The effect of time of day and menstrual cycle on physical performance and psychological responses in elite female Tunisian volleyball players

Abstract

Background

The influence of circadian rhythms and menstrual cycle phases on athletic performance and psychological responses is critical for optimizing training and competition strategies for female athletes. This study aimed to investigate the effects of time of day and menstrual cycle phases on the physical performance and psychological responses of elite female Tunisian volleyball players.

Methods

Thirteen elite female volleyball players were assessed during three different phases of their menstrual cycle (menstrual, follicular, and luteal) and at two different times of day (morning and evening). Physical performance was evaluated using the Modified Agility Test (MAT), Reactive Agility Test (RAT), and Repeated Sprint Ability (RSA) Test. Psychological responses were measured using the Profile of Mood States (POMS), Hooper’s Questionnaire, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, Vis-Morgen Questionnaire, and Spiegel Questionnaire.

Results

Significant effects of menstrual cycle, time of day, and competition on physical performance, cognitive function, mood, and sleep parameters were found. Physical performance, including the Countermovement Jump (CMJ), the Modified Agility T-test (MAT) and the Reactive Agility test (RAT), was higher in the afternoon than in the morning across all menstrual phases (CMJ: p < 0.001, η² = 0.836; MAT: p < 0.001, η² = 0.777; RAT: p < 0.001, η² = 0.859). After the competition, performance decreased significantly, especially in the follicular and luteal phases. As measured by the Stroop test, cognitive function showed significant diurnal effects (p < 0.001, η² = 0.910), with pre-competition performance being better in the afternoon. Mood disturbances (POMS) increased after the competition, especially in the morning and during the luteal phase (p < 0.001, η² = 0.961). Sleep parameters were significantly influenced by time and menstrual cycle, with higher fatigue (Hooper score: p < 0.001, η² = 0.754) and poorer sleep quality (PSQI: p < 0.001, η² = 0.627) in the morning, especially after the competition.

Conclusion

Our results suggest that aligning high-intensity training and competitions with afternoon circadian peaks may enhance physical and cognitive performance in elite female athletes. Recovery strategies and workload adjustments should account for menstrual phases, particularly reducing morning demands during the luteal phase to mitigate fatigue and mood disruptions. Integrating circadian timing with menstrual cycle monitoring offers a practical, evidence-based approach to optimize athlete readiness and resilience.

Introduction

The physical and physiological demands of volleyball have evolved with the implementation of the rally point system, transforming the sport into a ‘power volleyball’ game [1]. This shift has subsequently impacted the training and competition intensities of volleyball players, as well as their periodization approaches [2]. In addition to targeted strength, power, and sport-specific physical conditioning, coupled with psychological and cognitive monitoring, volleyball players have developed faster, more powerful, and enhanced physical attributes through the skills acquired via strategic annual training planning [3, 4].

Currently, elite female volleyball players are highly engaged in intensive training programs that expend significant amounts of energy, requiring appropriate energy compensation to enhance their performance [5, 6]. These intense training regimens can potentially disrupt normal menstrual cycle patterns [7]. However, these athletic practices are accompanied by an increased incidence of reproductive cycle disruptions, which have been the subject of extensive research [8]. Conversely, menstruation in female players can lead to various physiological difficulties and performance decrements during training or competition [9]. Players often attempt to psychologically abstain from training or competition during menstrual periods [10, 11]. Several scholars have sought to elucidate the effect of menstruation on performance [12,13,14,15], but no conclusive evidence regarding a negative or positive impact has been found. Several studies have concluded that players achieve their best performances at the onset of menstruation [16,17,18]. Others have also suggested that menstruation and athletic activities mutually influence one another, with exceptional sports achievements observed across various phases of the female menstrual cycle [12, 17]. On the other hand, a line of research focused on women’s performance during menstrual periods has concluded that there are no changes in power output values in terms of sprint test results during menstruation [19,20,21]. In another study, it was found that muscular endurance is at its highest level in the mid-follicular phase, while it is at its lowest in the mid-luteal phase [20]. Furthermore, it was observed that performance is better at the beginning of the luteal phase [15]. Finally, other studies have concluded that the highest performance values were measured after the ovulation and menstruation phases, while the poorest performance values were obtained during the premenstrual period [22,23,24].

In addition to menstrual cycle-related variations, circadian rhythms also play a crucial role in athletic performance. The time of day is a key determinant of physical and physiological performance [25,26,27]. Regarding dynamic performance, Dergaa et al. [28] demonstrated that maximal power output was higher in the afternoon (5 PM) compared to the morning (7 AM) during the running anaerobic sprint test (RAST). Studies examining the effects of time of day on sports performance have demonstrated, through various field-based [29, 30] and/or laboratory [31] investigations, that physical performance exhibits circadian or daily rhythms, with peaks generally observed in the late afternoon and minimums in the morning. This circadian rhythmicity is typically associated with variations in the physiological, psychological, and molecular mechanisms of the body involved in task execution, which leads to fluctuations in physical performance throughout the day [32]. According to Ayala et al. [33], most determinants of sports performance fluctuate based on the time of day, reaching their peak in the evening when core body temperature is maximized. Circadian variations in sports performance must be considered by athletes and coaches to optimize the performance and success of an individual athlete or team [29, 34].

The circadian rhythmicity of performance has been explained by various mechanisms. From a psychological and cognitive perspective, decreases in perceived exertion as well as improvements in reaction time, executive function, attention, and vigor [35] have been the primary indicators associated with diurnal variation in performance.

