Female athletes have hormonal and physiological characteristics that may require specific training adaptations.1 The number of studies conducted on female athletes and, more specifically, on female soccer players has grown in recent years.2–4 The impact of the natural-menstrual-cycle (NC) phases or the hormonal phases (active/inactive pills) in combined oral contraception (OC) on players training responses remains unclear.5–7 OC users are frequently excluded or used as a control group due to a potentially stable hormonal profile. But this profile induces a significant downregulation of endogenous sex hormones (via inhibition of gonadotropic hormones), which may have a potential effect on well-being or performance outcomes.8
Many teams use athlete monitoring systems, like relying on daily self-report, to evaluate their readiness and training performance.9,10 Common monitoring systems involve mobile applications and collect information on athletes’ fitness, sleep quality, mood, or rate of perceived exertion.11–13 Recently, several apps have been including menstrual cycle monitoring, following the beginning and the end of menstruation, symptoms, and determining the length of cycles.14 To monitor NC athletes, recent reviews suggest a combination of 3 methods: calendar-based counting methods, urinary ovulation prediction, and the measurement of serum estrogen and progesterone concentrations.15–18 However, it is hardly feasible, in real life, to include the last 2 in long-term NC monitoring in high-level athletes. For OC users, the different hormonal phases are easily defined according to the pill’s composition. Monitoring menstrual characteristics using the calendar-based method can be a first approach to identify the athlete’s specific profile, and that is an initial step to understand the influence of hormonal status on performance and/or well-being.14,17
To our knowledge, scientists and staff do not have any guidelines to implement NC and OC monitoring in addition to the classic elite athlete monitoring systems. The main advantage of app use is cost efficiency and ease of use for both athletes and staff.19 App usage, however, relies on consistent input from the athletes and regular monitoring from support staff to ensure there are no abnormalities, mistakes, or missed data entry. Methods sections often lack explanations or details regarding field application of such monitoring systems,17 especially in regard of NC and OC-pill phases, which have been rarely studied. Indeed, the methodological approach of an on-field monitoring, including all its pitfalls (eg, follow-up duration, technicalities—paper notation, phone call, online application, etc) and the athlete’s adherence or compliance, is rarely indicated.
Therefore, the purpose of this paper was to report a feasible and on-field methodological approach regarding monitoring athlete menstrual status, based on an online calendar application, to establish the athlete’s menstrual profile. Athletes’ commitment was also observed through their adherence and compliance, which were studied daily over the course of 7 months in an elite female soccer team, according to players’ menstrual status, age, and field position. This new method should help and encourage the implementation and optimization of NC and OC monitoring in teams.
Study Design
Participants
A National Second Division French soccer club volunteered to participate in this study. The players of the team received explanations of the study’s aims and methods. In addition, their written informed consent was collected. The study’s ethical standards conformed to the recommendations of the Declaration of Helsinki (2013). From a pool of 21 professional female soccer players, 19 agreed to participate. The monitoring began in early October, corresponding with the start of their competitive season, and finished at the end of April. In the French Championship and Cup, the team played an average of 3 competitive matches per month during the 2021–2022 season. The 19 players included 3 goal keepers, 7 defenders, 6 midfielders, and 3 forwards. All players were French except one Haitian and one Polish player. They had either a professional contract (n = 9), employment (n = 5), or were studying (n = 5). During the 2021–2022 season, 5 players participated in international selections with their national team. All other players had at least one selection in youth or a national team during their career. Two participants reported an anterior cruciate ligament injury during the data collection period and stopped the monitoring. In March, one player reported an Achilles’ tendon injury and discontinued the monitoring for the last month. They were excluded from further analysis after their injury. One player discontinued OC-pill usage after 2.5 months, and thereafter, the athlete was considered NC for adherence and compliance analysis but was removed from all other analysis.
