Abstract
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.
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Data availability
The mcPHASES dataset and relevant documentation are accessible through PhysioNet (https://physionet.org/content/mcphases/1.0.0/).
Code availability
Example scripts demonstrating data import procedures and figure generation methodologies presented in this article are made available via the public repository at https://github.com/chai-toronto/mcphases.
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Acknowledgements
This work was supported in part by NSERC Discovery Grants RGPIN-2021-03457 and RGPIN-2021-04268, a Google PhD Fellowship, and an unrestricted research gift from Google.
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G.L., K.T., and A.M. conceived the studies. G.L. conducted the studies. G.L., J.L., and K.K. analyzed the data. All authors reviewed the manuscript.
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Lin, G., Li, J.Y., Kalani, K. et al. A longitudinal dataset of physiological, hormonal, metabolic, and self-reported menstrual health data. Sci Data (2026). https://doi.org/10.1038/s41597-026-06805-3
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DOI: https://doi.org/10.1038/s41597-026-06805-3