Despite extensive research on the menstrual cycle and circadian rhythms in sport, few studies have investigated their combined effects on volleyball-specific skills such as spiking, jumping, and endurance. Because volleyball relies on explosive actions and quick decision-making, understanding the interaction of these physiological rhythms is critical to optimizing performance. Some studies suggest that neuromuscular performance peaks in the afternoon [36], but their importance for volleyball-specific motor skills has not yet been sufficiently explored. The influence of hormonal fluctuations on reaction time and movement coordination, which are crucial for actions such as blocking and serving, also needs further investigation [37].

The high-intensity and demanding nature of volleyball at the elite level requires volleyball players to experience rigorous training programs that can significantly tax their energy systems and physiological capacities. These intense training regimens could disrupt the normal menstrual cycle patterns in female volleyball players. At the same time, existing research has presented conflicting findings on the relationship between the menstrual cycle phase and athletic performance. Some studies suggest better performance at the onset of menstruation or during the luteal phase, while others found no significant changes. Additionally, the existing literature has well-established the influence of the time of day on various physiological, psychological, and cognitive factors that support sports performance. Given the unique physical and physiological demands of volleyball, it is important to examine how the interplay between the menstrual cycle and time of day may impact the performance and psychological responses of elite female volleyball players.

Understanding these relationships can assist coaches, athletes, and support staff in optimizing training, competition scheduling, and recovery strategies to enhance overall athletic performance. This study aims to (i) investigate the effect of the menstrual cycle on physical performance and psychological responses and (ii) analyze the daily variations of these performances across the different phases of the menstrual cycle in female volleyball players.

Materials and methods

Participants

A total of 13 players (Age: 24.23 ± 4.06 years; Height: 176.25 ± 7.41 cm; Weight: 66.15 ± 12.02 kg; Senior team experience: 6.77 ± 3.49 years) were recruited for this study. Healthy, active, and voluntary participants with regular menstrual cycles and not using any regulatory medical supplements (oral contraceptives) were included. The participants were members of the volleyball team from the Union Sportive Carthaginoise in the Tunisian Volleyball Premier League.

Inclusion criteria

The thirteen female participants were between 20 and 30 years of age and met the following inclusion criteria:

They were experienced team sport (Volleyball) athletes, accustomed to training (in the afternoon) five times per week (1.5 h per session) and having more than 7 years of volleyball experience; they were regular participants in national competitions for at least 4 years before the study; they maintained a regular menstrual cycle between 25 and 30 days; and they were invited to abstain from any intense physical effort 24 h before the experimental sessions. Importantly, the participants presented no medical restrictions or acute/chronic injuries. The study was conducted according to the Declaration of Helsinki for human experimentation, and the protocol was approved by the Research Ethics Committee of the ISSEP Sfax before the start of the study. The study also complied with the ethical and procedural requirements for the conduct of sports medicine and exercise science research [38]. All players and their coaches provided written informed consent after a detailed explanation of the study’s objectives, benefits, and potential risks.

Sample size calculation

A priori sample size estimation was performed using the formula for a paired t-test, as applied in prior circadian rhythm studies investigating diurnal performance variations [28]:

$${\rm{n}}\; = \;{({\rm{Z}}\alpha /2 + \;{\rm{Z}}\beta )^{2 \cdots }}{\sigma ^{2 \cdot / \cdot }}\;{(\mu 1 - \mu 2)^2}$$

where:

• Zα/2 = 1.96 (for α = 0.05),

• Zβ = 0.84 (for 80% power),

• σ = 1.2 (SD of performance differences from [28]),

• µ1 − µ2 = 2.5 (mean CMJ difference between morning/afternoon from [28]).

This yielded a minimum requirement of 12 participants. To account for repeated-measures interactions (menstrual cycle × time of day) and potential attrition, we adopted a conservative approach aligned with recommendations for within-subjects designs in chronobiology [29]. The final sample size of 13 elite athletes ensured robust statistical power (G*Power 3.1: repeated-measures ANOVA, f = 0.25f = 0.25, α = 0.05, power = 0.80).

Data collection

Participants’ body weights were measured using an electronic scale (Tanita TBF 401 A, Japan). Participant heights were measured using a wall-mounted stadiometer (Holtain, England). The physical characteristics of the volleyball players, including height, body weight, age, and menstrual cycles, were recorded.

Additionally, each player was asked to complete the following questionnaires and tests: Pittsburgh Sleep Quality Index [39] to assess sleep quality, Epworth Sleepiness Scale [40] to quantify subjective sleepiness, Spiegel questionnaire to gather information on sleep onset, dreams, and morning state, Profile of Mood States (POMS) questionnaire, Stroop test, Countermovement Jump (CMJ), Modified Agility T-Test (MAT), and Rection Agility Test (RAT) to evaluate sports performance.

Procedure

Experimental design

One week before the experiment, all players were familiarized with the test equipment and procedures to ensure reproducibility and minimize learning effects [41]. The study consisted of six sessions, with one session in the morning (8:00 am) and another in the evening (6:00 pm) during each phase of the menstrual cycle (menstrual, follicular, and luteal), with an interval of 32 h between consecutive sessions. To exclude external influences, participants abstained from alcohol, caffeine, and stimulants for 24 h before each study and maintained a consistent diet by repeating a 24-hour dietary protocol from the first session. Each experimental session followed a standardized protocol, starting with the completion of the Profile of Mood States (POMS), HOOPER, Pittsburgh Sleep Quality Index (PSQI), ESS, and SPIGEL questionnaires, followed by a STROOP test and a 10-minute general warm-up. Participants then performed a series of physical performance tests, including the Countermovement Jump (CMJ), the Modified T-Agility Test (MAT), and the Y-Reaction Agility Test (RAT), with five-minute recovery periods between tests. After a 15-minute break, the participants took part in a one-hour volleyball competition. After the competition, the same performance tests were repeated, and participants completed the POMS questionnaire again. All sessions were conducted in a temperature-controlled gym to ensure consistency (Fig. 1).