Methodology
Initial Questionnaire
Before menstrual status monitoring started, players completed an online initial questionnaire with closed-ended questions, relying on a Likert scale, yes–no questions, multiple-choice questions, and open comments. Designed to take 5 to 10 minutes, it allowed for the collection of demographic information (age, weight, and height), aspects of training and competition level (starting age of playing, weekly training duration), information about menstrual status history (menarche, contraception use, typical cycle duration and variability in length, and experienced pain or symptoms), and self-perceived cycle-related effect on performance (training and match). Participants were asked whether they were comfortable discussing their cycles with their staff. Players were also free to disclose previous diagnosis of medical conditions (including endometriosis, polycystic ovary syndrome) or surgical antecedents. Hormonal contraceptive users were asked to indicate the contraceptive method (type and brand) and what was involved in their decision. Players were also asked to indicate their interest in understanding the impact of their cycle from a nutritional, physiological, or psychological perspective with a visual analogic scale from 1 (not necessarily) to 5 (yes, it is essential). For each area, answers were converted in points to quantify each player’s interest. Points were then summed to create the player’s interest score index. Because there were 3 areas, total score ranged from 3 (minimum) to 15 (meaning that every area was essential for the player).
Menstrual-Status Monitoring and Phase Determination
The menstrual status monitoring relied on a menstrual diary, which was completed daily using an online application developed for that purpose. Every morning, the participants had to complete a questionnaire designed to take less than 1 minute. They indicated the start and the end of menstruation for the naturally menstruating players (which may also include copper-based intrauterine devices [IUDs])18 or the start or end of placebo/inactive pills for female athletes using combined OC. They noted the presence of menstrual symptoms and pain/injury (Supplementary Material [available online]). This questionnaire allowed tracking of other parameters related to performance and well-being. Thus, players also answered questions about their sleep, their perceived physical condition and emotional feelings, and perceived exertion rate posttraining/match (Supplementary Material [available online]). We regularly reminded the players to complete the questionnaire and to activate notifications. After 3 days of noncompletion, a message was sent to the athlete to ensure there were no technical problems with the surveys.
Players’ adherence to the daily monitoring (completion rate) was expressed as a percentage and determined by calculating the number of completion days divided by the total number of monitoring days. Players’ compliance (ie, filling in the app before noon) was calculated as the number of completions before noon divided by the total number of completion days. At the end of each month, 2 spreadsheet tools were used to evaluate players’ adherence and compliance (Figure 1A and 1B). For compliance analysis, as it was possible to wake up after 12:00 PM, times of waking up and app completion were compared. Times were also adapted based on players’ time zone travel.
The online menstrual status diaries allowed researchers to determine the length and phases of each cycle. Different phases were selected depending on athletes’ menstrual status. As previously described,1 we used 2 phases based only on the menstruation status defined according on the presence of clinical bleeding. For all athletes with NC, the first step consisted of estimating ovulation.20 Then, the follicular phase (FP) was defined from the first day of menstrual bleeding to the estimated ovulation day. FP was divided into 3 subphases: menses, mid-FP, and late FP.10 The luteal phase (LP) was calculated between the ovulation day and they day prior to the beginning of menstrual bleeding. LP was divided in 3 subphases: early LP, mid-LP, and late LP.10 For athletes who used hormonal contraceptives, phases were determined according to the type of hormonal contraceptive. Details about phase determination are included in Supplementary Material (available online). In parallel to the data collected, a spreadsheet was used to follow the estimated menstrual status phases (Figure 1C).
A daily application check was performed by the investigator to prevent wrong interpretations with data exportation and analysis at the end of the monitoring.
