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...
Guardado en:
Autores principales: | Thiago de Paula Oliveira, Georgie Bruinvels, Charles R Pedlar, Brian Moore, John Newell |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/0fe16aad3c494f218d67d7adeace97c9 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Sex differences in sleep and influence of the menstrual cycle on women's sleep in junior endurance athletes.
por: Maria Hrozanova, et al.
Publicado: (2021) -
A microfluidic culture model of the human reproductive tract and 28-day menstrual cycle
por: Shuo Xiao, et al.
Publicado: (2017) -
Ocular biometric characteristics during the menstrual cycle
por: Çakmak H, et al.
Publicado: (2015) -
Spectral dynamic causal modelling in healthy women reveals brain connectivity changes along the menstrual cycle
por: Esmeralda Hidalgo-Lopez, et al.
Publicado: (2021) -
Copeptin levels remain unchanged during the menstrual cycle.
por: Claudine A Blum, et al.
Publicado: (2014)