Hillard, P. | bioRxiv Science

bioRxiv2019

The menstrual cycle is a primary contributor to cyclic variation in women’s mood, behavior, and vital signs

Female mood, behavior, and vital signs exhibit cycles which fundamentally affect health and happiness. However, it is unclear which dimensions of mood, behavior, and vital signs vary cyclically, how cycles at different timescales compare in magnitude, and how cycles vary across countries. Here we separate female mood, behavior, and vital signs into four simultaneous cycles - daily, weekly, seasonal, and menstrual. We analyze nine mood dimensions, three behavior dimensions, and three vital signs using a dataset of 241 million observations from 3.3 million women in 109 countries. We find that the menstrual cycle is a primary contributor to cyclic variation: it is the cycle with the largest amplitude for all three vital signs, sexual activity, and 7 out of 9 dimensions of mood. The amplitude of the menstrual happiness cycle is 1.4x the amplitude of the daily cycle, 3.3x the amplitude of the weekly cycle, 2.3x the amplitude of the seasonal cycle, and 1.7x the Christmas increase in happiness. Menstrual cycle effects are directionally consistent across countries, demonstrating that they occur across cultures. Overall, our results demonstrate the primacy of the menstrual cycle and necessitate better accounting for it in clinical data and practice.

physiology↗

bioRxiv2018

Assessment of Menstrual Health Status and Evolution through Mobile Apps for Fertility Awareness

For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician. While these evaluations provide critical information on an individuals reproductive health status, they typically rely on memory-based self-reports, and the results are rarely, if ever, assessed at the population level. In recent years, mobile apps for menstrual tracking have become very popular, allowing us to evaluate the reliability and tracking frequency of millions of self-observations, thereby providing an unparalleled view, both in detail and scale, on menstrual health and its evolution for large populations. In particular, we were interested in exploring the tracking behavior of the app users and their overall observation patterns in an effort to understand if they were consistent with previous small-scale medical studies. We also investigated whether their precision allowed the detection and estimation of ovulation timing, which is critical for reproductive and menstrual health. Retrospective self-observation data were acquired from two mobile apps dedicated to the application of the sympto-thermal fertility awareness method, resulting in a dataset of more than 30 million days of observations from over 2.7 million cycles. The analysis of the data showed that up to 40% of the cycles in which users were seeking pregnancy had recordings every single day. With a modeling approach using Hidden Markov Models to describe the collected data and estimate ovulation timing, it was found that follicular phases average duration and range were larger than previously reported, with only 24% of ovulations occurring at days 14 to 15, while the luteal phase duration and range were in line with previous reports, although short luteal phases (10 days or less) were more frequently observed (in up to 20% of cycles). The digital epidemiology approach presented here can help to lead to a better understanding of menstrual health and its connection to womens health overall, which has historically been severely understudied.

epidemiology↗