Statistical Analysis
All numerical data are presented as mean (SD). All data normal distributions were tested using the Kolmogorov–Smirnov test, and the homogeneity of variance was tested using the F test. A parametric T test was used to compare demographic information and menstrual status-related history between players with NC and OC users. For the interest question, a group categorization was performed to assess potential differences in subgroups by menstrual status (ie, natural menstrual cycle [NC players] vs oral contraceptive users [OC users]); age (ie, ≤20 y old vs 21–25 y old vs >25 y old); and field position (ie, goal keepers vs defenders vs midfielders vs forwards). Accordingly, an appropriate statistical test (t test or 1-way analysis of variance) was used. For oversights, a 2-way analysis of variance was used to determine month effect and day effect. When a significant effect was found, post hoc multiple comparisons were performed using the Tukey test. Pearson correlations were used to test relationships between interest and other variables. P values <.05 were considered statistically significant. Statistical analyses of were carried out with the PRISM software (version 8.4.3, StatSoft Inc).
Results
Table 1 shows the descriptive data from the initial questionnaire. The mean age of 19 players was 23.7 (4.4) years. Five players were under 20 years old, 6 between 21 and 25, and 9 over 25 years old. The anthropometric characteristics were not different between NC players and OC users. The starting age of practice was similar in the 2 groups. Overall, the declared training volume was 9.4 (2.4) hours per week and significantly higher in OC users group (P = .03; Table 1).
Descriptive Data of the Female Soccer Players Enrolled in the Study
Variable | All (N = 19) | NC players (n = 14) | OC users (n = 5) | P |
---|---|---|---|---|
Age, y | 23.7 (4.4) | 23.5 (4.5) | 24.4 (4.7) | .71 |
Body weight, kg | 58.4 (6.2) | 58.8 (6.7) | 57.4 (5.0) | .68 |
Body fat, % | 20.0 (2.6) | 19.7 (2.7) | 20.9 (2.3) | .38 |
Height, cm | 164.7 (5.7) | 165.0 (6.6) | 163.8 (2.2) | .69 |
Starting age of playing, y | 6.8 (1.8) | 6.6 (1.7) | 7.2 (2.2) | .57 |
Training volume, h/wk | 9.4 (2.4) | 8.7 (1.9) | 11.4 (2.6)* | .03 |
Age of first menses, y | 13.4 (0.8) | 13.3 (0.8) | 13.8 (0.8) | .25 |
Abbreviations: NC players, players with natural menstrual cycle; OC, oral contraceptive pill. Note: Values are presented as mean (SD).
*P < .05 NC players vs OC users.
The majority of athletes (89.5%) were comfortable communicating about their menstrual cycle with their coaches/physical trainers. The average age of menarche for all players was 13 (1) years old. No players reported amenorrhea. Fourteen players (73.7%) did not use hormonal contraception, forming the NC group. Among them, 4 reported having irregular cycles. Concerning the 5 players using hormonal contraceptive, all used OC pills. Four used combined monophasic pills, and one used microprogestative pills without pause. For 2 players, their contraception choice aimed to limit the menstrual symptoms.
Overall, players declared an average of 4 menstrual symptoms per cycle with 95% of players reporting at least one symptom. NC players tended to declare more symptoms than OC users, 4.8 (3.2) versus 2.0 (1.7) (P = .086), respectively. Tiredness (52.6%), mood change (36.8%), and stomach cramps (36.8%) were the most frequently reported symptoms. According to players’ declarations, these symptoms had a strong impact on training performances, but only 2 players (10.5%) reported missing training “sometimes” and 3 “rarely” (15.8%). In addition, 45% of players observed modifications in their training and match performance over their cycles, especially NC players (only one OC user). Alterations mainly occurred during menses.
The phase determination gave menstrual status pattern for each athlete. Individual profiles are presented in Figure 2. Dotted gray lines indicate what the player reported in the initial questionnaire. The absence of dotted lines means that the player has classified her cycles as “irregular.” Over the entire follow-up, 5 to 7 complete cycles were obtained for each player. For NC players, the average length of the menstrual cycle was 30 (3) days, ranging from 21 to 48 days. Mean bleeding length was 6 (1) days, ranging from 4 to 11 days (one player consistently presented bleeding longer than 7 d and sought medical advice). The average variation between cycles was 3.5 (2.1) days for NC players and 0.0 (0.0) day for OC users.
Mean interest score index (ISI) was 9.8 (3.0) with no difference between NC players and OC users (P = .61; Figure 3A) or between field positions (P = .39). ISI seemed visually lower in young players; however, there was no statistical difference according to age (P = .60; Figure 3B). ISI was positively correlated with the number of experienced symptoms (r = .54, P = .03; Figure 3C). Players were largely interested in physiological aspects (P = .03; Figure 3D).
Table 2 shows players’ adherence and compliance during the monitoring. A decrease in both adherence and compliance in December and at the end of the season (March, April) was observed. Adherence tended to be different between months (P = .07), but compliance did not differ (P = .60). During the whole monitoring period, no difference was found in adherence and compliance according to contraceptive use (NC players vs OC users, respectively, P = .93 and P = .63), age (respectively, P = .29 and P = .57), and field position (respectively, P = .14 and P = .45).
Descriptive Data About Players’ Adherence and Compliance
Month | Adherence, % | Compliance, % |
---|---|---|
October | 92.0 (13.4) | 82.5 (13.1) |
November | 92.7 (7.3) | 81.7 (15.4) |
December | 86.9 (10.2) | 72.4 (16.5) |
January | 88.2 (11.2) | 75.7 (21.1) |
February | 88.0 (15.2) | 73.4 (24.7) |
March | 83.3 (18.2) | 74.1 (22.2) |
April | 77.9 (24.0) | 74.2 (24.4) |
Mean | 87.0 (14.2) | 76.3 (19.6) |
Note: Values are presented as mean (SD).
Players’ adherence was positively correlated with the ISI during the first 4 months of monitoring (r = .52, P = .021). The correlation tended to remain after 5 (r = .46, P = .065) and 6 months (r = .42, P = .072) of monitoring but was no longer significant at the end of the follow-up (r = .35, P = .13). Players’ compliance tended to be correlated with ISI (P = .09, r = .39) at the end of the monitoring.
Four to 11 players were concerned by these reminders. Players needed the most reminders in December. The number of unfilled out questionnaires was significantly higher in December, March, and April versus October (respectively, P = .004, P = .0001, P = .0008) and November (respectively, P = .011, P = .0004, P = .003). A trend was observed for a day effect (P = .09) wherein unfilled out questionnaires appeared to be more frequent on Thursdays and weekends.
Discussion
We report a feasible and on-field methodological approach to adequately monitor NC and OC athletes based on an online calendar app, which helps to estimate each athlete’s menstrual profile. To our knowledge, this is the first study to assess female players’ interest in tracking menstrual status. We draw attention to several vigilance points surrounding the implementation of menstrual cycle monitoring for team sport athletes.
The initial questionnaire completed at the beginning of the study provided access to essential information, particularly the menstrual status history, the self-perceived effect of menstrual status on performance and general fitness, and the ease of talking about cycles with their coach or staff. Though 2 players (10.5%) felt uncomfortable in communication about their cycles with their coaches, it was not a problem for the majority of the athletes. This was an unexpected result given that previous studies revealed that 63% of athletes did not want to talk to their coaches about their menstrual cycles, particularly when male coaches were concerned.21 Parker et al22 highlighted that Women’s Soccer League players were not discussing their menstrual characteristics with a health care professional. Similar observations were made among elite female rugby players and Australian female athletes.23,24 Some players typically fear that coaches may make competition choices (on field vs reserve player) based on what information they provide about their menstrual phases.25 However, if athletes and scientists want to observe the impact of menstrual status on performance variables, they will have to work with strength and conditioning coaches to plan tests on specific days of each cycle phase.
Thus, we recommend creating a communication space between scientists and players in addition to a specific dashboard calendar for coaches with days only (Supplementary Material [available online]).
