Modelling menstrual cycle length in athletes using state-space models.

ABSTRACT: 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 tr

SUBMITTER: de Paula Oliveira T 

PROVIDER: S-EPMC8379295 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature