O007 Menstrual Cycle Phase Classification Methods and the Relationship with Sleep, Mood, and Performance in Elite Female Athletes
Sleep Advances(2024)
School of Psychological Sciences
Abstract
Abstract Introduction Female athletes are often excluded from sports-science research due to hormonal variations across the menstrual cycle. Effects of menstrual cycle phase on outcomes are inconclusive likely due to between-study variation in phase classification. We aimed to test if the relationship between menstrual cycle phase and sleep, mood, and performance differed depending on whether a continuous, 2-phase, 3-phase, or 4-phase approach was used to classify cycle phase. Methods Elite Australian Rules footballers reporting a regular menstrual cycle and not on contraception (N=18, M-age=24.9±5.1) were recruited. Over two weeks, they completed continuous sleep/wake monitoring via actigraphy and sleep diaries, daily self-reported sleep quality, mood, and performance ratings, and five sessions of cognitive (vigilant attention) and physical (ecological kicking task) performance testing. Using baseline self-report data, menstrual cycle phase for each day of the study was inferred using a continuous (percentage through the cycle), 2-phase (follicular, luteal), 3-phase (menses, mid-late follicular, luteal), and 4-phase (menses, mid-late follicular, early-mid luteal, pre-menses) model. Results No outcome was consistently predicted by all phase-models. Objective sleep efficiency was lower in menses according to the 3- and 4-phase models. Subjective mood and performance were higher in the mid-late follicular according to the 3-phase model. Discussion The relationships between menstrual cycle phase and sleep, mood, and performance depend on how menstrual cycle phases are classified. Discordant findings highlight the importance of methodological differences in athlete menstrual cycle research. The effects of the menstrual cycle phase need to be further studied using consistent and standardised classifications across studies.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined