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

Abstract

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.

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data

“…While there has been ample work on hormone-level characterizations of the menstrual cycle [68][69][70][71] , studies of the relationship between menstrual patterns and symptomatic variables are limited-recent work has explored this association using selftracked data, but over a limited set of symptoms 72 and without discriminating over age or birth control usage 60 . A method for estimating ovulation timing based on Fertility Awareness Method observations (i.e., basal body temperature (BBT), cervical mucus, cervix position, and vaginal sensation) has been presented 62 , but such data are inaccessible for this study due to the European Union's General Data Protection Regulation and other dataprivacy concerns (sensitive fields such as appointments, ovulation and pregnancy tests, and BBT were not available in Clue's dataset).…”

“…Millions of women around the world routinely track their menstrual cycles and a variety of contextual factors and symptoms, accumulating high volumes of temporal, heterogeneous data via many different apps [55][56][57][58][59] . As exemplified by studies connecting the menstrual cycle to variations in women's mood, behavior, and vital signs 60 , selftracked data can provide insights into cycle characteristics 61 , ovulation timing, and the evolution of reproductive health for large populations 62 , as well as empower informed decisionmaking through increased self-awareness 63 .…”

Healthsheet: Development of a Transparency Artifact for Health Datasets

“…Multiple studies also show that health research on topics related to women's health does not get sufficient funding and attention [13,14,16,52].…”

Labeling Self-Tracked Menstrual Health Records With Hidden Semi-Markov Models

“…This information can be used to tackle scientific challenges and address unanswered questions about the human reproductive biology. For example, this data can be used to evaluate whether seasonal and geographical variations of fertility [8] is due to changes in ovulation or loss rates or to study, at large scale, the predictability of mental health variations throughout the menstrual cycle [9], [10]. Beyond the potential of these large retrospective datasets, apps and/or connected devices also provide a scalable way to prospectively collect longitudinal data of menstrual-health related body signs and symptoms for a large population size over a long period of time without requiring in-person visits to a clinic.…”