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLate folicular\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMid-luteal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.96\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.64\u0026thinsp;\u0026plusmn;\u0026thinsp;5.87\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.87*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSJ (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e29.42\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e29.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e30.10\u0026thinsp;\u0026plusmn;\u0026thinsp;7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHJ (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e171.73\u0026thinsp;\u0026plusmn;\u0026thinsp;33.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e173.65\u0026thinsp;\u0026plusmn;\u0026thinsp;33.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e177.34\u0026thinsp;\u0026plusmn;\u0026thinsp;33.54*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 compared to early follicular phase. CMJ: Countermovement Jump; SJ: Squat Jump; HJ: Horizontal Jump.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Running-based Anaerobic Sprint Test\u003c/h2\u003e \u003cp\u003eNo differences were observed between menstrual cycle phases for RAST variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eFollowing best practice methodologies to establish menstrual cycle phases, the objective of the study was to evaluate the anaerobic performance of healthy, active women at different phases of the ovarian cycle. It was observed that physical performance differed throughout the cycle according to the test. The ability to perform repeated sprints did not change during menstrual cycle. Furthermore, RFD and RF performed better in LF compared to ML, and jumping performance (CMJ and HJ) was higher in ML compared to EF. The findings partly support the idea that strength and power should have better performance in the LF and lower performance in the ML.\u003c/p\u003e \u003cp\u003eConsidering that mean RFD value was 12.61% and RF was 12.17% higher in LF compared to ML, it is possible to conclude that strength was improved in LF. Furthermore, the ability to produce force rapidly depends predominantly on increased muscle activation at the onset of contraction [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which is linked to RFD performance, assessed in the MVIC test.\u003c/p\u003e \u003cp\u003eNo difference was observed in the SJ test between the LF and the EF and ML phases. However, CMJ and HJ improved in ML compared to the EF. A study with male college weightlifters found no positive correlation between peak RFD and SJ test performance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. According to a review [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], there is no consensus on the influence of parameters such as peak RFD and time to peak RFD on sports performance. Furthermore, the ability to produce a higher RFD in initial movement intervals (0-100ms) is related to performance in various sports movements [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], however, no differences were found in this interval individually, only in average RFD. The RFD parameters studied here appear to have no influence on the participants' jumping performances, as RFD improves in LF compared to ML, while CMJ and HJ perform better in ML compared to EF.\u003c/p\u003e \u003cp\u003eUnlike that was found in this manuscript, a recent study [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] found no differences during the menstrual cycle for CMJ height in elite Australian football athletes. The difference in participant characteristics may have influenced the result, since the study was conducted with high-level athletes, while the participants in the present study were physically active. One hypothesis for this difference is that the jumping tasks require greater inter and intramuscular coordination compared to the MVIC test. Since the participants were not necessarily trained for this task, the increase in RFD did not translate into jumping performance, masking a possible muscle recruitment effect. Furthermore, they [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] studied only two phases of the cycle: follicular phase (not subdivided into early and late) and luteal phase, using the calendar method and urine samples to check pregnanediol glucuronide (PdG) levels. It is possible that differences between studies are justified by the way cycle phases were divided (into 2 or 3 phases) and identified (calendar, urine (PdG), urinary LH measurement and basal temperature).\u003c/p\u003e \u003cp\u003eNevertheless, another study [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] also found better CMJ performance in the luteal phase: the mean relative concentric power was 16.8% higher in the luteal phase compared to the EF. However, the best SJ performance was in the EF compared to the luteal phase [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this study, as in another recent ones, in sub-elite female soccer players [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and with loaded jumps and in elite soccer athletes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], no differences were observed between menstrual cycle phases for SJ. Studies involving horizontal jumps are a minority in recent literature. Based on the findings of a single study comparing the three menstrual cycle phases [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the cycle does not appear to influence performance in the horizontal jump test. Another study [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] found a difference for the vertical jump in sedentary women. They had a better performance in the LF compared to the late luteal phase (days 26\u0026ndash;28 of the cycle). Therefore, the phase of the cycle that demonstrated the peak of jumping performance (both horizontal and vertical jumps) was inconsistent among different studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne hypothesis for the increased jumping performance in the present study is that estradiol and progesterone have a role in augmenting and attenuating neuromuscular function, respectively [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A study that analyzed female rugby athletes (national level) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and found that an increase in estradiol concentration was associated with increased impulse at 200 ms during the CMJ, while elevated progesterone was associated with an increase in contraction time during SJ. Data from the present study suggest that coordinative tasks, involving strength and speed together, the luteal phase may be favored, when both estradiol and progesterone are elevated.\u003c/p\u003e \u003cp\u003eBased on performance in MVIC test, results obtained in this study suggest that there may be an improvement in the performance of strength tasks in LF when these tasks are decontextualized from time. Tasks decontextualized from time were defined as those in which time remained constant (e.g., 5 s in the MVIC test), unlike a jump test, in which the speed of movement may influence performance.\u003c/p\u003e \u003cp\u003ePrevious studies suggest there is no influence of cycle phases on sprint performance. A study [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] found no difference between EF and ML in a test of three 30-meter sprints and another one [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] also found no difference in the 2x20m sprint test between phases, even when analyzing EF, LF, and ML. In addition, a study [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] conducted with men and women in the 20-second maximum bicycle test also concluded that hormonal changes during menstrual cycle do not influence anaerobic all-out performance in women. These results are similar to those found in the RAST performed in this study, in which no differences were found between phases for maximum and minimum values, or values related to the test in general (average power, total time, and fatigue index). The participants' performance in RAST is consistent with the results obtained in previous studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], suggesting that hormonal fluctuations do not influence the performance of repeated sprints in women.\u003c/p\u003e \u003cp\u003eAmong the limitations of this study is the lack of information regarding symptoms that may be related to the menstrual cycle\u0026mdash;such as mood, pain (headache, lower back pain, etc.), fatigue, menstrual flow, and subjective perception of effort\u0026mdash;which can affect physical performance and therefore need to be considered when related to athletic performance [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A study conducted with professional rugby players found that 93% of female athletes experienced negative symptoms related to their menstrual cycle [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A study that evaluated performance in two phases of the cycle (EF and ML) through time to exhaustion noted that women with high menstrual flow had a shorter time to exhaustion [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which shows how menstrual symptoms can contribute to a change in performance that is not necessarily directly associated with hormone levels, but rather with perceived discomfort.\u003c/p\u003e \u003cp\u003eAlthough studying three phases of the cycle, rather than just two, the present study did not verify the serum concentration of hormones related to the menstrual cycle. Despite this, the combined methods used in this study to identify menstrual cycle phases represent some of the least invasive and most cost-effective approaches currently reported in the literature [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Another study limitation was the heterogeneity in the participants' training levels (recreationally active and trained/developmental), evidenced especially by the differences in the maximum isometric strength test. Although one of the inclusion criteria was to have a regular strength training routine of at least 8 weeks, some tests involving a higher level of coordination, such as sprints or jumps, may be affected by the level of physical fitness, regardless of hormonal fluctuations. Nevertheless, the heterogeneity may not be as relevant in the context of the phenomenon, as the participants largely shared familiarity with the analysis tools. Future studies should continue to evaluate menstrual cycle phases in a subdivided manner, level the participants' training level, and investigate the role of qualitative/subjective symptoms.