The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual tracking mobile apps become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. By exploring a database of user-tracked observations from the Clue app by BioWink GmbH of over 378,000 users and 4.9 million natural cycles, we show that self-reported menstrual tracker data can reveal statistically significant relationships between per-person cycle length variability and self-reported qualitative symptoms. A concern for self-tracked data is that they reflect not only physiological behaviors, but also the engagement dynamics of app users. To mitigate such potential artifacts, we develop a procedure to exclude cycles lacking user engagement, thereby allowing us to better distinguish true menstrual patterns from tracking anomalies. We uncover that women located at different ends of the menstrual variability spectrum, based on the consistency of their cycle length statistics, exhibit statistically significant differences in their cycle characteristics and symptom tracking patterns. We also find that cycle and period length statistics are stationary over the app usage timeline across the variability spectrum. The symptoms that we identify as showing statistically significant association with timing data can be useful to clinicians and users for predicting cycle variability from symptoms, or as potential health indicators for conditions like endometriosis. Our findings showcase the potential of longitudinal, high-resolution self-tracked data to improve understanding of menstruation and women’s health as a whole.

月经周期是育龄妇女整体健康的关键指标。以前，主要通过调查结果来研究月经；然而，随着月经追踪移动应用程序越来越广泛地采用，随着时间的推移，它们提供了越来越大、内容丰富的月经健康体验和行为来源。通过探索 BioWink GmbH 的 Clue 应用程序的用户跟踪观察数据库，该数据库包含超过 378,000 名用户和 490 万个自然周期，我们表明，自我报告的月经跟踪器数据可以揭示人均周期长度变异性和自我周期长度之间的统计显着关系。报告定性症状。对自我跟踪数据的一个担忧是，它们不仅反映了生理行为，还反映了应用程序用户的参与动态。为了减轻这种潜在的伪影，我们开发了一个程序来排除缺乏用户参与的周期，从而使我们能够更好地区分真实的月经模式与跟踪异常。我们发现，基于月经周期长度统计数据的一致性，位于月经变异谱不同端的女性在周期特征和症状跟踪模式方面表现出统计上显着的差异。我们还发现，周期和周期长度统计数据在整个变化范围内的应用程序使用时间轴上是固定的。我们确定的与时间数据显示统计显着相关性的症状可帮助临床医生和用户根据症状预测周期变异性，或作为子宫内膜异位症等疾病的潜在健康指标。我们的研究结果展示了纵向、高分辨率自我跟踪数据的潜力，可以提高对月经和女性整体健康的了解。