Modelling Menstrual Cycle Length in Athletes Using State-space Models

Abstract

The ability to predict menstrual cycle length to a high degree of precision enables female athletes to track their period and tailor their training and nutrition correspondingly knowing when to push harder when to prioritise recovery and how to minimise the impact of menstrual symptoms on performance. Such individualisation is possible if cycle length can be predicted to a high degree of accuracy. To achieve this, a hybrid predictive model was built using data on 16,990 cycles collected from a sample of 2,178women (mean age 33.89 years, range 14.98 - 47.10, number of menstrual cycles ranging from 4 - 53). To capture the within-subject temporal correlation, a mixed-effect state-space model was fitted incorporating a Bayesian approach for process forecasting to predict the duration (in days) of the next menstrual cycle. The modelling procedure was split into three steps(i) a time trend component using a random walk with an overdispersion parameter, (ii) an autocorrelation component using an autoregressive moving-average (ARMA) model, and (iii) a linear predictor to account for covariates (e.g. injury, stomach cramps, training intensity). The inclusion of an overdispersion parameter suggested that 26.81% [24.14%,29.58%] of cycles in the sample were overdispersed where the random walk standard deviation under a non-overdispersed cycle is 1.0530 [1.0060,1.0526] days while under an overdispersed cycle it increased to 4.7386 [4.5379,4.9492] days. To assess the performance and prediction accuracy of the model, each woman’s last observation was used as test data. The Root Mean Square Error (RMSE), Concordance Correlation Coefficient (CCC) and Pearson correlation coefficient (r) between the observed and predicted values were calculated. The model had an RMSE of 1.6126 days, a precision of 0.7501 and overall accuracy of 0.9855. In the absence of hormonal measurements, knowing how aspects of physiology and psychology are changing across the menstrual cycle has the potential to help female athletes personalise their training, nutrition and recovery tailored to their cycle to sustain peak performance at the highest level and gain a competitive edge. In conclusion, the hybrid model presented here is a useful approach for predicting menstrual cycle length which in turn can be used to support female athlete wellness to optimise performance

Characteristics of the Menstrual Cycle According to the Sports Groups of the New Generation of Female Athletes

“…Logistic regression showed that there are no significant predictors for predicting cycle regularity. Certain studies (Oliveira et al, 2021) show the development of models for predicting the regularity of the menstrual cycle in female athletes, such as a hybrid predictive model, but it is more focused on predicting the length of the menstrual cycle (Oliveira et al, 2021). Meignié et al (2021) state that during the menstrual cycle among top female athletes, various parameters related to sports performance are affected, but the parameters themselves and the size and direction of the effects are not convincing.…”