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eAnaerobic performance in different menstrual cycle phases did not change uniformly, showing differences depending on the aspect of strength analyzed. Actions related to maximum strength, such as rate of force development and resultant force (MVIC test), tend to be better during the late follicular phase. On the other hand, actions related to rapid strength, such as CMJ and HJ, performed better during the mid-luteal phase, while the ability to perform repeated sprints did not change throughout the cycle.\u003c/p\u003e \u003cp\u003eTherefore, based on the results obtained in this study, it is suggested that strength efforts decontextualized from time seem to perform better in the follicular phase, while coordinative and fast tasks seem to be improved in the luteal phase in healthy and physically active women. Further studies are needed to identify the influence of the menstrual cycle on the physical performance of healthy women.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eAuthors declare they have no competing financial or non-financial interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e The study was conducted according to the guidelines of the Declaration of Helsinki and approved by Research Ethics Committee of the [BLINDED FOR REVIEW PROCESS].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003e Written informed consent was obtained from all individual participants prior to participation.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e This research was financed by [BLINDED FOR REVIEW PROCESS].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: E.F.P., J.A.P.; Data curation: M.S.S., N.A.O.M., G.M.P., J.A.P.; Formal analysis: G.M.P., J.A.P.; Funding acquisition: E.F.P., J.A.P.; Investigation: N.A.O.M., M.S.S., J.A.P.; Methodology: E.F.P., J.A.P.; Project administration: E.F.P., J.A.P.; Resources: E.F.P., J.A.P.; Software: G.M.P.; Supervision: E.F.P.; Validation: E.F.P., G.M.P., J.A.P.; Visualization: E.F.P., G.M.P., J.A.P.; Writing \u0026ndash; original draft: J.A.P.; Writing \u0026ndash; review \u0026amp; editing: E.F.P, M.S.S., G.M.P., J.A.P.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHulteen RM, Smith JJ, Morgan PJ, Barnett LM, Hallal PC, Colyvas K, et al. Global participation in sport and leisure-time physical activities: A systematic review and meta-analysis. Preventive Medicine. Elsevier Inc; 2017;95:14\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ypmed.2016.11.027\u003c/span\u003e\u003cspan address=\"10.1016/j.ypmed.2016.11.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanse de Jonge X, Minahan C. Physiology and Performance Research in Female Athletes: Bridging the Gap Between Opportunity and Evidence-Based Support. IJSPP. 2025;20:181\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1123/ijspp.2024-0540\u003c/span\u003e\u003cspan address=\"10.1123/ijspp.2024-0540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElliott-Sale KJ, Minahan CL, de Jonge XAKJ, Ackerman KE, Sipil\u0026auml; S, Constantini NW, et al. Methodological Considerations for Studies in Sport and Exercise Science with Women as Participants: A Working Guide for Standards of Practice for Research on Women. Sports Medicine. Springer International Publishing; 2021;51:843\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40279-021-01435-8\u003c/span\u003e\u003cspan address=\"10.1007/s40279-021-01435-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarmichael MA, Thomson RL, Moran LJ, Wycherley TP. The impact of menstrual cycle phase on athletes\u0026rsquo; performance: a narrative review. International Journal of Environmental Research and Public Health. 2021a;18:1\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph18041667\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18041667\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanse de Jonge X, Thompson B, Han A. Methodological Recommendations for Menstrual Cycle Research in Sports and Exercise. Medicine and science in sports and exercise. 2019;51:2610\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1249/MSS.0000000000002073\u003c/span\u003e\u003cspan address=\"10.1249/MSS.0000000000002073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNulty KL, Elliott-Sale KJ, Dolan E, Swinton PA, Ansdell P, Goodall S, et al. The Effects of Menstrual Cycle Phase on Exercise Performance in Eumenorrheic Women: A Systematic Review and Meta \u0026ndash; Analysis. Sports Medicine. Springer International Publishing; 2020; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40279-020-01319-3\u003c/span\u003e\u003cspan address=\"10.1007/s40279-020-01319-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSims ST, Heather AK. Myths and Methodologies: Reducing scientific design ambiguity in studies comparing sexes and/or menstrual cycle phases. Experimental Physiology. 2018;103:1309\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1113/EP086797\u003c/span\u003e\u003cspan address=\"10.1113/EP086797\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLebrun CM. Effects of the Menstrual Cycle and Oral Contraceptives on Sports Performance. Women in Sport. 1993;16:37\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/9780470757093.ch3\u003c/span\u003e\u003cspan address=\"10.1002/9780470757093.ch3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRedman LM, Weatherby RP. Measuring Performance during the Menstrual Cycle: A Model Using Oral Contraceptives. Medicine \u0026amp; Science in Sports \u0026amp; Exercise. 