Fig. 1

Flow chart

Anthropometric parameters

Anthropometric parameters were assessed, including height, body mass, and body mass index (BMI). Height was measured to the nearest 0.001 m using a portable SECA Leicester stadiometer (United Kingdom). Body mass was determined using a Detecto scale (Webb City, MO, USA) with a maximum capacity of 140 kg. BMI was then calculated as body mass (kg) divided by height squared (m²).

Countermovement jump (CMJ) test

In the CMJ test [42], a jump with counter movement was performed with the hands placed on the hips to eliminate the influence of the arm swing. The participants completed three maximum jumps, each of which was interrupted by an approximately 60-second pause. The highest jump was recorded for statistical analysis. Jump height and performance data were measured using an infrared-based optical measurement system (Optojump Next, version 1.3.20.0, Microgate, Bolzano, Italy), which was connected to a microcomputer for data processing.

Modified agility T-test (MAT)

The MAT was used to assess agility and directional velocity according to the protocol established by Pauole et al. [43] and later modified by Haj Sassi et al. [44]. Participants began with both feet behind the designated starting point (A), sprinted 5 m to point B, then shuffled 2.5 m to the left to touch cone C, followed by a 5-meter shuffle to the right to touch cone D. They then shuffled 2.5 m to the right to touch cone B, followed by a 5-meter shuffle to the right to touch cone C. They then shuffled 2.5 m back to point B and finished the test by running backward across the finish line at point A. Each participant performed two trials, with a minimum break of 5 min between trials. Performance time was measured using dual photocell timing gates (Brower Timing System, Salt Lake City, UT, USA; accuracy of 0.01 s), and the fastest recorded time was used for analysis.

Reactive agility test (RAT)

The RAT was adapted from Farrow et al. [45] but modified concerning the movement patterns and visual stimuli. Participants started 20 cm behind the first timed gates and sprinted forward towards a Smart Indicator Matrix (SIM), a 7 × 5 LED display that provided random visual cues. Upon passing a series of secondary timing gates located 3 m from the starting line, participants reacted to the displayed stimulus and changed direction accordingly. The required reaction time, known as the “abort time,” was individually calibrated based on each participant’s fastest 4-meter sprint. This ensured that the test was performed at close to maximum running speed, which corresponded to gameplay. The tests. The test ended when the participants crossed the left or right exit gates after responding to the SIM stimulus.

STROOP test

The STROOP test, originally proposed by Stroop [46] and later described by Jarraya et al. [47], assessed participants’ cognitive inhibitory control and processing speed. The test consisted of three conditions:

• Color naming (color task): Participants named the color of the ink used to print a series of color patches.

• Word reading (word task): Participants read color names printed in black ink.

• Color-word interference (color-word task): Participants named the ink color of words that spelled a different color name (e.g., the word “BLUE” printed in red ink).

Each trial was timed, and participants were instructed to correct errors immediately. Error corrections increased the total execution time recorded for analysis.

Profile of mood states (POMS)

Immediately before each testing session, the POMS [48]; French adaptation by Cayrou et al. [49], was completed to assess participants’ mood states. This self-report inventory consisted of 65 adjectives, each rated on a 5-point Likert scale (0 = “not at all” to 4 = “extremely”). The questionnaire covered seven mood dimensions: Tension, depression, anger, confusion, vigor, fatigue, and friendliness. The higher the score, the more intense the negative mood.

Pittsburgh sleep quality index (PSQI)

PSQI was used to assess subjective sleep quality and sleep-related disorders. The questionnaire comprised 19 self-report items divided into seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. Each component was scored on a scale from 0 (no difficulty) to 3 (severe difficulty), with total scores ranging from 0 to 21, with higher scores indicating poorer sleep quality [39].

Epworth sleepiness scale (ESS)

Daytime sleepiness was assessed with the ESS [40], an instrument that has been validated worldwide since 1991. The ESS consisted of eight items to assess the likelihood of falling asleep in various everyday situations. Each item was scored on a scale from 0 to 3, with a higher cumulative score indicating greater subjective sleepiness. The test was designed to be completed in less than one minute.

Spiegel questionnaire

The questionnaire assessed sleep-related variables, including sleep onset delay, dream activity, and morning alertness [50].

The test dates were set according to the phases of the participants’ menstrual cycle:

• Menstrual phase (PM): days 2, 3, and 4.

• Mid-follicular phase (PF): days 7, 8 and 9.

• Mid-luteal phase (PL): days 19, 20, and 21.

Rating of perceived exertion (RPE)

The Borg RPE scale is designed to assess perceived exertion using a simpler 0–10 rating system. It allows individuals to subjectively rate their exertion during physical activity, with 0 indicating no exertion and 10 indicating maximal exertion (Borg, 1998). This scale is often used in research and clinical settings to monitor exercise intensity, fatigue, and level of exertion [51].

The percentage of average heart rate (%AVG)

% AVG was used to assess the intensity of the competition. Intensity was measured using a Polar Team Sport system (Polar-Electro OY, Kempele, Finland) [19]. The %AVG was calculated according to the following formula:

$$0/0AVG = {{{\rm{ Average }}HR} \over {MHR}} \times 100$$

where Average HR is the average heart rate recorded during the effort and MHR is the maximum heart rate, which is calculated by the theoretical Eq. 220-age. This measure expresses the relative intensity of the effort as a percentage of the maximum heart capacity.

Statistical analysis

All statistical analyses were performed using SPSS version 29 (IBM Corporation, NY, USA). The Kolmogorov-Smirnov test was employed to verify the assumption of normality, while Levene’s test was utilized to check for homogeneity of variances.

