Abstract

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.

中文翻译：

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下一个周期开始日期的预测模型，说明了月经自我跟踪的依从性

摘要

客观的

该研究试图基于移动健康自跟踪周期数据建立下一个月经周期开始日期的预测模型。由于应用程序用户可能会跳过跟踪，因此将月经的生理模式与跟踪行为分开对于预测模型的开发是必要的。

材料和方法

我们使用来自流行的月经追踪器（186000 名月经超过 200 万个追踪周期）的数据来学习预测模型，该模型（1）明确说明自我追踪依从性；(2) 随着给定周期的演变更新预测，允许对这些预测如何随时间变化的可解释性洞察；(3) 能够在结合人口水平信息的同时对个人的周期长度历史进行建模。

结果

与 5 个基线（均值、中值、卷积神经网络、循环神经网络和长短期记忆网络）相比，该模型产生了更好的预测，并且随着循环的发展始终优于它们。该模型还提供了对跳过跟踪概率的预测。

讨论

月经追踪器等移动健康应用程序提供了丰富的自我跟踪观察来源，但这些数据的可靠性值得怀疑，因为它们取决于用户对应用程序的依从性。通过采用机器学习方法对自跟踪周期长度进行建模，我们可以将真正的周期行为与用户依从性分开，从而对潜在的观察数据结构进行更明智的预测和洞察。

结论

将月经的生理模式与依从性分开可以对月经周期开始日期进行准确和信息丰富的预测，这对于移动跟踪应用程序来说是必要的。所提出的预测模型可以支持应用程序用户更加了解他们的自我跟踪行为并更好地了解他们的周期动态。