The average age at menarche was similar to that reported in various elite athlete populations21,23,26 but slightly older than in the general population (12.5 y) according to a French report.27 Previous studies reported a prevalence of low energy availability between 24% and 88% in female soccer players.28–30 The primary etiology of amenorrhea is functional due to an inadequate caloric intake, resulting in chronic energy deficiency.31 In the studied team, no player reported amenorrhea, suggesting that they had adequate energy intake.
Although our sample is small, the proportion of NC players and OC users is similar to results obtained in other studies22,24,32 but differs from those obtained in competitive Norwegian skiers and biathletes (68% used hormonal contraceptive).33 Proportions of hormonal contraceptive users may depend on sport and cultural contexts. With an average of 4 symptoms per cycle and 95% of players having at least one, the distribution of symptoms (mostly tiredness, mood change, and stomach cramps) is consistent with previous studies.22,23,34 Despite the severity of symptoms and their impacts, few players missed practices. This is concurrent with previous observations showing that 13% of players reported missed training or matches in English clubs.35
More than half of the players (55.6%) estimated that their performance and perceived physical condition changed during their cycle, particularly during menstruation. These observations are consistent with a 2022 study wherein McNamara et al36 showed that two-thirds of elite female athletes preparing for Olympic and Paralympic Games perceived that cycle affected performance. Most athletes identified a window for optimal performance36 but were mostly negatively impacted during menses.22,35 Among players identifying changes, none reported adapting their trainings according to their cycles, suggesting that some players trained although they felt unable to do so thoroughly. Findlay et al23 also reported that several rugby players did not believe that their menstrual symptoms were a valid reason to refrain from training. Interestingly, 80% of players in our study thought that knowing more about their menstrual profile and the consequences of variations in their cycles could help them better manage their training. ISI results between NC players and OC users were similar. However, we observed that players who experienced more symptoms were more interested in menstrual tracking.
Surprisingly, among the numerous studies using menstrual cycle monitoring, athlete adherence and compliance on different methods were never mentioned. Only one study evaluated athlete compliance related to urine sample collection and reported a moderate compliance (66%), with only 3 participants who collected at least 75% of scheduled samples.37 Here, we displayed a mean score above 75%, suggesting a good adherence and compliance rate. Such high scores were likely a result of regular reminders to complete the surveys in the app. Adaptations to the timing of survey completion were sometimes needed when players went to training camp with their national teams and had jet lag and or less phone access.
We did not observe any adherence or compliance difference between athletes according to age, field position, and contraceptive use. However, the trends of correlation observed between ISI score and compliance suggest that ISI could be used as an indicator of players’ survey compliance. A lower adherence was observed in December as well as the end of the season (March and April). Unsurprisingly, reminder messages on Thursdays or at the end of the week (Saturdays, usual game eve) commonly occurred. Throughout a soccer season, December corresponds to the winter break period, meaning fewer team practices, no competition, a vacation, and a festive period for most players. Therefore, we suggest discussing results with players in December or January (midseason monitoring) to keep players engaged with the surveys. The decrease of adherence at the end of the season, especially in April, may be the result of surveillance lassitude. Also, some players with college student status took exams this month. Regarding weekdays, players rarely trained on Thursdays as it was their rest day, which may, in part, explain larger omission rates.
The monitoring allowed cycle phases and the menstrual profile to be estimated for each athlete. Cycle and bleeding durations were consistent with values reported in previous studies.37,38 We observed a large intraindividual and interindividual variability, highlighting athletes’ singularity and supporting an individual approach.6,14,39,40 Cycle monitoring may help to identify variations in length or symptomatology. A cycle becoming longer or shorter could be an indication of training load adaptation.
Sometimes, NC players would not indicate the end of their menstruation. A follow-up with the athlete would occur to see whether it was a simple oversight rather than a cycle delay. It is important to be tactful and remain extremely attentive to confidentiality. We have seen developments regarding athlete contracts with the addition of items on securing status, salary, maternity leave, or sponsors during pregnancy,41,42 but not all athletes have such guarantees.