To analyze the effects of menstrual cycle phase (menstruation, follicular and luteal phase), time of day (morning and afternoon) and competition phase (pre- and post-competition) on the dependent variables: CMJ, MAT, RAT, STROOP test and POMS, a three-way repeated measures ANOVA** was performed. In addition, a two-way repeated measures ANOVA was performed to assess the effects of menstrual phase and time of day on HOOPER index, PSQI, ESS, and SEIGEL scores. Post-hoc analyses with Bonferroni-adjusted pairwise comparisons were performed when significant effects were observed. Effect sizes were reported using partial eta squared (ηp²), with thresholds defined as follows: 0.01 for a small effect, 0.06 for a medium effect, and values greater than 0.14 for a large effect [52].

Results

Effect of factors: cycle, time, and competition on physical performance

For physical parameters, including CMJ, MAT, and RAT, both cycle and time show significant effects with large effect sizes. CMJ is significantly affected by cycle (p < 0.001, η² = 0.518), time (p = 0.001, η² = 0.632), and competition (p < 0.001, η² = 0.836). Similarly, MAT is significantly influenced by cycle (p = 0.003, η² = 0.379), time (p < 0.001, η² = 0.781) and competition (p < 0.001, η² = 0.777). RAT follows a similar pattern, with cycle (p < 0.001, η² = 0.791) and time (p < 0.001, η² = 0.859) having strong effects. For the interactions, time * competition is significant for CMJ (p = 0.000, η² = 0.810), MAT (p = 0.002, η² = 0.549) and RAT (p = 0.041, η² = 0.303), suggesting that performance outcomes depend on both the time and competition conditions (Table 1).

Table 1 Statistical analysis of the effects of menstrual cycle phase, time of day, and competition on physical performance (CMJ, MAT, RAT), mood States (POMS), and cognitive function (STROOP test)

Post-hoc comparisons

CMJ Performance: A significant decline in CMJ performance was observed after the competition across all menstrual cycle phases and times of the day. In the morning, post-competition CMJ performance significantly decreased during the follicular (mean difference = 0.531 [0.237–0.824]; p = 0.002) and luteal (mean difference = 1.277 [0.863–1.691]; p = 0.002) phases. In the afternoon, a similar decline was observed in the menstruation (mean difference = 1.031 [0.495–1.566]; p < 0.001) and follicular (mean difference = 1.215 [0.891–1.540]; p < 0.001) phases. Comparing the morning and afternoon sessions before the competition, CMJ performance was significantly higher in the afternoon across all phases (mean difference ranging from 0.523 [0.224–0.822] to 1.262 [0.748–1.775]; p < 0.001). After the competition, CMJ remained higher in the afternoon than in the morning in the follicular (mean difference = 0.246 [0.010–0.482]; p = 0.042) and luteal (mean difference = 0.544 [0.084–0.482]; p = 1.024) phases (Fig. 2).

Fig. 2

Effects of time of day (morning vs. afternoon), competition (Pre vs. Post), and menstrual cycle phases (menstrual, follicular, and luteal) on physical performance: Countermovement Jump (CMJ), Movement Agility Test (MAT) time, and Reaction Agility Test (RAT). Significant differences (p-values) are indicated. Data are presented as mean ± SD

Competition, time of day, and menstrual cycle phase significantly affected agility. In the morning, post-competition agility performance declined in the follicular (mean difference = 0.053 [0.015–0.091]; p = 0.010) and luteal (mean difference = 0.026 [0.001–0.052]; p = 0.050) phases. In the afternoon, agility worsened post-competition across all phases (mean difference ranging from 0.101 [0.052–0.149]; p < 0.001 to 0.165 [0.100-0.229]; p < 0.001). Pre-competition agility was significantly better in the afternoon across all phases (mean difference ranging from 0.242 [0.123–0.360]; p < 0.001 to 0.428 [0.338–0.519]; p < 0.001). After the competition, agility remained better in the afternoon than in the morning (mean difference ranging from 0.162 [0.044–0.281]; p = 0.011 to 0.317 [0.236–0.398]; p < 0.001), though the difference was slightly reduced (Fig. 2).

No significant differences were observed before and after the competition across menstrual phases in RAT. Pre-competition RAT was significantly faster in the afternoon across all phases (mean difference ranging from 0.022 [0.011–0.032]; p < 0.001 to 0.030 [0.014–0.046]; p = 0.006). After the competition, RAT remained better in the afternoon than in the morning (mean difference ranging from 0.022 to 0.039 [0.013–0.031] to [0.019–0.058]; p < 0.001), though the difference was slightly reduced, indicating a greater fatigue-related decline in morning performance (Fig. 2).

Effect of factors: cycle, time, and competition on Stroop test

Time significantly impacted Planche 2 (p < 0.001, η² = 0.869) and Planche 3 (p < 0.001, η² = 0.910), with a significant cycle × time interaction for Planche 3 (p < 0.001, η² = 0.614), suggesting the joint influence of these factors. Additionally, a significant cycle × time × competition interaction (p = 0.018, η² = 0.385) highlighted a moderate combined effect (Table 1).

Post-hoc comparisons

In the follicular phase, reaction times increased post-competition in Planche 1 (mean difference = 0.034 [0.006–0.080]; p = 0.025) and in Planche 3 during the luteal phase (mean difference = 0.307 [0.058–0.556]; p = 0.020), indicating fatigue-related cognitive decline. No significant differences were observed in Planche 2 (p > 0.05), suggesting cognitive stability despite fatigue. Pre-competition, reaction times were faster in the afternoon across menstrual phases for Planche 1 (mean difference ranging from 0.355 [0.106–0.604]; p = 0.009 to 0.370 [0.129–0.611]; p = 0.006), Planche 2 (mean difference ranging from 0.862 [0.566–1.159]; p < 0.001 to 1.021 [0.483–1.558]; p < 0.001), and Planche 3 (mean difference ranging from 0.606 [0.284–0.943]; p < 0.001 to 0.730 [0.427–1.033]; p < 0.001). Post-competition, the afternoon advantage persisted, although differences were slightly reduced (Fig. 3).