2004;130\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1249/01.MSS.0000106181.52102.99\u003c/span\u003e\u003cspan address=\"10.1249/01.MSS.0000106181.52102.99\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOğul A, Ercan S, Ergan M, İnce Parpucu T, \u0026Ccedil;etin C. The effect of menstrual cycle phase on multiple performance test parameters. Turkish Journal of Sports Medicine. 2021;56:159\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47447/tjsm.0552\u003c/span\u003e\u003cspan address=\"10.47447/tjsm.0552\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYapici-Oksuzoglu A, Egesoy H. The effect of menstrual cycle on anaerobic power and jumping performance. Pedagogy of Physical Culture and Sports. 2021;25:367\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15561/26649837.2021.0605\u003c/span\u003e\u003cspan address=\"10.15561/26649837.2021.0605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVespasiano BDS, Dias R, Correa DA. a utiliza\u0026ccedil;\u0026atilde;o do Question\u0026aacute;rio Internacional de Atividade F\u0026iacute;sica (IPAQ) como ferramenta diagn\u0026oacute;stica do n\u0026iacute;vel de aptid\u0026atilde;o f\u0026iacute;sica: uma revis\u0026atilde;o no Brasil. Sa\u0026uacute;de em Revista. 2012;12:49\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorg GAV. Psychophysical bases of perceived exertion. Medicine and Science on Sports and Exercise. 1982;14:377\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscamilla RF. Knee biomechanics of the dynamic squat exercise. Medicine and science in sports and exercise. 2001;33:127\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/00005768-200101000-00020\u003c/span\u003e\u003cspan address=\"10.1097/00005768-200101000-00020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaff GG, Ruben RP, Lider J, Twine C, Cormie P. A comparison of methods for determining the rate of force development during isometric midthigh clean pulls. Journal of Strength and Conditioning Research. 2015;29:386\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, et al. _shiny: Web Application Framework for R_ [Internet]. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=shiny\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=shiny\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. A Language and Environment for Statistical Computing [Internet]. Vienna, \u0026Aacute;ustria: R Foundation for Statistical Computing; 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreira A, Oliveira PR De, Okano AH, Souza M De. A din\u0026acirc;mica de altera\u0026ccedil;\u0026atilde;o das medidas de for\u0026ccedil;a e o efeito posterior duradouro de treinamento em basquetebolistas submetidos ao sistema de treinamento em bloco. Revista Brasileira de Medicina do Esporte. 2004;10:243\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosco C, Belli A, Astrua M, Tihanyi J, Pozzo R, Kellis S, et al. A dynamometer for evaluation of dynamic muscle work. Eur J Appl Physiol Occup Physiol. 1995;70:379\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF00618487\u003c/span\u003e\u003cspan address=\"10.1007/BF00618487\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeylan C, Malatesta D. Effects of In-season Plyometric Training Within Soccer Practice on Explosive Actions of Young Players. Journal of Strength and Conditioning Research. 2009;23:2605\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmirniotou A, Katsikas C, Paradisis G, Argeitaki P, Zacharogiannis E, Tziortzis S. Strength-power parameters as predictors of sprinting performance.pdf. The Journal of Sports Medicine and Physical Fitness. 2008;48:447.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoseguini AZ, Silva ASR da, Gobatto CA. Determina\u0026ccedil;\u0026otilde;es e Rela\u0026ccedil;\u0026otilde;es dos Par\u0026acirc;metros Anaer\u0026oacute;bios do RAST, do Limiar Anaer\u0026oacute;bio e da Resposta Lactacidemica Obtida no In\u0026iacute;cio, no Intervalo e ao Final de uma Partida Oficial de Handebol. Revista Brasileira de Medicina do Esporte. 2008;14:46\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates D, Maechler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67:1\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v067.i01\u003c/span\u003e\u003cspan address=\"10.18637/jss.v067.i01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlivier J, Johnson WD, Marshall GD. The logarithmic transformation and the geometric mean in reporting experimental IgE results: what are they and when and why to use them? Annals of Allergy, Asthma \u0026amp; Immunology. 2008;100:333\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1081-1206(10)60595-9\u003c/span\u003e\u003cspan address=\"10.1016/S1081-1206(10)60595-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoode K, Rey K. _ggResidpanel: Panels and Interactive Versions of Diagnostic Plots using \u0026rsquo;ggplot2\u0026rsquo;_ [Internet]. 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=ggResidpanel\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=ggResidpanel\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenth R. _emmeans: Estimated Marginal Means, aka Least-Squares Means_ [Internet]. