mcPHASES: A Dataset of Physiological, Hormonal, and Self-reported Events and Symptoms for Menstrual Health Tracking with Wearables
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".