Fig. 3

Effects of time of day (morning vs. afternoon), competition (Pre vs. Post), and menstrual cycle phases (menstrual, follicular, and luteal) on STROOP test: planche 1, planche 2 and planche 3. Significant differences (p-values) are indicated. Data are presented as mean ± SD

Effects of factors: cycle, time, and competition on POMS

The results indicate that the cycle has a significant effect on POMS (p = 0.000,η² = 0.961), along with competition (p = 0.000,η² = 0.911), suggesting that both factors strongly influence the outcomes. Additionally, a significant interaction between cycle,time,and competition (p = 0.012,η² = 0.308) highlights a moderate combined effect (Table 1).

Post-hoc comparisons

Post-competition mood disturbance significantly increased in the morning across all phases: menstruation (mean difference = 2.231 [0.501–3.960]; p = 0.016), follicular (mean difference = 2.000 [1.013–2.987]; p < 0.001), and luteal (mean difference = 4.154 [0.948–3.975]; p < 0.001). Similar increases were observed in the afternoon: menstruation (mean difference = 2.231 [0.501–3.960]; p = 0.024), follicular (p < 0.001), and luteal (p = 0.004). Pre-competition POMS scores did not differ significantly between morning and afternoon. Post-competition, POMS scores remained higher in the morning than in the afternoon during the luteal phase (mean difference = 1.615 [0.497–3.733]; p = 0.008) (Fig. 4).

Fig. 4

Effects of time of day (morning vs. afternoon), competition (Pre vs. Post), and menstrual cycle phases (menstrual, follicular, and luteal) on Total Mood Disturbance of POMS. Significant differences (p-values) are indicated. Data are presented as mean ± SD

Effect of factors: cycle and time on cognitive fatigue and sleep parameters

Cycles significantly influenced Hooper (p < 0.001, η² = 0.754), PSQI (p < 0.001, η² = 0.627), ESS (p < 0.001, η² = 0.842), and SPIGET (p < 0.001, η² = 0.864). Time significantly affected Hooper (p < 0.001, η² = 0.916), ESS (p = 0.002, η² = 0.567), and SPIGET (p < 0.001, η² = 0.813), with significant cycle × time interactions for SPIGET (p = 0.044, η² = 0.585) and ESS (p = 0.036, η² = 0.642), indicating moderate interactive effects (Table 2).

Table 2 Statistical analysis of the effects of menstrual cycle phase and time of day, cycle and time on cognitive fatigue, sleep and physiological parameters

Post-hoc comparisons for cognitive fatigue and sleep parameters

HOOPER score

Post-competition, HOOPER scores significantly increased in the morning across all menstrual phases, indicating greater perceived fatigue. The most notable increases were observed during the follicular phase (mean difference = 2.412 [1.308–3.517]; p < 0.001) and the luteal phase (mean difference = 2.615 [1.604–3.626]; p < 0.001). In the afternoon, post-competition, HOOPER scores also increased significantly, though the effect was slightly less pronounced compared to the morning. The largest differences were seen in the luteal phase (mean difference = 2.154 [0.951–3.356]; p = 0.001) and follicular phase (mean difference = 1.938 [1.015–2.861]; p < 0.001) (Fig. 5).

Fig. 5

Effects of time of day (morning vs. afternoon), competition (Pre vs. Post) and menstrual cycle phases (menstrual, follicular, and luteal) on HOOPER test, PSQI, ESS and SIGEL. Significant differences (p-values) are indicated. Data are presented as mean ± SD

When comparing pre-competition values between morning and afternoon sessions, HOOPER scores were significantly lower in the afternoon across all phases, suggesting reduced perception of fatigue before competition later in the day (mean difference ranging from 1.315 [0.754–1.875]; p = 0.003 to 1.842 [1.002–2.683]; p < 0.001). After the competition, HOOPER scores remained lower in the afternoon than in the morning in the follicular (mean difference = 1.124 [0.382–1.867]; p = 0.005) and luteal phases (mean difference = 1.431 [0.812–2.051]; p < 0.001) (Fig. 5).

PSQI (sleep quality)

Post-competition, PSQI scores increased significantly in the morning during the follicular (mean difference = 1.029 [0.484–1.574]; p < 0.001) and luteal (mean difference = 1.212 [0.648–1.776]; p < 0.001) phases, indicating poorer sleep quality after exertion. In the afternoon, PSQI scores also increased post-competition in the follicular (mean difference = 0.965 [0.455–1.474]; p = 0.001) and luteal (mean difference = 1.001 [0.489–1.513]; p < 0.001) phases (Fig. 5).

Before the competition, sleep quality was significantly better in the afternoon across all phases (mean difference ranging from 0.752 [0.392–1.112]; p < 0.001 to 1.145 [0.815–1.475]; p < 0.001). Post-competition, PSQI scores remained lower (better sleep quality) in the afternoon than in the morning during the follicular (mean difference = 0.654 [0.234–1.074]; p = 0.002) and luteal (mean difference = 0.792 [0.389–1.194]; p < 0.001) phases (Fig. 5).

Epworth sleepiness scale (ESS)

Fatigue significantly impacted daytime sleepiness, as indicated by ESS scores. Post-competition ESS scores increased significantly in the morning during the follicular (mean difference = 1.477 [0.889–2.066]; p < 0.001) and luteal (mean difference = 1.892 [1.104–2.680]; p < 0.001) phases. In the afternoon, ESS scores also increased post-competition, though to a lesser extent (mean difference = 1.224 [0.589–1.859]; p = 0.002 in follicular, 1.409 [0.742–2.077]; p < 0.001 in luteal phase) (Fig. 5).

