Modelling menstrual cycle length in athletes using state-space models

The ability to predict an individual’s menstrual cycle length to a high degree of precision could help female athletes to track their period and tailor their training and nutrition correspondingly. Such individualisation is possible and necessary, given the known inter-individual variation in cycle length. To achieve this, a hybrid predictive model was built using data on 16,524 cycles collected from a sample of 2125 women (mean age 34.38 years, range 18.00–47.10, number of menstrual cycles ranging from 4 to 53). A mixed-effect state-space model was fitted to capture the within-subject temporal correlation, 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 (1) a time trend component using a random walk with an overdispersion parameter, (2) an autocorrelation component using an autoregressive moving-average model, and (3) a linear predictor to account for covariates (e.g. injury, stomach cramps, training intensity). The inclusion of an overdispersion parameter suggested that $$26.36\%$$ 26.36 % $$[23.68\%,29.17\%]$$ [ 23.68 % , 29.17 % ] of cycles in the sample were overdispersed. The random walk standard deviation for a non-overdispersed cycle is $$27.41 \pm 1.05$$ 27.41 ± 1.05 [1.00, 1.09] days while under an overdispersed cycle, the menstrual cycle variance increase in 4.78 [4.57, 5.00] 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 and Pearson correlation coefficient (r) between the observed and predicted values were calculated. The model had an RMSE of 1.6412 days, a precision of 0.7361 and overall accuracy of 0.9871. In conclusion, the hybrid model presented here is a helpful approach for predicting menstrual cycle length, which in turn can be used to support female athlete wellness.

Ovarian Reserve Biomarkers and Menstrual Cycle Length in a Prospective Cohort Study

Context While age-related changes in menstrual cycle length are well-known, it is unclear whether AMH or other ovarian reserve biomarkers have a direct association with cycle length. Objective To determine the association between biomarkers of ovarian reserve and menstrual cycle length. Design Secondary analysis using data from Time to Conceive (TTC), a prospective time-to-pregnancy cohort study. The age-independent association between cycle length and biomarkers of ovarian reserve was analyzed using linear mixed and marginal models. Setting and Participants TTC enrolled women aged 30 to 44 with no history of infertility who were attempting to conceive for <3 months. Serum AMH, FSH, and Inhibin B levels were measured on cycle day 2, 3, or 4. Participants recorded daily menstrual cycle data for ≤ 4 months. Main Outcome Measures Primary outcome was menstrual cycle length; follicular and luteal phase lengths were secondary outcomes. Results Multivariable analysis included 1880 cycles from 632 women. Compared with AMH levels of 1.6-3.4 ng/mL, women with AMH <1.6 ng/mL had cycles and follicular phases that were 0.98 (95% Confidence Interval (CI): -1.46, -0.50) and 1.58 days shorter (95% CI: -2.53, -0.63), respectively, while women with AMH >8 ng/mL had cycles that were 2.15 days longer (95% CI: 1.46, 2.83), follicular phases that were 2 days longer (95% CI 0.77, 3.24), and luteal phases that were 1.80 days longer (95% CI 0.71, 2.88). Conclusion Increasing AMH levels are associated with longer menstrual cycles due to both a lengthening of the follicular and the luteal phase independent of age.

Modelling Menstrual Cycle Length in Athletes Using State-space Models

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