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e%3Chttps://CRAN.R-project.org/package=emmeans%3E\u003c/span\u003e\u003cspan address=\"http://%3Chttps://CRAN.R-project.org/package=emmeans%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKay AKA, Stellingwerff T, Smith ES, Martin DT, Mujika I, Goosey-Tolfrey V, et al. Defining Training and Performance Caliber: A Participant Classification Framework. nternational Journal of Sports Physiology and Performance. 2022;17:317\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1123/ijspp.2021-0451\u003c/span\u003e\u003cspan address=\"10.1123/ijspp.2021-0451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaffiuletti NA, Aagaard P, Blazevich AJ, Folland J, Tillin N, Duchateau J. Rate of force development: physiological and methodological considerations. Eur J Appl Physiol. 2016;116:1091\u0026ndash;116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00421-016-3346-6\u003c/span\u003e\u003cspan address=\"10.1007/s00421-016-3346-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawamori N, Rossi SJ, Justice BD, Haff EE, Pistilli EE, O\u0026rsquo;Bryant HS, et al. Peak force and rate of force development during isometric and dynamic mid-thigh clean pulls performed at various intensities. Journal of Strength and Conditioning Research. 2006;20:483\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1519/18025.1\u003c/span\u003e\u003cspan address=\"10.1519/18025.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHern\u0026aacute;ndez-Dav\u0026oacute; J, Sabido R. Rate of force development: reliability, improvements and influence on performance. A review. European Journal of Human Movement. 2014;33:46\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarmichael MA, Thomson RL, Moran LJ, Dunstan JR, Nelson MJ, Mathai ML, et al. A pilot study on the impact of menstrual cycle phase on elite australian football athletes. International Journal of Environmental Research and Public Health. 2021b;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph18189591\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18189591\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith ES, Weakley J, McKay AKA, McCormick R, Tee N, Kuikman MA, et al. Minimal influence of the menstrual cycle or hormonal contraceptives on performance in female rugby league athletes. European Journal of Sport Science. 2024;24:1067\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ejsc.12151\u003c/span\u003e\u003cspan address=\"10.1002/ejsc.12151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIgonin PH, Cognasse F, Gonzalo P, Philippot P, Rogowski I, Sabot T, et al. Monitoring of sprint and change of direction velocity, vertical jump height, and repeated sprint ability in sub-elite female football players throughout their menstrual cycle. Science and Medicine in Football. Routledge; 2024;8:418\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/24733938.2024.2328674\u003c/span\u003e\u003cspan address=\"10.1080/24733938.2024.2328674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouvier J, Igonin P-H, Boithias M, Four\u0026eacute; A, Belli A, Boisseau N, et al. The squat jump and sprint force\u0026ndash;velocity profiles of elite female football players are not influenced by the menstrual cycle phases and oral contraceptive use. Eur J Appl Physiol [Internet]. 2025 [cited 2025 Feb 24]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00421-025-05723-3\u003c/span\u003e\u003cspan address=\"10.1007/s00421-025-05723-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies BN, Elford JC, Jamieson KF. Variations in performance in simple muscle tests at different phases of the menstrual cycle. J Sports Med Phys Fitness. 1991;31:532\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTasmektepligil MY, Agaoglu SA, T\u0026uuml;rkmen L, T\u0026uuml;rkmen M. The motor performance and some physical characteristics of the sportswomen and sedentary lifestyle women during menstrual cycle. Archives of Budo. 2010;6:195\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePallavi LC, D Souza UJ, Shivaprakash G. Assessment of Musculoskeletal Strength and Levels of Fatigue during Different Phases of Menstrual Cycle in Young Adults. Journal of Clinical and Diagnostic Research. 2017;11:11\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7860/JCDR/2017/24316.9408\u003c/span\u003e\u003cspan address=\"10.7860/JCDR/2017/24316.9408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJulian R, Hecksteden A, Fullagar HHK, Meyer T. The effects of menstrual cycle phase on physical performance in female soccer players. PLoS One. 2017;12:e0173951. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0173951\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0173951\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiecek M, Szymura J, Maciejczyk M, Cempla J, Szygula Z. Effect of sex and menstrual cycle in women on starting speed, anaerobic endurance and muscle power. Physiology International. 2016;103:127\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1556/036.103.2016.1.13\u003c/span\u003e\u003cspan address=\"10.1556/036.103.2016.1.13\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinahan C. Menstrual cycles and macrocycles: Science, not socials, is doing the heavy lifting. The Journal of Physiology. 