Before the competition, ESS scores were significantly lower in the afternoon than in the morning across all phases (mean difference ranging from 1.021 [0.487–1.555]; p = 0.001 to 1.312 [0.734–1.890]; p < 0.001). After the competition, daytime sleepiness remained lower in the afternoon compared to the morning during the follicular (mean difference = 0.845 [0.432–1.258]; p = 0.002) and luteal (mean difference = 1.126 [0.601–1.651]; p < 0.001) phases (Fig. 5).

Spiegel Score (Sleep Perception): Spiegel scores, representing sleep disturbances, significantly increased post-competition in the morning across all menstrual phases. The most substantial increases were observed in the follicular (mean difference = 1.931 [0.875–2.987]; p < 0.001) and luteal (mean difference = 2.115 [1.025–3.205]; p < 0.001) phases. In the afternoon, post-competition Spiegel scores also increased significantly in the follicular (mean difference = 1.758 [0.891–2.624]; p < 0.001) and luteal (mean difference = 1.953 [0.973–2.933]; p < 0.001) phases (Fig. 5).

Pre-competition, Spiegel scores were significantly lower in the afternoon across all menstrual phases (mean difference ranging from 1.211 [0.587–1.835]; p < 0.001 to 1.588 [0.957–2.219]; p < 0.001), indicating better sleep perception before afternoon sessions. After the competition, Spiegel scores remained lower in the afternoon than in the morning in the follicular (mean difference = 1.034 [0.412–1.656]; p = 0.001) and luteal (mean difference = 1.295 [0.738–1.852]; p < 0.001) phases, reinforcing the trend of better sleep perception for later competition (Fig. 5).

Effects of factors: cycle and time on RPE and %AVG HR

The results indicate no significant main effects of Cycle or Time on RPE and %AVG HR (all p-values > 0.05). In addition, there is no significant Cycle × Time interaction effect for both RPE (p = 0.886, η² = 0.010) and %AVG HR (p = 0.886, η² = 0.008), though the effect sizes are very small (Table 2; Fig. 6).

Fig. 6

Effects of time of day (morning vs. afternoon), competition (Pre vs. Post) and menstrual cycle phases (menstrual, follicular, and luteal) on RPE and % AVG HR. Significant differences (p-values) are indicated. Data are presented as mean ± SD

Discussion

Impact of time of day on performance

The findings of the present study indicated a significant effect of time of day on various performance metrics, including cognitive, physical, and psychological parameters. Specifically, the results from the Stroop Test indicated that cognitive performance was generally better in the afternoon compared to the morning, as evidenced by faster completion times and fewer errors. This observation is consistent with a substantial body of literature suggesting that cognitive performance tends to peak in the afternoon or evening due to the influence of circadian rhythms on alertness and cognitive function [53]. The circadian rhythm, which is regulated by the suprachiasmatic nucleus in the hypothalamus, drives fluctuations in core body temperature, hormone levels, and arousal, all of which contribute to the observed diurnal variations in performance [54, 55].

The study’s findings extend beyond cognitive tasks to physical performance metrics as well. The results from MAT, RAT, and all demonstrated significant improvements in performance during the afternoon sessions. Specifically, the athletes demonstrated faster times and better agility in the afternoon compared to the morning. This improvement in physical performance can be attributed to the higher core body temperatures observed in the afternoon, which have been shown to enhance muscle function, flexibility, and joint range of motion [29]. Additionally, neuromuscular efficiency tends to be higher later in the day, contributing to improved power and speed [36]. These findings are consistent with previous research that has documented enhanced physical performance in the afternoon due to various physiological and biochemical factors, such as increased enzyme activity and optimized energy metabolism [56,57,58].

The study’s findings also extend to the psychological domain, as evidenced by the results from the POMS questionnaire. The athletes reported lower fatigue and higher vigor scores in the afternoon compared to the morning, which indicates that their mood states were more favorable later in the day. This observation aligns with previous research indicating that the interaction between circadian rhythms and psychological factors can influence diurnal variations in mood states [35]. The improved mood states observed in the afternoon may have a synergistic effect, potentially enhancing both cognitive and physical performance [59, 60]. This interplay between physiological and psychological factors contributes to the overall time-of-day effects on athletic output.

Furthermore, the study’s findings highlighted the significant influence of sleep quality and daytime sleepiness on performance across different times of day. The results from PSQI and ESS indicated that poor sleep quality and higher daytime sleepiness, which were more prevalent in the morning, negatively impacted the athletes’ performance metrics. These observations define the importance of maintaining adequate sleep hygiene and aligning sleep schedules with training and competition times to mitigate the adverse effects of sleep deprivation on athletic performance [39, 61,62,63]. Proper sleep management can help optimize physiological and psychological factors, contributing to improved cognitive, physical, and overall athletic output throughout the day [64].

Menstrual cycle phase and performance

The results of the present study indicate that the menstrual cycle phase significantly impacts the physical performance and psychological responses of elite female volleyball players. Specifically, the athletes exhibited superior performance metrics across MAT, RAT, and Test during the follicular phase of the menstrual cycle. This phase is characterized by lower levels of female hormones, such as estrogen and progesterone, which may contribute to enhanced physical performance and reduced perceived fatigue observed in the participants [65,66,67,68]. The fluctuations in female sex hormone levels throughout the menstrual cycle have been widely documented to influence various physiological and psychological factors, including muscle function, thermoregulation, and mood states [23, 69,70,71]. The relatively lower levels of estrogen and progesterone during the follicular phase may result in improved neuromuscular coordination, reduced joint laxity, and more favorable metabolic conditions, all of which can contribute to the enhanced physical performance observed in the present study [72, 73]. Furthermore, the reduced fatigue levels reported by the athletes during the follicular phase may be associated with hormonal changes and their impact on perceived exertion and mood [74].

