Real-Life Insights on Menstrual Cycles and Ovulation Using Big Data
First published: 2020
Brief summary
Statistical modeling of a large real-world menstrual cycle dataset examines the relationship between total cycle length and the timing of ovulation, refining prediction of the fertile window beyond the traditional fixed-cycle assumption.
Article
Real-Life Insights on Menstrual Cycles and Ovulation Using Big Data is a peer-reviewed journal article published by Human Reproduction Open (Oxford) in 2020. It focuses on statistical modelling of a large real-world menstrual cycle dataset examines the relationship between total cycle length and the timing of ovulation, refining prediction of the fertile window beyond the traditional fixed-cycle assumption.
The analysis focuses on cycle lengths analysed across 23-35 day range. It also considers robust regression modelling of follicular phase vs. total cycle length. It also considers large real-world tracking-app dataset. This gives the cycle claim a specific numerical and evidential setting rather than presenting periodicity only as a visual impression.
The article reports the following result: The current study investigated a large dataset on the menstrual cycles of women seeking to conceive, using robust regression analysis to explore the relationship between the follicular phase and total cycle length. The interpretation is strongest when sample selection, age, physiology, measurement error and individual variability are accounted for.
For cycles researchers, the article brings together menstrual cycles, ovulation timing, fertile window, reproductive big data. It is relevant to biological cycle research because living systems contain interacting clocks and rhythms whose periods vary with physiology, age, environment and measurement method.
Because it is a peer-reviewed journal article, the article is a strong starting point for discussion in the Biology & Human Biology forum, although its conclusions should still be compared with later replications and updated datasets.
Source details and credits
- Source / publisher: Human Reproduction Open (Oxford)
- Source type: Peer-reviewed journal article
- URL type: WWW
- Credits: Human Reproduction Open (Oxford)
- URL: https://academic.oup.com/hropen/article/2020/2/hoaa011/5820371