2025;603:1023\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1113/JP288096\u003c/span\u003e\u003cspan address=\"10.1113/JP288096\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFindlay RJ, Macrae EHR, Whyte IY, Easton C, Forrest N\u0026eacute;e Whyte LJ. How the menstrual cycle and menstruation affect sporting performance: experiences and perceptions of elite female rugby players. Br J Sports Med. 2020;54:1108\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bjsports-2019-101486\u003c/span\u003e\u003cspan address=\"10.1136/bjsports-2019-101486\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Carvalho G, Papoti M, Rodrigues MCD, Foresti YF, De Oliveira Guirro EC, De Jesus Guirro RR. Interaction predictors of self-perception menstrual symptoms and influence of the menstrual cycle on physical performance of physically active women. Eur J Appl Physiol [Internet]. Springer Science and Business Media LLC; 2023 [cited 2025 July 30]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00421-022-05086-z\u003c/span\u003e\u003cspan address=\"10.1007/s00421-022-05086-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sport-sciences-for-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssfh","sideBox":"Learn more about [Sport Sciences for Health](http://link.springer.com/journal/11332)","snPcode":"11332","submissionUrl":"https://submission.nature.com/new-submission/11332/3","title":"Sport Sciences for Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Menstrual cycle, Athletic training, Strength and conditioning, Exercise physiology","lastPublishedDoi":"10.21203/rs.3.rs-9215036/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9215036/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWomen remain underrepresented in research due to the menstrual cycle\u0026rsquo;s complexity. Most studies use outdated methods, limiting insights into menstrual cycle effects on performance. Therefore, there is a need to expand the body of knowledge using modern and robust methodologies. The aim of this study was to investigate the anaerobic performance of women (18\u0026ndash;35y) across different ovarian cycle phases and their influence on performance. The protocol was conducted in three cycle phases: early follicular (EF), late follicular (LF), and mid-luteal (ML) phase.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSixteen volunteers underwent anthropometric measurements, maximal isometric strength test with rate of force development (RFD), horizontal jump (HJ), countermovement jump (CMJ), squat jump (SJ) and the running-based anaerobic sprint test (RAST).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRFD and resultant force showed better performance in LF compared to ML (p\u0026thinsp;=\u0026thinsp;0.015, ΔRFD:12.61%; p\u0026thinsp;=\u0026thinsp;0.030, ΔResultant Force:12,17%); CMJ and HJ performed better in ML compared to EF (p\u0026thinsp;=\u0026thinsp;0.002, ΔCMJ:5.7%; p\u0026thinsp;=\u0026thinsp;0.001, ΔHJ:3.2%). RAST and anthropometric measurements showed no differences between phases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIt is suggested that strength efforts tend to improve in the late follicular phase, while coordinative and speed-related tasks seem to improve during ML. Recognizing that neuromuscular demands respond differently to hormonal fluctuations may help reduce performance variability and optimize outcomes by leveraging specific menstrual phases. Future research should also investigate the role of subjective and qualitative symptoms and assess whether/how they impact performance, even in similar hormonal profiles.\u003c/p\u003e","manuscriptTitle":"Influence of menstrual cycle phases on anaerobic physical performance of healthy women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 12:48:06","doi":"10.21203/rs.3.rs-9215036/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-09T21:08:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T18:27:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T17:38:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338486670192095607263218244162112647691","date":"2026-04-05T15:03:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T11:04:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130643768919970385989861146376212049619","date":"2026-04-01T15:42:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130029774854901734153404920426570096208","date":"2026-03-31T13:28:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268605596659177306088432750760057712961","date":"2026-03-31T12:08:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6997036097122681699389675627679635488","date":"2026-03-30T13:38:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-30T12:24:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-26T06:11:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-26T06:10:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sport Sciences for Health","date":"2026-03-24T17:38:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sport-sciences-for-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssfh","sideBox":"Learn more about [Sport Sciences for Health](http://link.springer.com/journal/11332)","snPcode":"11332","submissionUrl":"https://submission.nature.com/new-submission/11332/3","title":"Sport Sciences for Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"138d59c4-c97c-45ef-98b9-985de366fe89","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T19:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 12:48:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9215036","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9215036","identity":"rs-9215036","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}