mcPHASES: A Dataset of Physiological, Hormonal, and Self-reported Events and Symptoms for Menstrual Health Tracking with Wearables

Individuals who menstruate are frequently led to believe that there is a
standard menstrual cycle, typically characterized as 28 days in length with
predictable and uniform patterns. This framing often emphasizes cycle dates as
the only relevant metric, overlooking the broader physiological and emotional
fluctuations throughout the cycle driven by complex hormonal interactions.
Consequently, when individuals encounter menstrual experiences that do not
align with calendar-based metrics, they are often left without adequate
frameworks for understanding their menstrual health, which can result in
distress or delays in seeking care. Our work advocates for a new definition of
menstrual health that encompasses a wider range of physiological signals in
order to acknowledge its connection to overall wellbeing, establish realistic
expectations for menstruators, and build better health management systems.
However, historical stigmatization has led to a dearth of datasets suitable
for pursuing these aims.

mcPHASES ( **m** enstrual **c** ycle **P** hysiological, **H** ormonal, **a**
nd **S** elf-Reported **E** vents and **S** ymptoms) is a comprehensive
dataset consisting of multimodal physiological, hormonal, and self-reported
measures collected to support holistic menstrual health research. Data from 42
Canadian young adult menstruators was collected across two 3-month periods.
Participants wore Fitbit Sense smartwatches and Dexcom G6 continuous glucose
monitors to measure physiological signals, and they used Mira Plus Starter
Kits to track their hormone levels. Additionally, participants self-reported
daily experiences like cramps, sleep quality, and stress levels. The dataset
contains 23 structured tables organized by signal category so that researchers
can examine relationships between physiological signals and hormonal
fluctuations, analyze the impacts of lifestyle factors on the menstrual cycle,
and develop better algorithms for menstrual cycle prediction. More broadly,
mcPHASES supports research in women's health, digital health technologies, and
personalized care by providing unprecedented multimodal data for building a
more accurate understanding of menstrual health patterns.