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 usertracked observations from the Clue app by BioWink GmbH of over 378,000 users and 4.9 million natural cycles, we show that selfreported menstrual tracker data can reveal statistically significant relationships between per-person cycle length variability and selfreported 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.
Mild hyperkalemia can be effectively treated with a single 60-g oral dose of SPS as monotherapy, with minimal risk of hypokalemia. Moderate to severe hyperkalemic episodes warrant alternative therapy. The potassium-lowering effect is correlated to SPS dose and is independent of interindividual characteristics.
Objective
The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.
Materials and Methods
We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information.
Results
Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities.
Discussion
Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure.
Conclusions
Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.
The mechanisms of human birth seasonality have been debated for over 150 years. In particular, the question of whether sexual activity or fertility variations drive birth seasonality has remained open and difficult to test without large-scale data on sexual activity. Analyzing data from half-a-million users worldwide collected from the female health tracking app Clue in combination with birth records, we inferred that birth seasonality is primarily driven by seasonal fertility, yet increased sexual activity around holidays explains minor peaks in the birth curve. Our data came from locations in both the Northern Hemisphere (UK, US, and France) and the Southern Hemisphere (Brazil). We found that fertility peaks between the autumn equinox and winter solstice in the Northern Hemisphere locations and shortly following the winter solstice in the Southern Hemisphere locations.
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