Differences were sometimes observed between what athletes reported in the initial questionnaire and what we found at the end of the monitoring, such as about the length of the cycles (shown in Figure 3). For example, player number 5 reported herself as “irregular,” whereas her cycles were regular. Conversely, player number 10 reported cycles between 28 and 32 days whereas they were actually shorter or longer. Similarly, differences can be unveiled about symptoms (initial questionnaire vs daily monitoring) or the impact of menstrual cycle phases (self-perceived vs analysis of monitored well-being and training variables). All together, these observations support longitudinal monitoring to better characterize athletes’ menstrual profiles and provide better information for them. OC users should also be monitored, especially during the first months of pill utilization, to determine whether the chosen pill is suitable or has an effect on their training quality or performance. Studies are also lacking on this subject.39 In our study, no players had any other hormonal contraception (eg, implants, intrauterine devices, patches, etc).
Practical Application
To our knowledge, this is the first on-field methodological approach in implementing NC and OC monitoring of female soccer players. The first strength of this methodology (summarized in Figure 4) is to be easily transferable to other cohorts of athletes, in individual or team sports, whatever the level. Our monitoring allows inclusion of all athletes (with NC, OC, other hormonal contraceptive methods, or menstrual dysfunctions), which is important to maintain team spirit.
Based on the latest recommendations surrounding menstrual cycles of female athletes,15–17 our methodology would gain accuracy in using ovulation testing (urinary luteinizing hormone surge testing), which is not as expensive or time consuming as blood sampling. This would avoid a miscategorization between FP and LP and allow detection of anovulatory cycles. However, the method we developed was designed to allow easy longitudinal monitoring and to be implemented in an elite sports context. We believe that ovulation testing should not be normalized in the sport context without prior professional training and appropriate ethical validation, considering the intimacy of such measures.43 Assuming that the thermogenic effect of increased progesterone during the LP can cause a small increase of body basal temperature, the body basal temperature method could be used to indicate, retrospectively, that ovulation has occurred. However, the validity and reliability of this noninvasive method are questionable.44 It might also be difficult to include this measure, which needs a strong practice, in the busy elite athlete planning. Repeated blood sampling seems not to be appropriate for athletes’ long-term surveillance considering the invasiveness of such measurements as well as the substantial human, material, and time resources needed. However, we suggest that urinary and blood hormone monitoring methods should be considered when irregularities are identified and/or for female athletes who experience menstrual symptoms.
By identifying the menstrual profile of each athlete, coaches could observe the impact of menstrual status on training ability and on performance and then adapt their program. In addition, significant variations in cycle length can be detected early, and if they persist, their causes can be investigated (imbalance between training load and energy intake, sleep, stress, etc), and appropriate strategies can be implemented and evaluated.
Conclusion
The methodological approach presented in this article is intended to guide scientists, researchers, and staff members when implementing or optimizing menstrual-status monitoring from an online application. As every athlete’s menstrual status is different, this methodology may be used as a first approach to identify the athlete’s menstrual profile and observe its effects on well-being and performance. Adding objective methods (hormonal assays and ovulation tests) may better describe the impacts of hormonal variation.
Acknowledgments
This study received funding from the Agence National du Sport and from the Institut National du Sport de l’Expertise et de la Performance. The authors want to thank both institutions for their full support and the players and coaching staff for their great cooperation throughout the season. Dupuit was in constant contact with players and coaches and supervised complete monitoring during the season. She analyzed all data with Blanquet and Chassard and wrote the first drafts of the paper. LeHeran, Delaunay, and Bernardeau were staff members and privileged contact for monitoring implementation during the season. Author Contributions: Overall study design: Meignié, Antero, and Duclos. Successive versions of the manuscript: Dupuit, Meignié, Antero, Duclos, and Toussaint. Manuscript revisions and approval of the submitted version: All authors.
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