The mcPHASES (menstrual cycle Physiological, Hormonal, and Self-Reported Events and Symptoms) dataset provides a multimodal record of menstrual health that integrates physiological monitoring, hormone measurements, and self-reported experiences. Forty-two Canadian young adults who menstruate participated in a 3-month observation period, and 20 of them also completed a second 3-month observation period. During data collection, participants wore Fitbit Sense smartwatches to capture diverse physiological signals and Dexcom G6 continuous glucose monitors for metabolic data. Hormone levels were obtained using at-home Mira Plus urinalysis tests, and daily symptom and lifestyle information (e.g., pain, sleep, stress) was reported through surveys. In total, the dataset comprises 23 structured tables organized by signal category, allowing for analyses that link endocrine dynamics to wearable-derived measures and self-reported outcomes. This resource supports investigations into cycle variability, hormone-physiology interactions, and contextual influences on menstrual health, while also offering benchmark data for developing predictive algorithms and advancing menstrual health informatics.
Lin et al. (Tue,) studied this question.