Conversely, the performance metrics of the athletes tended to decline during the luteal phase of the menstrual cycle. This phase is characterized by higher levels of progesterone, which could potentially lead to increased body temperature, altered substrate metabolism, and reduced endurance capacity [75, 76]. Previous studies have demonstrated that the luteal phase is associated with higher fatigue scores and decreased muscle efficiency, which aligns with the findings of reduced agility and sprint ability observed in the present investigation [77, 78].

The physiological changes associated with the elevated progesterone levels during the luteal phase may contribute to the observed decrements in physical performance [9, 19]. Increased body temperature can impair thermoregulation and lead to an earlier onset of fatigue, while alterations in substrate utilization and energy metabolism may compromise endurance and power output [79, 80]. Additionally, higher progesterone levels have been linked to increased perceived exertion and decreased neuromuscular function, further contributing to the overall decline in physical performance during the luteal phase [73, 74].

The increased fatigue and negative moods that occur during this phase can have far-reaching effects on the mental well-being of female athletes [81]. Coaches and support staff should consider strategies to support athletes’ mental health during the luteal phase, such as additional recovery time, stress-reducing techniques or adjusting training intensity. Adapting training programs to the physiological and psychological state of athletes can help to reduce the risk of burnout, overtraining and a possible decline in self-confidence [82].

The study’s findings also reported significant variations in psychological responses across the different phases of the menstrual cycle. The results from the POMS questionnaire indicated that the athletes reported higher levels of fatigue and negative mood states during the luteal phase compared to the follicular and menstrual phases. These findings are in line with recent research by Rossi et al. [83], who highlight the complex interplay between hormonal fluctuations and performance anxiety in adolescent female volleyball players. Their study highlights the need for a multi-approach perspective to assess how psychological and physiological factors interact across the phases of the menstrual cycle, potentially influencing both emotional state and athletic performance.

This observation is reliable, with preceding research suggesting that the hormonal fluctuations associated with the menstrual cycle can influence mood and cognitive function, thereby impacting athletic performance [23, 24]. The elevated progesterone levels during the luteal phase have been linked to changes in neurotransmitter systems, brain activity, and emotional processing, which can contribute to the observed increases in fatigue and negative mood states [84,85,86].

The existing literature on the effect of the menstrual cycle on athletic performance presents mixed findings. While some studies report no significant differences in performance across the various menstrual cycle phases, others have identified specific phases where performance is optimized [68, 87]. For instance, a recent study by De Assis Arantes et al. [20] found that muscular endurance peaked during the mid-follicular phase and was lowest in the mid-luteal phase, which is consistent with the observations from the present investigation. The discrepancies in the literature may be attributed to several factors, including differences in study methodologies, participant characteristics, and the specific performance measures assessed [23, 73]. Additionally, the individual variability in menstrual cycle characteristics and hormonal profiles among athletes can contribute to the mixed findings [65]. Some individuals may unveil more pronounced performance changes across the menstrual cycle, while others may be less affected, depending on their hormonal sensitivity and adaptations.

Further, the existing literature suggests that the onset of menstruation can either hinder or have no effect on athletic performance, depending on individual variability and the specific demands of the sport [68]. The findings of the present study, which highlight superior physical performance during the follicular phase, align with those studies that emphasize the positive impact of lower hormonal levels on athletes’ physical capabilities [15, 20].

The relatively lower levels of estrogen and progesterone during the follicular phase may contribute to enhanced neuromuscular coordination, reduced joint laxity, and more favorable metabolic conditions observed in the participants [88]. These physiological factors can lead to improved agility, speed, and endurance, as evidenced by the superior performance metrics in the Modified Agility Test (MAT), Reactive Agility Test (RAT), and Repeated Sprint Ability (RSA) Test. In contrast, the higher hormonal levels during the luteal phase and menstruation may have a more detrimental impact on physical performance, potentially due to factors such as increased body temperature, altered substrate metabolism, and reduced neuromuscular function [74, 76].

Combined effects of time of day and menstrual cycle

The results of this study revealed a significant three-way interaction between the time of day, menstrual cycle phase, and the Hooper Index, a sleep quality measurement, muscle soreness, stress levels, and overall mood. This supports the complex relationship between the circadian system and the menstrual cycle and how their synergistic influence can contribute to diurnal and cyclical variations in an athlete’s overall physical and mental state [25, 35, 36, 55].

Notably, the findings indicated that athletes experienced the most substantial benefits from afternoon training sessions during the follicular phase of the menstrual cycle. During this phase, characterized by lower levels of progesterone and higher levels of estrogen, athletes demonstrated enhanced physical and cognitive performance [23, 68]. In this context, existing research suggests that estrogen may have a protective effect on muscle function and facilitate greater endurance [89]. The afternoon sessions likely further amplified these effects due to the body’s natural circadian rhythm, which tends to peak in the late afternoon, leading to optimal muscle function, core temperature, and alertness [29, 53].

The combination of the follicular phase’s hormonal profile and the body’s diurnal patterns appears to create an ideal physiological and psychological environment for athletic performance. The relatively lower levels of progesterone and higher levels of estrogen during the follicular phase may contribute to improved neuromuscular coordination, reduced joint laxity, and more favorable metabolic conditions [69, 75, 88, 90]. These factors, coupled with the circadian-driven enhancements in physical and cognitive capabilities in the afternoon, resulted in the superior performance observed in the athletes during this specific time and menstrual cycle phase. In contrast, during the luteal phase, the benefits of afternoon training were less pronounced. The study found that performance variations between morning and afternoon sessions were minimal during this phase. This could be attributed to the thermogenic effect of progesterone, which raises core body temperature and potentially affects thermoregulation and overall energy expenditure, thus attenuating the usual afternoon performance peak [91]. Moreover, higher progesterone levels have been associated with increased fatigue, decreased motivation, and reduced muscle efficiency, which could diminish the benefits of afternoon training sessions [92]. The physiological and psychological changes during the luteal phase appear to minimize the typical diurnal variations in performance observed in athletes. The disruption of the body’s natural circadian rhythms on various physiological and cognitive functions may contribute to the attenuation of the afternoon performance advantage during this phase of the menstrual cycle.

Practical applications

Coaches can use these insights to optimize their training plans by matching high-intensity or skill-based sessions to times of day and cycle phases associated with peak performance. For example, a challenging workout in the afternoon of the follicular phase could maximize performance results. Conversely, lighter, recovery-oriented sessions during the luteal phase or in the morning hours, when performance potential is lower, might be more appropriate. Understanding individual variability in circadian rhythms and hormonal responses is critical to tailoring training plans to ensure that each athlete achieves optimal results while minimizing the risk of injury and overtraining. This approach emphasizes the importance of incorporating physiological and hormonal insights into athletic planning to improve overall performance.

Limitation

Although this research offers important understandings of how time of day and menstrual cycle phases impact physical performance and psychological factors in elite female volleyball athletes, it is crucial to acknowledge certain limitations when interpreting the findings. First, hormone levels were not directly measured in the study, which limits the ability to fully understand the physiological mechanisms underlying performance variations. Without hormone measurements, it remains difficult to determine the exact interactions between the phases of the menstrual cycle and athletic performance. Another limitation is the relatively small sample size (n = 13), which may affect the generalizability of the results. Although the within-subjects design and robust statistical analyses help to mitigate this issue, replication with a larger, more diverse sample of female volleyball players or athletes from different sports and levels of competition would improve external validity. Expanding the range of participants could provide a broader perspective on how these factors influence performance in different sports groups. In addition, the study relied on self-reported measures, such as the ESS and the PSQI, to assess sleepiness and sleep quality. While these instruments are widely validated, they are subject to potential biases and inaccuracies. The inclusion of objective assessments, such as polysomnography or actigraphy, could increase the reliability of sleep-related outcomes and provide a more comprehensive understanding of their impact on performance. Furthermore, this study focused on short-term, acute responses and only captured temporal and cyclical fluctuations. While this approach allowed for an examination of fluctuations in performance and psychological responses, it does not account for long-term adaptations. Longitudinal studies spanning several weeks or months would be valuable to determine how these factors evolve and influence overall athletic development. Additionally, the study did not take into account certain individual characteristics, such as fitness level and sleep history, which may have influenced the observed patterns. Individual variability in physiological and psychological responses was also not explicitly considered. The consideration of these factors in future studies could improve the interpretation of the results and provide a more nuanced understanding of the determinants of performance and recovery in female athletes. Finally, possible fatigue or learning effects due to repeated testing over six experimental sessions should be considered. Although the balanced order of testing helped to minimize systematic bias, the cumulative physical and mental demands may have influenced performance trends. This limitation should be considered when interpreting the results, as repeated exposure to test procedures may have led to performance improvements or decrements over time. Future research should consider alternative protocols to better control these effects.

Future directions

Future research incorporating hormonal assessments could clarify the relationship between menstrual cycle phases and athletic performance. Additionally, variations in chronotype and menstrual symptoms, such as cramps, fatigue, and mood changes, may have influenced individual responses, contributing to result variability. Future research should further investigate the effects of menstrual cycle phases on key performance characteristics such as endurance and strength to gain a better understanding of how hormonal fluctuations affect athletic ability. Through integrating expertise from sports science, nutrition, and psychology, a collaborative multidisciplinary approach could yield significant insights into the relationship between circadian rhythms, hormonal shifts, nutritional patterns, and psychological variables, leading to practical strategies for optimizing performance and recovery. In addition, refining the selection of psychological assessment tools to include measures of motivation, performance anxiety, and mental focus would improve our understanding of the psychological responses of female athletes. Indeed, environmental factors such as temperature, humidity, and lighting should also be considered to determine their influence on circadian rhythms and physical performance. The integration of continuous monitoring technologies, including wearable devices, could provide real-time data on performance, recovery, and physiological responses, enabling a more dynamic assessment of these interactions. In addition, investigating the variability of menstrual symptoms and their impact on performance outcomes using validated symptom tracking tools could help clarify individual differences in menstrual-related symptoms and their impact on athletic performance.

Conclusion

This comprehensive study elucidates the significant and interrelated influences of both the time of day and menstrual cycle phases on the physical performance and psychological responses of elite female volleyball players. The findings confirm that athletic performance, particularly in measures of agility, reactive agility, and sprint ability, is generally superior in the evening compared to the morning. This suggests a strong circadian rhythm influence, with athletes potentially benefiting from scheduling intensive training sessions and competitive events later in the day to capitalize on their natural diurnal patterns. Additionally, the study demonstrates that the menstrual cycle phase markedly affects performance metrics and mood states, with the follicular phase showing the best overall physical and psychological outcomes. The intricate interplay between the time of day and the menstrual cycle phase revealed that the optimal convergence of performance occurs when training sessions are strategically aligned with the afternoon hours of the follicular phase. In contrast, the luteal phase showed less variation in performance between morning and afternoon sessions, potentially due to the physiological and thermoregulatory effects of the elevated progesterone levels. These findings accent the critical necessity for considering both circadian and menstrual cycle factors when planning training regimens for elite female athletes.